1. |
- Lübbe, Nils, 1982, et al.
(author)
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Predicted road traffic fatalities in Germany: The potential and limitations of vehicle safety technologies from passive safety to highly automated driving
- 2018
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In: Conference proceedings International Research Council on the Biomechanics of Injury, IRCOBI. - 2235-3151. ; 2018-September, s. 17-52
-
Conference paper (peer-reviewed)abstract
- It has been proposed that automated vehicles will greatly increase road traffic safety. However, few attempts have been made to quantify this thesis and to compare the expected benefits with more traditional safety systems. This study was carried out in five steps, adding systems in each step (from passive safety, standard Advances Driver Assistance Systems (ADAS), advanced ADAS, safety-minded driving, to cautious driving) in order to capture the benefit of increasing levels of automation. Conservative and optimistic rules based on the expected performance of each safety system were developed and applied to the German In-Depth Accident Study database. Adding safety systems was effective in preventing fatalities, ranging from 12-13% (step 1, passive safety, no automation, conservative-optimistic estimate) to 45-63% (step 5, cautious driving). The highest automation level, in step 5, achieved a reduction of Vulnerable Road User (VRU) fatalities of 33-41%. Thus, passive and active safety systems contribute substantially to preventing fatalities and their further development and deployment should not be abandoned. Even the safest foreseeable, highly automated passenger cars are not likely to avoid all crashes and all road traffic fatalities. While increased market penetration across safety systems will make road traffic substantially safer, more efforts are needed to protect VRUs.
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2. |
- Ardeshiri, Tohid, 1980, et al.
(author)
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Sensor Fusion for Vehicle Positioning in Intersection Active Safety Applications
- 2006
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In: 8th International Symposium on Advanced Vehicle Control. - 9860059470
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Conference paper (other academic/artistic)abstract
- Global Positioning System (GPS) is being increasingly used in active safety applications. One field of active safety in which navigation information can be used is Intersection Active Safety Applications (IASA) which requires a precise and continuous estimate of vehicle position and heading direction to function properly. In this paper an implementation of Extended Kalman Filter for estimation of vehicle position and heading direction in an intersection active safety application is presented. The algorithm was tested on a complex urban trajectory of 2 km long and showed very encouraging results.
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3. |
- Ardeshiri, Tohid, 1980, et al.
(author)
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Offset Eliminative Map Matching Algorithm for Intersection Active Safety
- 2006
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In: 2006 IEEE Intelligent Vehicles Symposium, IV 2006; Meguro-Ku, Tokyo; Japan; 13 June 2006 through 15 June 2006. - 9784901122863 ; :1689609, s. 82-88
-
Conference paper (peer-reviewed)abstract
- Digital map information and Continuous Positioning Systems (CPS) are being increasingly used in active safety applications. However due to imprecision associated with digital road maps and inevitable inaccuracies in CPS positions, a map matching algorithm is essentialfor these applications.One field of active safety in which navigation information can be used is Intersection Active Safety Applications (IASA) which requires a precise position of vehicle relative to road network in an intersection. In this paper a novel map matching algorithm for an IASA is presented.To determine the vehicle trajectory relative to the road network, the proposed map matching algorithm calculates the general offset between digital road map and the CPS given vehicle trajectory by fusion of local offsets with a Kalman filter, incorporating their respective ncertainties. The created offset liminative map matching algorithm was tested on a omplex urban trajectory and showed very ncouraging results.
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4. |
- Bianchi Piccinini, Giulio, 1982, et al.
(author)
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Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders
- 2017
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In: Journal of Safety Research. - : Elsevier BV. - 0022-4375. ; 62, s. 143-153
-
Journal article (peer-reviewed)abstract
- Introduction: In the last 30 years, China has undergone a dramatic increase in vehicle ownership and a resulting escalation in the number of road crashes. Although crash figures are decreasing today, they remain high; it is therefore important to investigate crash causation mechanisms to further improve road safety in China. Method: To shed more light on the topic, naturalistic driving data was collected in Shanghai as part of the evaluation of a behavior-based safety service. The data collection included instrumenting 47 vehicles belonging to a commercial fleet with data acquisition systems. From the overall sample, 91 rear-end crash or near-crash (CNC) events, triggered by 24 drivers, were used in the analysis. The CNC were annotated by three researchers, through an expert assessment methodology based on videos and kinematic variables. Results: The results show that the main factor behind the rear-end CNC was the adoption of very small safety margins. In contrast to results from previous studies in the US, the following vehicles' drivers typically had their eyes on the road and reacted quickly in response to the evolving conflict in most events. When delayed reactions occurred, they were mainly due to driving-related visual scanning mismatches (e.g., mirror checks) rather than visual distraction. Finally, the study identified four main conflict scenarios that represent the typical development of rear-end conflicts in this data. Conclusions: The findings of this study have several practical applications, such as informing the specifications of in-vehicle safety measures and automated driving and providing input into the design of coaching/training procedures to improve the driving habits of drivers.
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5. |
- Bärgman, Jonas, 1972, et al.
(author)
-
ANNEXT - Slutrapport
- 2013
-
Reports (other academic/artistic)abstract
- Huvudmålet med ANNEXT har varit att utvärdera och illustrera styrkan i att använda redan insamlade externa naturalistiska kördata från DriveCams datainsamling (www.drivecam.com), för att öka förståelsen kring vad som orsakar trafikolyckor. Fram till nyligen har det inte funnits data som medger detaljerade studier av vad som sker i de viktiga sekunderna precis innan en olycka (eller nästan-olycka) i verklig trafik. Det vill säga, det har inte varit möjligt att studera t.ex. förares aktiviteter (t.ex. distraktioner såsom mobilanvändande) eller hur interaktionen mellan fordonen och dess förare faktiskt gått till under sekunderna före krock. Vi har använt oss av data from händelseinitierade inspelningsenheter i kommersiella fordon, där data inte samlats in som en del i något forskningsprojekt, utan som en del i en affärsverksamhet där DriveCam sålt en tjänst till företag som har flottor med fordon. Det vill säga, dessa händelseinspelare installeras inte i fordon för att specifikt forska på orsaker till olyckor, utan som en del i en tjänst för att förbättra trafiksäkerheten för just dessa företag. Ett tydligt mål är att få ner stilleståndskostnader för företagens fordon vid olyckor, men det har också effekten att antalet olyckor (och därmed skadade och dödade) minskar på vägarna där de körs. Fordonen körs som en del i den vanliga verksamheten hos företagen. När en olycka eller nästan-olycka identifieras med hjälp av kinematiska triggers (t.ex. tröskelvärden på acceleration), sparas data 8 sekunder föra och 4 sekunder efter triggern i inspelningsenheten. Data innefattar bl.a. GPS, video på föraren och framåt samt accelerometerdata. Denna skickas sedan tillbaks till DriveCam där en genomgång av alla händelser görs och varje event klassas för trafiksäkerhetsrelevans. I ANNEXT har vi fått tillgång till 100 påkörandehändelse (70 olyckor och 30 nästan-olyckor) samt 93 korsningshändelser (63 olyckor och 30 nästan-olyckor). Alla händelser har kodats av DriveCam-personal och vi på SAFER har sedan analyserat data. Följande beskriver processen genom projektet: Första steget var att ta fram en preliminär analysplan, baserad på projektansökan och ytterligare identifierade behov. Steg två var att kontakta DriveCam och få till ett kontrakt inom vilkets ramar projektet kunde genomföras. Scenarios identifierades och kriterier för hur händelser skulle väljas ut utvecklades och itererades mot DriveCam. Detaljerna är beskrivna i en publikation (Engström, Werneke, et al., 2013) från projektet (se separat avsnitt). Analysplanen har förfinats allteftersom i projektet. Som en del i analysplanen utvecklades en annoteringsbeskrivning, eller kodbok. Denna beskriver de variabler som vi identifierat som nödvändiga för analysen och som kräver manuell annotering av DriveCam-video. I ANNEXT utförde en dedikerad annoterare på DriveCam all primär videoannotering. För att förfina kodboken och verifiera annotering åkte SAFER-deltagare till DriveCam vid två tillfällen. När den huvudsakliga annotering var avslutad genomförde SAFER-partners 1) utveckling av metoder för extraktion av optiska parameterar baserat på annoterad video (Bärgman et al., 2013), och processade dessa data för att kvalitetssäkra inför analys, 2) iterativ vidareutveckling av kodningsschemat som kan kallas Kodbok för bidragandefaktorer (se resultat) och applicerade denna på tillgänglig data. Under våren och sommaren 2013 presenterades två vetenskapliga artiklar på konferenser. Ytterligare publikationer är under utveckling (se separat avsnitt)
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6. |
- Bärgman, Jonas, 1972, et al.
(author)
-
Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems
- 2017
-
In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 102, s. 165-180
-
Journal article (peer-reviewed)abstract
- As the development and deployment of in-vehicle intelligent safety systems (ISS) for crash avoidance and mitigation have rapidly increased in the last decades, the need to evaluate their prospective safety benefits before introduction has never been higher. Counterfactual simulations using relevant mathematical models (for vehicle dynamics, sensors, the environment, ISS algorithms, and models of driver behavior) have been identified as having high potential. However, although most of these models are relatively mature, models of driver behavior in the critical seconds before a crash are still relatively immature. There are also large conceptual differences between different driver models. The objective of this paper is, firstly, to demonstrate the importance of the choice of driver model when counterfactual simulations are used to evaluate two ISS: Forward collision warning (FCW), and autonomous emergency braking (AEB). Secondly, the paper demonstrates how counterfactual simulations can be used to perform sensitivity analyses on parameter settings, both for driver behavior and ISS algorithms. Finally, the paper evaluates the effect of the choice of glance distribution in the driver behavior model on the safety benefit estimation. The paper uses pre-crash kinematics and driver behavior from 34 rear-end crashes from the SHRP2 naturalistic driving study for the demonstrations. The results for FCW show a large difference in the percent of avoided crashes between conceptually different models of driver behavior, while differences were small for conceptually similar models. As expected, the choice of model of driver behavior did not affect AEB benefit much. Based on our results, researchers and others who aim to evaluate ISS with the driver in the loop through counterfactual simulations should be sure to make deliberate and well-grounded choices of driver models: the choice of model matters.
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7. |
- Bärgman, Jonas, 1972, et al.
(author)
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Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?
- 2020
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In: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 14:9, s. 1058-1067
-
Journal article (peer-reviewed)abstract
- This study demonstrates the need for a holistic safety-impact assessment of an advanced driver assistance system (ADAS) and its effect on eye-glance behaviour. It implements a substantial incremental development of the what-if (counterfactual) simulation methodology, applied to rear-end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off-road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off-road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off-road glances together with the safety effects provided by the ADAS.
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8. |
- Bärgman, Jonas, 1972, et al.
(author)
-
How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2
- 2015
-
In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 35, s. 152-169
-
Journal article (peer-reviewed)abstract
- As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers’ glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and near-crashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a ‘what-if’ (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver–vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.
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9. |
- Bärgman, Jonas, 1972, et al.
(author)
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Methodological challenges of scenario generation validation: A rear-end crash-causation model for virtual safety assessment
- 2024
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In: Transportation Research Part F: Traffic Psychology and Behaviour. - 1369-8478. ; 104, s. 374-410
-
Journal article (peer-reviewed)abstract
- Safety assessment of crash and conflict avoidance systems is important for both the automotive industry and other stakeholders. One type of system that needs such an assessment is a driver monitoring system (DMS) with some intervention (e.g., warning or nudging) when the driver looks off-road for too long. Although using computer simulation to assess safety systems is becoming increasingly common, it is not yet commonly used for systems that affect driver behavior, such as DMSs. Models that generate virtual crashes, taking crash-causation mechanisms into account, are needed to assess these systems. However, few such models exist, and those that do have not been thoroughly validated on real-world data. This study aims to address this research gap by validating a rear-end crash-causation model which is based on four crash-causation mechanisms related to driver behavior: a) off-road glances, b) too-short headway, c) not braking with the maximum deceleration possible, and d) sleepiness (not reacting before the crash). The pre-crash kinematics were obtained from the German GIDAS in-depth crash database. Challenges with the validation process were identified and addressed. Most notably, a process was developed to transform the generated crashes to mimic the crash severity distribution in GIDAS. This step was necessary because GIDAS does not include property-damage-only (PDO) crashes, while the generated crashes cover the full range of severities (including low-severity crashes, of which many are PDOs). Our results indicate that the proposed model is a reasonably good crash generator. We further demonstrated that the model is a valid method for assessing DMSs in virtual simulations; it shows the safety impact of shorter ‘longest’ off-road glances. As expected, ‘cutting away’ long off-road glances substantially reduces the number of crashes that occur and reduces the average delta-v. This work highlights the need to both a) thoroughly understand the process of generating virtual scenarios and b) have the tools to validate them. While more work to develop validation processes for scenario generation is needed across all levels of crash severity, the transform and other validation tools that were developed bring us one step closer to accurate validation methodologies.
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10. |
- Bärgman, Jonas, 1972
(author)
-
Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research
- 2016
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Doctoral thesis (other academic/artistic)abstract
- In the last several years, the focus of traffic safety research—especially when performed in association with the automotive industry—has shifted from preventing injury during a crash to avoiding the crash altogether or mitigating its effects. Pre-crash safety measures include intelligent safety systems (e.g., different levels of automated driving), infrastructure design, behavior-based safety, and policy-making. Understanding driver behavior is crucial in the development and evaluation of such measures. Naturalistic driving data (NDD) can facilitate this understanding by providing information about crash causation and contribute to the evaluation of pre-crash safety measures and the effects of driver behavior on safety. However, NDD’s complexity calls for new and better methods to fully exploit its advantages. This thesis, together with the five included papers, addresses several gaps in current scientific knowledge by presenting novel methods for analyzing NDD that address multiple aspects of the development process for pre-crash safety measures. The chunking method (Paper I) helps to identify and overcome common biases in analysis of everyday-driving time-series data, while the expert-assessment-based crash-causation analysis method (Paper II, supported by Paper III) is a novel approach to studying crash causation through the analysis of NDD with video. Product and prototype development can be improved by utilizing counterfactual simulations, for which the choice of driver behavior model is shown to be crucial (Paper IV)—an awareness that was previously lacking. Being able to compare the effects of drivers’ specific behaviors (e.g., driver-vehicle interactions or in-vehicle secondary tasks) on safety could both speed up development of safety measures and improve vehicle designs and design guidelines. Methods to perform such comparisons through the combination of counterfactual glance behavior and pre-crash kinematics have been missing (they are provided in Paper V). This thesis further improves the evaluation of pre-crash safety measures by providing more robust analyses of everyday driving data (Paper I) and by demonstrating the importance of good mathematical models of driver behavior in virtual evaluation (Paper IV). In summary, these new methods fill important research gaps and have the potential to improve the design of pre-crash safety measures through the use of NDD. Using NDD can augment our understanding of driver behavior and crash causation, important aspects of improving traffic safety and fulfilling Sweden’s Vision Zero.
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11. |
- Bärgman, Jonas, 1972, et al.
(author)
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On data security and analysis platforms for analysis of naturalistic driving data
- 2011
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In: Proceedings of the 8th European Congress and Exhibition on Intelligent Transport Systems and Services, June 2011, Lyon.
-
Conference paper (peer-reviewed)abstract
- Studies involving naturalistic driving data, of which Naturalistic Field Operational Tests (N-FOTs) are a subset, are becoming increasingly important for understanding the factors influencing accident causation as well as for the development and evaluation of active safety systems. The methodology project FESTA developed a handbook on how to plan and implement FOTs. This handbook has been extensively used as a guideline in the euroFOT project. However, “the devil is in the details” when implementing e.g. the platforms for data security and analysis in projects which deal with analysis of large amounts of sensitive naturalistic driving data, such as euroFOT. That is, although a guideline such as FESTA is used, how the details are implemented is what makes the implementation a success or not. This paper is a case description of the implementation of the data security and analysis platform used for euroFOT (and other naturalistic data projects) at the SAFER Vehicle and Traffic Safety Centre. The paper covers aspects ranging from physical access to analysis rooms and corresponding digital access, via the platforms for pre-processing of data, to the platforms for information extraction for hypothesis analysis and statistics. The considerations in the design and choice of these platforms include subjects (drivers) privacy concerns, industry commercial concerns, as well as the needs and requirements from the analysis.
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12. |
- Bärgman, Jonas, 1972
(author)
-
On the analysis of naturalistic driving data
- 2015
-
Licentiate thesis (other academic/artistic)abstract
- In the last several years, the focus of traffic safety research has shifted from injury prevention during a crash to measures taken before a crash, in order to mitigate its effects or avoid it completely. Measures include advanced driver assistance systems, safety aspects of autonomous driving and infrastructure design, behavior-based safety (driver training), and policy-making. All of these pre-crash measures require an understanding of driver behavior. As a result of this need, naturalistic driving data (NDD) has emerged as a crucial data source with high ecological validity. NDD enable not only the real-world assessment of driver behavior, but also that of road infrastructure and pre-crash safety measures. However, NDD’s great potential is hindered by its complexity. Consequently, new methods to analyze NDD are greatly needed. This thesis presents a novel framework for traffic safety research using NDD and discusses the framework’s benefits and drawbacks. Furthermore it presents novel methods for analyzing NDD. The first paper presents a robust method to reduce bias in the analysis of time-series NDD. The second paper ports the DREAM method, used in traditional on-scene crash investigations, to vehicle-to-pedestrian incidents in NDD with video data. The third paper analyzes NDD with a novel method based on expert judgment. This method, inspired by DREAM, is currently applied to commercially collected and event-based, real-world crashes with driver and forward video. Finally, the fourth paper presents a new, pragmatic method to extracting range, range rate and optical parameters (e.g. looming) from the forward video in commercially collected lead-vehicle NDD. In summary, the methods developed and presented in this thesis use quantitative and qualitative analyses of time-series and video data from naturalistic driving to augment our understanding of driver behavior. Pre-crash safety measures will be further advanced not only by these insights, but also by future applications of the methods developed in this thesis.
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13. |
- Bärgman, Jonas, 1972, et al.
(author)
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Quantifying drivers’ comfort-zone and dread-zone boundaries in left turn across path/opposite direction (LTAP/OD) scenarios
- 2015
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In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 35, s. 170-184
-
Journal article (peer-reviewed)abstract
- The aim of this study is to quantify drivers' comfort- and dread-zone boundaries in left-t urn-across-path/opposite-direction (LTAP/OD) scenarios. These scenarios account for a large fraction of traffic fatalities world-wide. The comfort zone is a dynamic spatiotemporal envelope surrounding the vehicle, within which drivers feel comfortable and safe. The dread zone, a novel concept, describes a zone with a smaller safety margin that drivers will not voluntarily enter, but can push themselves into when conditions provide additional motivation (e.g., when hurried). Quantifying comfort- and dread-zone boundaries in the context of turning left before or after an oncoming vehicle has the potential to inform and improve both the design and driver acceptance of advanced driver assistance systems (ADAS) and autonomous vehicles. Using a within-subject design, a test-track experiment was conducted with drivers turning an instrumented vehicle left across the path of an oncoming vehicle. The oncoming vehicle was a self-propelled full-sized computer-controlled balloon vehicle going straight at a constant speed (50 km/h). The driver assumed full control of the instrumented vehicle approximately 20 m before the intersection and had to make the decision to turn left before or after the oncoming balloon vehicle. There were two experimental conditions, comfortable driving and hurried driving. Measures for each turn included postencroachment time (PET), lateral acceleration, and self-reports of comfort and risk. Drivers consistently accepted shorter time gaps and higher lateral accelerations when hurried. We interpret these findings to suggest that drivers invoke two dynamic, contextuallydefined safety margins. The first is the comfort-zone boundary, a limit which drivers do not voluntarily cross without extra motives. The second is the dread-zone boundary, a more distant limit which drivers do not voluntarily cross even with extra motives. Grouping the responses (high/low) to the driver behavior questionnaire (DBQ) improved the ability to predict the dread-zone boundary PET given the comfort-zone boundary PET.
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14. |
- Bärgman, Jonas, 1972, et al.
(author)
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The UDrive dataset and key analysis results
- 2017
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Reports (other academic/artistic)abstract
- UDrive is a large European naturalistic driving study, sponsored by the European Commission (FP7).Nineteen partners across Europe have come together and, along with stakeholders, defined researchquestions, developed data acquisition, collected and managed data, and finally, performed a first analysis onthe UDrive dataset with respect to driver/rider behaviour related to traffic safety and the environment (ecodriving).This document presents key results of the UDrive analysis performed in UDrive Sub-project 4: Data analysis.It also describes the UDrive dataset and, in brief, how we got here.
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15. |
- Bärgman, Jonas, 1972, et al.
(author)
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Using manual measurements on event recorder video and image processing algorithms to extract optical parameters.
- 2013
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In: Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. ; , s. 177-183
-
Conference paper (peer-reviewed)abstract
- Vehicle kinematics and optical parameters such as optical angle, optical expansion rate, and tau are thought to underlie drivers’ ability to avoid and handle critical traffic situations. Analyses of these parameters in naturalistic driving data with video, such as commercial event recordings of near-crashes and crashes, can provide insight into driver behavior in critical traffic situations. This paper describes a pair of methods, one for the range to a lead vehicle and one for its optical angle, that are derived from image processing mathematics and that provide driver behavior researchers with a relatively simple way to extract optical parameters from video-based naturalistic data when automatic image processing is not possible. The methods begin with manual measurements of the size of other road users on a video on a screen. To develop the methods, 20 participants manually measured the width of a lead vehicle on 14 images where the lead vehicle was placed at different distances from the camera. An on-market DriveCam Event Recorder was used to capture these images. A linear model that corrects distortion and modeling optics was developed to transform the on-screen measurements distance (range) to and optical angle of the vehicle. The width of the confidence interval for predicted range is less than 0.1m when the actual distance is less than 10m and the lead-vehicle width estimate is correct. The methods enable driver behavior researchers to easily and accurately estimate useful kinematic and optical parameters from videos (e.g., of crashes and near-crashes) in event-based naturalistic driving data.
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16. |
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17. |
- Carsten, Oliver, et al.
(author)
-
Driver Distraction and Inattention
- 2017
-
Reports (other academic/artistic)abstract
- In UDRIVE, the major focus of the work on driver inattention and distraction has been focused on obtaining a better understanding of whether and how drivers manage their secondary task activities — when they choose to engage, what tasks they select, whether they adjust their activity to different situations and whether they are willing to surrender secondary task activities as the primary task of driving becomes more demanding. In other words, the focus is on self-regulation, on how drivers manage their secondary task activity in the context of the dynamics of the traffic and road situation. That management includes the determination not to engage in such tasks in the first place or only to engage in some particular activities. NDS are particularly suited to such an investigation, since experimental studies in driving simulators and even on test tracks tend to suffer from an instruction effect, in that participants are typically instructed to carry out an activity at a given moment. Thus such experimental studies provide insight into how driver attention, driver information processing and driving performance are affected by secondary tasks, but are less useful when research is focused on driver management of task activity
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18. |
- Dotzauer, Mandy, et al.
(author)
-
Risk factors, crash causation and everyday driving
- 2017
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Reports (other academic/artistic)abstract
- The UDRIVE Naturalistic Driving Study (NDS) is the first large scale European project to observe driving behaviour directly in the field. Its goal was to identify risky driving behaviour, understand what drivers commonly do (everyday driving), why and when driver’s attention is diverted from the road (distracted driving), focus on power two wheelers (PTW), pedestrians and cyclists as they are road users that are exceptionally exposed to crashes (vulnerable road users; VRU) and learn about the properties of sustainable driving behaviour (eco-driving). One of the advantages of this project is the unique opportunity to observe critical events while they occur, and with the help of the records, go back in time and investigate what may have caused them. Drivers from France (FR), Germany (GE), Poland (PL), Spain (SP), the Netherlands (NL), and the UK volunteered to participate in this study. Mechanics equipped their vehicles with cameras and sensors and thus created a fleet of 200 vehicles. This included 120 cars (FR, GE, PL, NL, and UK), 40 scooters (SP), and 40 trucks (NL). The data was collected in a box, referred to as data acquisition system (DAS), which was installed in the trunk of the vehicles. It recorded videos of seven to eight cameras, CAN, GPS and acceleration data. The data was collected between a time period of 12 to 21 months, accumulating 87 871 hours of data. This deliverable reports the results of normal and risky driving behaviour while the findings of the other research topics will be reported in respective deliverables
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19. |
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20. |
- Dozza, Marco, 1978, et al.
(author)
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Chunking: a procedure to improve naturalistic data analysis
- 2013
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 58, s. 309-317
-
Journal article (peer-reviewed)abstract
- Every year, traffic accidents are responsible for more than 1,000,000 fatalities worldwide. Understanding the causes of traffic accidents and increasing safety on the road are priority issues for both legislators and the automotive industry. Recently, in Europe, the US and Japan, significant public funding has been allocated for performing large-scale naturalistic driving studies to better understand accident causation and the impact of safety systems on traffic safety. The data provided by these naturalistic driving studies has never been available before in this quantity and comprehensiveness and it promises to support a wide variety of data analyses. The volume and variety of the data also pose substantial challenges that demand new data reduction and analysis techniques. This paper presents a general procedure for the analysis of naturalistic driving data called chunking that can support many of these analyses by increasing their robustness and sensitivity. Chunking divides data into equivalent, elementary chunks of data to facilitate a robust and consistent calculation of parameters. This procedure was applied, as an example, to naturalistic driving data from the SeMiFOT study in Sweden and compared with alternative procedures from past studies in order to show its advantages and rationale in a specific example. Our results show how to apply the chunking procedure and how chunking can help avoid bias from data segments with heterogeneous durations (typically obtained from SQL queries). Finally, this paper shows how chunking can increase the robustness of parameter calculation, statistical sensitivity, and create a solid basis for further data analyses. (C) 2012 Elsevier Ltd. All rights reserved.
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21. |
- Dozza, Marco, 1978, et al.
(author)
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Data and data collection methodologies for the development of computational models of AV/VRU interaction and their integration into virtual simulation testing of AV : Deliverable 2.3 in the EC ITN project SHAPE-IT
- 2023
-
Reports (other academic/artistic)abstract
- Several computational models explaining interactions between AVs and the VRUs pedestrians and cyclists have been developed in SHAPE-IT. For instance, there are now models predicting whether a pedestrian or cyclist will cross or yield at an intersection. Further, interaction models were developed and/or verified using different types of data collected in experiments or 'in the wild'. These data were combined and fed to different algorithms that leveraged machine learning to describe road-user behaviour. This deliverable address both pedestrian and cyclist interactions with AVs, utilising both naturalistic data and data collected in controlled environments. The former comprised site-based and in-vehicle data collections. The latter included data from several virtual environments (e.g., driving simulators, riding simulators, and pedestrian simulation environments). The main conclusion of this deliverable is that the potential for computational models of AV/VRU interaction to promote AV safety while reducing the cost and time of AV development is high. However, more data is needed before human behaviour (especially in critical scenarios) is captured precisely and comprehensively enough that their integration into virtual simulations delivers explainable, accurate, and reliable results. This deliverable is rather a stepping stone to be used to define intermediate goals for the eventual development of computational models of AV/VRU interaction and their integration into virtual simulations for safety benefit assessment. Within SHAPE-IT, ESR3, ESR13, and ESR14 developed everyday-driving models that may be used directly in traffic simulations, while the focus of ESR15 has been on methods related to and applications of counterfactual simulations.
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22. |
- Dozza, Marco, 1978, et al.
(author)
-
Using Wireless Communication to Control Road-user Interactions in the Real World
- 2017
-
In: Proceeding of the Road Safety and Simulation International Conference, RSS2017, 17-19 October 2017.
-
Conference paper (peer-reviewed)abstract
- Autonomous vehicles promise to conquer the road network, revolutionize transportation, and improve traffic safety in the near future. However, before they can take over, they will be sharing the infrastructure with manned vehicles, and will be expected to behave like those vehicles do in traffic. In other words, the first autonomous vehicles will have to remain within human comfort boundaries so as not to surprise, confuse, or scare any road users.For safety reasons, comfort boundaries in critical situations can only be measured in driving simulators or on test tracks. In less critical situations, field and naturalistic studies provide the most realistic estimations of comfort boundaries. In fact, naturalistic data is collected in the real world by drivers following their daily routines; thus they capture realistic driver behavior. However, when we want to assess comfort boundaries in specific scenarios — such as intersections — with complex interactions, even large databases may offer limited data with great variability.This study presents and verifies a new experimental methodology to diminish variability in naturalistic and field data collection without compromising ecological validity. This methodology controls the environment in real time, depending on the behavior of study participants to produce specific driving situations in the real world. Recreating similar driving conditions over and over increases data consistency, enabling data to be averaged across participants and repetitions. This paper estimates comfort-zone boundaries at intersections from naturalistic data. Potential applications for this methodology include the development and evaluation of advanced driving assistance systems, as well as the design of test procedures for active safety and cooperative systems.
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23. |
- Ehsani, Johnathon P., et al.
(author)
-
Naturalistic Driving Studies: An Overview and International Perspective
- 2021
-
In: International Encyclopedia of Transportation: Volume 1-7. ; 7, s. 20-38
-
Book chapter (other academic/artistic)abstract
- Naturalistic driving studies (NDS) are a method in transportation research that is increasingly used to bridge the gap between epidemiological research (e.g., using population crash databases) and individual level or experimental research (e.g., self-reported surveys or driving simulators). This article begins with defining NDS and providing a brief overview of NDS methods, including the strengths, limits and the unique ethical issues involved in conducting NDS. Following this, five case studies from Australia, Canada, China, the European Union, and the United States are presented, along with a synthesis of the lessons they have learned. The article concludes with a discussion of what the future of NDS may look like.
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24. |
- Eiríksdóttir, Hrafnhildur Hekla, 1988, et al.
(author)
-
The perfect mismatch: Analysis of rear-end crash causation mechanisms based on naturalistic crash data
- 2017
-
In: 6th International Symposium on Naturalistic Driving Research. June 8-9. The Hague, The Netherlands.
-
Conference paper (peer-reviewed)abstract
- The present analysis aimed to replicate the analysis of Victor et al. (2015), who investigated how off-road glances cause rear-end crashes and near-crashes. The key finding in Victor et al. (2015) was that many rear-end crashes occur due to a “perfect mismatch” between the timing of the last glance before the crash/near crash and the change in kinematics resulting from the lead vehicle braking. The present analysis, based on a set of event-triggered naturalistic crashes and near crashes obtained from commercial on-board safety monitoring devices, largely corroborated the previous findings and, additionally, developed a new metric that offered some further insight into the perfect mismatch mechanism.
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25. |
- Engström, Johan A Skifs, 1973, et al.
(author)
-
Analysis of the role of inattention in road crashes based on naturalistic on-board safety monitoring data
- 2013
-
In: PROCEEDINGS of the 3rd International Conference on Driver Distraction and Inattention.
-
Conference paper (peer-reviewed)abstract
- The general objective of the present analysis was to investigate the role of driver inattention in rear-end crashes and crossing path intersection crashes. To this end, a set of 133 naturalistic crashes (70 rear-end and 63 intersection crashes), obtained by means of the DriveCam on-board safety monitoring (OBSM) system, were analyzed based on a novel methodology for assigning and aggregating crash-contributing factors. The analysis focused on rear-end crashes where the OBSM-instrumented vehicle was striking a lead vehicle and crossing-path intersection crashes where the driver of the instrumented vehicle intended to proceed straight through the intersection. It was found that driver inattention, in particular driver distraction involving a diversion of gaze from the forward roadway, was the dominating factor contributing to the rear-end crashes.Although driver inattention also contributed to the intersection crashes, the patterns of contributing factors for this crash type were quite different compared to the rear-end crashes. In particular, in the intersection crashes, visual occlusion and insufficient selection of safety margins were identified as key contributing factors. Cognitively distracting activities that did not involve a diversion of gaze from the forward roadway, such as cell phone conversation, did not contribute frequently to avoidance failures for any of the crash types. The present results show that the role of driver inattention as acrash-contributing factor depends strongly on the type of crash. They also support previous findings from naturalistic driving studies that visual diversion from theforward roadway is the key mechanism by which inattention leads to rear-end crashes.
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26. |
- Engström, Johan A Skifs, 1973, et al.
(author)
-
Great expectations: A predictive processing account of automobile driving
- 2018
-
In: Theoretical Issues in Ergonomics Science. - 1464-536X .- 1463-922X. ; 19:2, s. 156-194
-
Journal article (peer-reviewed)abstract
- Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimisation. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimising the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.
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27. |
- Figalova, Nikol, et al.
(author)
-
Methodological Framework for Modelling and Empirical Approaches (Deliverable D1.1 in the H2020 MSCA ITN project SHAPE-IT)
- 2021
-
Reports (other academic/artistic)abstract
- The progress in technology development over the past decades, both with respect to software and hardware, offers the vision of automated vehicles as means of achieving zero fatalities in traffic. However, the promises of this new technology – an increase in road safety, traffic efficiency, and user comfort – can only be realized if this technology is smoothly introduced into the existing traffic system with all its complexities, constraints, and requirements. SHAPE- IT will contribute to this major undertaking by addressing research questions relevant for the development and introduction of automated vehicles in urban traffic scenarios. Previous research has pointed out several research areas that need more attention for a successful implementation and deployment of human-centred vehicle automation in urban environments. In SHAPE-IT, for example, a better understanding of human behaviour and the underlying psychological mechanisms will lead to improved models of human behaviour that can help to predict the effects of automated systems on human behaviour already during system development. Such models can also be integrated into the algorithms of automated vehicles, enabling them to better understand the human interaction partners’ behaviours. Further, the development of vehicle automation is much about technology (software and hardware), but the users will be humans and they will interact with humans both inside and outside of the vehicle. To be successful in the development of automated vehicles functionalities, research must be performed on a variety of aspects. Actually, a highly interdisciplinary team of researchers, bringing together expertise and background from various scientific fields related to traffic safety, human factors, human-machine interaction design and evaluation, automation, computational modelling, and artificial intelligence, is likely needed to consider the human-technology aspects of vehicle automation. Accordingly, SHAPE-IT has recruited fifteen PhD candidates (Early Stage Researchers – ESRs), that work together to facilitate this integration of automated vehicles into complex urban traffic by performing research to support the development of transparent, cooperative, accepted, trustworthy, and safe automated vehicles. With their (and their supervisors’) different scientific background, the candidates bring different theoretical concepts and methodological approaches to the project. This interdisciplinarity of the project team offers the unique possibility for each PhD candidate to address research questions from a broad perspective – including theories and methodological approaches of other interrelated disciplines. This is the main reason why SHAPE-IT has been funded by the European Commission’s Marie Skłodowska-Curie Innovative Training Network (ITN) program that is aimed to train early state researchers in multidisciplinary aspects of research including transferable skills. With the unique scope of SHAPE-IT, including the human-vehicle perspective, considering different road-users (inside and outside of the vehicle), addressing for example trust, transparency, and safety, and including a wide range of methodological approaches, the project members can substantially contribute to the development and deployment of safe and appreciated vehicle automation in the cities of the future. To achieve the goal of interdisciplinary research, it is necessary to provide the individual PhD candidate with a starting point, especially on the different and diverse methodological approaches of the different disciplines. The empirical, user-centred approach for the development and evaluation of innovative automated vehicle concepts is central to SHAPE- IT. This deliverable (D1.1 “Methodological Framework for Modelling and Empirical Approaches”) provides this starting point. That is, this document provides a broad overview of approaches and methodologies used and developed by the SHAPE-IT ESRs during their research. The SHAPE-IT PhD candidates, as well as other researchers and developers outside of SHAPE-IT, can use this document when searching for appropriate methodological approaches, or simply get a brief overview of research methodologies often employed in automated vehicle research. The first chapter of the deliverable shortly describes the major methodological approaches to collect data relevant for investigating road user behaviour. Each subchapter describes one approach, ranging from naturalistic driving studies to controlled experiments in driving simulators, with the goal to provide the unfamiliar reader with a broad overview of the approach, including its scope, the type of data collected, and its limitations. Each subchapter ends with recommendations for further reading – literature that provide much more detail and examples. The second chapter explains four different highly relevant tools for data collection, such as interviews, questionnaires, physiological measures, and as other current tools (the Wizard of Oz paradigm and Augmented and Virtual Reality). As in the first chapter this chapter provides the reader with information about advantages and disadvantages of the different tools and with proposed further readings. The third chapter deals with computational models of human/agent interaction and presents in four subchapters different modelling approaches, ranging from models based on psychological mechanisms, rule-based and artificial intelligence models to simulation models of traffic interaction. The fourth chapter is devoted to Requirements Engineering and the challenge of communicating knowledge (e.g., human factors) to developers of automated vehicles. When forming the SHAPE-IT proposal it was identified that there is a lack of communication of human factors knowledge about the highly technical development of automated vehicles. This is why it is highly important that the SHAPE-IT ESRs get training in requirement engineering. Regardless of the ESRs working in academia or industry after their studies it is important to learn how to communicate and disseminate the findings to engineers. The deliverable ends with the chapter “Method Champions”. Here the expertise and association of the different PhD candidates with the different topics are made explicit to facilitate and encourage networking between PhDs with special expertise and those seeking support, especially with regards to methodological questions.
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28. |
- Flannagan, Carol, 1962, et al.
(author)
-
Replacement of distractions with other distractions: A propensity-based approach to estimating realistic crash odds ratios for driver engagement in secondary tasks
- 2019
-
In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 63, s. 186-192
-
Journal article (peer-reviewed)abstract
- As Automated Vehicles (AVs) enter the fleet at lower levels of automated (SAE, 2018), the need for human drivers to remain engaged in the driving task will continue. Thus, understanding driver distraction and estimating the reduction in risk associated with removing distractions is important as AV technology develops. While previous research (e.g., Dingus et al., 2016) has estimated large odds ratios (i.e., 3–4) for using cell-phones while driving, countermeasures directed at reducing cell-phone use have not realized large crash reductions. One reason may be that drivers may replace cell-phone use with other risky activities and that odds ratios (ORs) have often compared cell-phone use to ideal driving rather than a realistic reference. Using data from the second Strategic Highway Research Program (SHRP2), we developed two cell-phone propensity models, one with age and one without, to develop weights for events without cell phone use. Using these weights, we estimated the probability of engagement in a variety of tasks in place of cell-phone use. We also estimated weighted odds ratios for cell-phone use (all uses) and cell-phone talking only. Weighted ORs are lower than unweighted ORs and much lower than ORs compared to ideal driving. This is consistent with the idea that in practice, even if cell-phone bans are effective at reducing cell-phone use, they may not greatly reduce risk because drivers may replace cell-phone use with other distracting activities in the same situations in which they normally use cell phones while driving. We also discuss the influence of young drivers on our results. Younger drivers in the dataset are more likely to use cell phones and thus are influential in the propensity model results.
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29. |
- Flannagan, Carol, 1962, et al.
(author)
-
What Are Drivers Doing When They Aren't on the Cell Phone?
- 2018
-
Conference paper (peer-reviewed)abstract
- Cell-phone bans have been motivated by previous research estimating large odds ratios (i.e., 3-4) for using cell-phones while driving. However, large crash reductions have not been realized. One reason may be that drivers may replace cell-phone use with other risky activities and that ORs have often compared cell-phone use to ideal driving rather than a realistic reference. Using SHRP2 data, we developed two cell-phone propensity models, one with age and one without, to develop weights for events without cell phone use. Using these weights, we estimated the probability of engagement in a variety of tasks in place of cell-phone use. We also estimated weighted ORs for cell-phone use (all uses) and cell-phone talking only. Weighted ORs are lower than unweighted ORs and much lower than ORs compared to ideal driving. This is consistent with the idea that in practice, even if cell-phone bans are effective at reducing cell-phone use, they may not greatly reduce risk because drivers may replace cell-phone use with other distracting activities in the same situations in which they normally use cell phones while driving. We also discuss the influence of young drivers on our results. Younger drivers in the dataset are more likely to use cell phones and thus are influential in the propensity model results.
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30. |
- Habibovic, Azra, 1982, et al.
(author)
-
DREAMi – Final Report
- 2013
-
Reports (other academic/artistic)abstract
- Traditionellt har data från trafikolyckor varit den enda tillgängliga informationskällan som använts för att förstå hur och varför säkerhetskritiska trafiksituationer uppstår. I synnerhet har djupstudier av olyckor varit viktiga eftersom de möjliggör en detaljerad inblick i händelseförloppet. Olycksdatabaser med djupstudier innehåller dock ett begränsat antal fall och ger i princip ingen tidsserieinformation om händelserna som leder till olyckan.För att kompensera för dessa brister har naturalistiska körstudier utvecklats. I sådana studier körs fordon under verkliga trafikförhållanden och är instrumenterade med kameror och andra sensorer för att samla information om förare, fordon och omgivningen. Med denna metod är det oftast möjligt att observera ett stort antal säkerhetskritiska situationer, så kallade incidenter.DREAMi har undersökt om data från naturalistiska körstudier kan användas för att förstå orsaker som leder till incidenter. Data var insamlad i ett annat projekt i Japan och inkluderade bl.a. videoinspelningar av föraren och omgivningen. För att identifiera och koda orsakerna använde projektet Driving Reliability and Error Analysis Method (DREAM). Eftersom DREAM utvecklats för analys av olycksorsaker baserat på information från djupstudier var det nödvändigt att göra modifieringar i metoden för att anpassa den till information tillgänglig i incidenter på video från naturalistiska körstudier. DREAMi har därmed skapat en metod som är unik och finns ännu inte inom trafiksäkerhetsforskning. Den modifierade metoden har med stor framgång applicerats på 90 fotgängarincidenter insamlade i Japan För att undersöka hur den modifierade metoden fungerar i praktiken, har projektet applicerat den på 90 fotgängarincidenter insamlade i Japan.Detta projekt var också ett första forskningssamarbete inom området trafiksäkerhet mellan SAFER - Vehicle and Traffic Safety Center at Chalmers och Japan Automotive Research Institute (JARI). Samarbetet har stärkt trafiksäkerhetsforskningen i båda länderna, samt gjort SAFER mer attraktiv på den internationella arenan. I synnerhet har DREAMi använts för att motivera SAFERs deltagande i internationella projekt som ANNEXT och US SHRP2.
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31. |
- Habibovic, Azra, 1982, et al.
(author)
-
Driver behavior in car-to-pedestrian incidents: An application of the Driving Reliability and Error Analysis Method (DREAM)
- 2013
-
In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 50, s. 554-565
-
Journal article (peer-reviewed)abstract
- To develop relevant road safety countermeasures, it is necessary to first obtain an in-depth understanding of how and why safety-critical situations such as incidents, near-crashes, and crashes occur. Video-recordings from naturalistic driving studies provide detailed information on events and circumstances prior to such situations that is difficult to obtain from traditional crash investigations, at least when it comes to the observable driver behavior. This study analyzed causation in 90 video-recordings of car-to-pedestrian incidents captured by onboard cameras in a naturalistic driving study in Japan. The Driving Reliability and Error Analysis Method (DREAM) was modified and used to identify contributing factors and causation patterns in these incidents. Two main causation patterns were found. In intersections, drivers failed to recognize the presence of the conflict pedestrian due to visual obstructions and/or because their attention was allocated towards something other than the conflict pedestrian. In incidents away from intersections, this pattern reoccurred along with another pattern showing that pedestrians often behaved in unexpected ways. These patterns indicate that an interactive advanced driver assistance system (ADAS) able to redirect the driver's attention could have averted many of the intersection incidents, while autonomous systems may be needed away from intersections. Cooperative ADAS may be needed to address issues raised by visual obstructions.
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32. |
- Imberg, Henrik, 1991, et al.
(author)
-
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
- 2022
-
Journal article (other academic/artistic)abstract
- Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets and measurement-constrained experiments. However, traditional subsampling methods often suffer from the lack of information available at the design stage. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements were observed compared to traditional sampling methods.
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33. |
- Jansen, Reinier J., et al.
(author)
-
Interactions between car drivers and cyclists: A naturalistic perspective on gaze behavior in right-turn maneuvers
- 2017
-
In: 6th International Symposium on Naturalistic Driving Research, June 8-9. The Hague, The Netherlands.
-
Conference paper (peer-reviewed)abstract
- Failure to perform appropriate visual checks at intersections may have contributed to the 2069 cyclist fatalities in the European Union in 2015. This study investigates whether and when car drivers perform visual checks for potentially encroaching cyclists during right-turn maneuvers at urban intersections. UDrive naturalistic data from instrumented vehicles in France, the Netherlands, Poland, and Great Britain were used. The dataset consisted of 852 right-turn maneuvers (UK: left-turn, throughout this document) across 56 drivers. In the six seconds prior to the maneuver, drivers checked their blind spot in 4% of the cases. This frequency increased to 8% when the maneuver was included. The prevalence of cyclist facilities was highest in the Netherlands, which is also the country where blind spot checks were performed most often. When not checking the blind spot, drivers mostly looked toward the road they were turning into. Country predicted neither the timing of sideway glances, nor whether there was any (right) sideways glance. Our findings may inform driver training to increase awareness of cyclists at urban intersections, as well as the design of other traffic safety measures.
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34. |
- Jansen, Reinier J, et al.
(author)
-
Interactions with vulnerable road users
- 2017
-
Reports (other academic/artistic)abstract
- Within UDRIVE there has been a specific focus on pedestrians, cyclists and Powered Two Wheelers (PTWs). These groups of road users are particularly vulnerable in traffic because they lack the protective shell that helps preventing serious injury once involved in a collision. In addition, these transport modes have several features that make them more prone to getting involved in a crash, e.g. related to reduced conspicuity and for the two-wheelers the difficulty to remain in balance, either or not in combination with high speeds. This type of factors make that pedestrians, cyclists and PTWs have a high risk of getting fatally or seriously injured in traffic.Within UDRIVE, a large amount of ‘naturalistic’ data was collected to get more in-depth insight in the interactions of these groups with passenger cars and trucks. The aim was to identify and understand the everyday behavioural patterns in these interactions as well as the circumstances of conflicts or safety critical events in these interactions. The current Deliverable reported on the analyses and results of a number of specific interaction types.
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35. |
- Jokhio, Sarang, et al.
(author)
-
Analysis of Time-to-Lane-Change-Initiation Using Realistic Driving Data
- 2024
-
In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 25:5, s. 4620-4633
-
Journal article (peer-reviewed)abstract
- Lane changing is a complex, yet extremely common driving manoeuvre. Studying lane changes can provide insight into how long drivers wait after activating their turn signal before changing lanes -a time that we call time-to-lane-change-initiation (TTLCI). TTLCI can offer valuable insights into driver behaviour prior to changing lanes. However, a better understanding of TTLCI, particularly in real-world settings, is lacking. To address this knowledge gap, we investigated TTLCI using driving data collected on public roads in Gothenburg, Sweden. We used the Kaplan-Meier (K-M) method and the mixed-effect Cox Proportional Hazard (CPH) model (statistical techniques from survival analysis) to comprehensively analyze TTLCI and identify factors that significantly influence it. The results of the K-M method indicate that most lane changes were initiated within two seconds of activating the turn signal. The mixed-effect CPH model showed that the speed of the lane-changing vehicle, the type and direction of the lane change, the presence of lead and lag vehicles, and the lag gap were all significant factors. These findings provide new insights into pre-lane-change behaviour and pave the way for future studies, in part by improving current lane change models. Moreover, the findings have implications for future regulations concerning turn-signal usage by human drivers. Additionally, our results can contribute to the development of algorithms for autonomous vehicles by improving their ability to detect imminent lane changes by surrounding vehicles.
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36. |
- Jokhio, Sarang, et al.
(author)
-
Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data
- 2024
-
In: IET Intelligent Transport Systems. - 1751-9578 .- 1751-956X. ; 18:2, s. 393-408
-
Journal article (peer-reviewed)abstract
- Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals before starting a lane change. In this study, realistic driving data are analyzed to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. Turn signal usage is examined and factors that influence it are identified by employing Bayesian hierarchical modelling. The study found that drivers used their turn signal before changing lanes in 60% of cases, after starting the lane change in 33% of cases, and did not use it at all in 7% of cases. The Bayesian hierarchical modelling results indicate that various factors, such as the speed and direction of lane changes and the presence of surrounding vehicles, influence the usage of turn signals. The study concludes that understanding the factors affecting turn signal usage is crucial for improving traffic safety in current and future mixed traffic with autonomous vehicles. The study discusses the implications of findings concerning increasing turn signal compliance through general policy-making, improving existing in-vehicle technologies and including turn signal usage in Pay-As-You-Drive insurances.
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37. |
- Jokhio, Sarang, et al.
(author)
-
Influence of surrounding traffic on lane change dynamics: Insights from a video-based laboratory study
- 2024
-
In: Transportation Research Part F: Traffic Psychology and Behaviour. - 1369-8478. ; 105, s. 87-98
-
Journal article (peer-reviewed)abstract
- The inherent complexity associated with lane-changing manoeuvres can significantly disrupt traffic flow and increase the risk of collisions. While the lane-changing vehicle influences the surrounding traffic, it is also simultaneously influenced by it. This study aims to examine how the presence and behaviour of surrounding vehicles affect the lane changer's behaviour. A study was conducted in the laboratory using video stimuli of various simulated lane-changing scenarios to achieve the research aim. The study used a two-block design. In the first block, the impact of the lag vehicle was evaluated by varying its gap and behaviour (acceleration, deceleration or maintaining speed) relative to the lane-changing vehicle. In the second block, both the lead and lag gaps were manipulated with respect to the lane-changing vehicle. Data from the participants (n=29) were collected on dependent variables, including lane change decisions (gap acceptance or rejection), perceived cooperation, and reaction time. The analysis was conducted using an Aligned Rank Transform ANOVA. The findings of this laboratory-based study suggest that the lag vehicle, specifically its behaviour, has more influence on lane change decisions than the lead vehicle in the target lane. Additionally, when a lag vehicle decelerates to create a gap in response to a lane changer's request, its actions are perceived as more cooperative, compared to when it accelerates or maintains speed. Furthermore, decisions to change lanes are made faster when the lag vehicle shows a deceleration behaviour. The results of this laboratory-based study provide valuable insights for improving current lane-changing models. We also discuss the implications of findings for improving algorithms governing autonomous vehicle interactions in mixed traffic. Finally, we discuss the benefits and limitations of laboratory-based approaches in studying causal relationships among different factors, as well as the generalizability of our findings.
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38. |
- Kovaceva, Jordanka, 1980, et al.
(author)
-
A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres
- 2020
-
In: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 75, s. 87-105
-
Journal article (peer-reviewed)abstract
- The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions. This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming. The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT. The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems.
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39. |
- Kovaceva, Jordanka, 1980, et al.
(author)
-
Car drivers overtaking cyclists: A European perspective using naturalistic driving data
- 2017
-
In: The 6th International Naturalistic Driving Research Symposium, the Hague, the Netherlands, 8-9 June 2017.
-
Conference paper (peer-reviewed)abstract
- In Europe, the number of road crashes is decreasing, while the number of crashes involving cyclists are not decreasing at the same rate as car crashes. Crashes which occur while the vehicle is overtaking a cyclist often result in severe injuries or fatalities. Understanding the behaviour of car drivers overtaking cyclists, can facilitate increased road safety through improved guidelines and policies, as well as in-vehicle technologies.This study investigates how car drivers overtake cyclists on rural roads in four European countries by analysing the UDrive naturalistic driving data. One objective is to understand if, in different countries, there is a difference in the lateral distance when the car is passing the cyclist. Other objective is to investigate if the time-to-collision (TTC), at the start of the overtaking, affects the lateral distance when the car is passing the cyclist.Minor differences between countries were found with respect to lateral distance. Greater lateral distance while passing cyclists was observed with increase in time-to-collision at the start of the overtaking. Furthermore, vehicle speed, distance between the lane edge and the cyclists, and the presence of leading vehicle significantly affected the driver comfort zone. The driver comfort zone during overtaking manoeuvres from naturalistic driving data could provide information for legislators and policy makers in Europe, as well as support safety system design in the automotive industry.
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40. |
- Kovaceva, Jordanka, 1980, et al.
(author)
-
Drivers overtaking cyclists in the real-world: evidence from a naturalistic driving study
- 2019
-
In: Safety Science. - : Elsevier BV. - 0925-7535 .- 1879-1042. ; 119, s. 199-206
-
Journal article (peer-reviewed)abstract
- The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than bicycles. During these manoeuvres, drivers try to minimize risk in the complex traffic environment by staying in their comfort zone while overtaking the cyclist. This study quantified drivers’ comfort zone boundaries (CZBs) and investigated the combination of factors that affect the CZBs while drivers overtake cyclists in a naturalistic setting. The results show that the higher the car speed the larger the CZBs while approaching and passing, but the presence of an oncoming vehicle significantly decreased the CZB during passing. The drivers’ age, gender, and Arnett Inventory of Sensation Seeking score were not found to have a statistically significant impact on the CZBs. The findings of this study provide implications for the design of road safety intervention programs that increase safety for all road users and the development of advanced driver-assistance systems that could interact with cyclists.
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41. |
- Kovaceva, Jordanka, 1980, et al.
(author)
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On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer
- 2022
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 165
-
Journal article (peer-reviewed)abstract
- The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car–to–cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists’ fatalities by 53–96% and serious injuries by 43–94%, depending on the driver response model. The shorter the driver’s reaction time and the larger the driver’s deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW’s great potential to avoid crashes and reduce injuries in car–to–cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits.
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42. |
- Lee, Ja Young, et al.
(author)
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How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving
- 2018
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 110, s. 29-37
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Journal article (peer-reviewed)abstract
- Drivers engage in non-driving tasks while driving, such as interactions entertainment systems. Studies have dentified glance patterns related to such interactions, and manual radio tuning has been used as a reference task to set an upper bound on the acceptable demand of interactions. Consequently, some view the risk associated with radio tuning as defining the upper limit of glance measures associated with visual-manual in-vehicle activities. However, we have little knowledge about the actual degree of crash risk that radio tuning poses and, by extension, the risk of tasks that have similar glance patterns as the radio tuning task. In the current study, we use counterfactual simulation to take the glance patterns for manual radio tuning tasks from an on-road experiment and apply these patterns to lead-vehicle events observed in naturalistic driving studies. We then quantify how often the glance patterns from radio tuning are associated with rear-end crashes, compared to driving only situations. We used the pre-crash kinematics from 34 crash events from the SHRP2 naturalistic driving study to investigate the effect of radio tuning in crash-imminent situations, and we also investigated the effect of radio tuning on 2,475 routine braking events from the Safety Pilot project. The counterfactual simulation showed that off-road glances transform some near-crashes that could have been avoided into crashes, and glance patterns observed in on-road radio tuning experiment produced 2.85–5.00 times more crashes than baseline driving.
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43. |
- Ljung Aust, Mikael, 1973, et al.
(author)
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Manual for DREAM version 3.2
- 2012
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Reports (other academic/artistic)abstract
- The Driving Reliability and Error Analysis Method (DREAM) is based on the Cognitive Reliability and Error Analysis Method (CREAM; Hollnagel, 1998). CREAM was developed to analyse accidents within process control domains such as nuclear power plants and train operation, and DREAM is an adaptation of CREAM to suit the road traffic domain. The purpose of DREAM is to make it possible to systematically classify and store accident and incident causation information. This means that DREAM, like all other methods for accident/incident analysis, is not a provider but an organiser of explanations. For any of the contributing factor categories available in DREAM to be used, it must be supported by relevant empirical information. DREAM in itself cannot tell us why accidents happen (if it could, we would need neither on-scene investigations nor interviews).DREAM includes three main components: an accident model, a classification scheme and a detailed procedure description which step by step goes through what needs to be done in order to perform a DREAM analysis on an investigated accident/incident. Below, the accident model will be given more detailed descriptions. After this follows a description of the classification scheme, and then comes the analysis process, including example cases and recommendations for how to do the categorisation in certain typical scenarios.
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44. |
- Markkula, Gustav M, 1978, et al.
(author)
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A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies
- 2016
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In: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 95, s. 209-226
-
Journal article (peer-reviewed)abstract
- Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario's kinematics, quantified in terms of visual looming of the lead vehicle on the driver's retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5 s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined "hazard onset", but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario.
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45. |
- Merat, Natasha, et al.
(author)
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An Overview of Interfaces for Automated Vehicles (inside/outside) (Deliverable D2.1 in the H2020 MSCA ITN project SHAPE-IT)
- 2021
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Reports (other academic/artistic)abstract
- This Deliverable starts with a short overview of the design principles and guidelines developed for current Human Machine Interfaces (HMIs), which are predominantly developed for manually driven vehicles, or those with a number of Advanced Driver Assistance Systems (ADAS), at SAE Levels 0 and 1 (SAE, 2018). It then provides an overview of how the addition of more capable systems, and the move to higher levels of vehicle automation, is changing the role the human inside an Automated Vehicle (AV), and the ways in which future automated vehicles at higher levels of automation (SAE level 4 and 5) must communicate with other road users, in the absence of an “in charge” human driver. It is argued that such changes in the role of the driver, and more transfer of control to the AV and its different functionalities, means that there will be more emphasis on the roles and responsibilities of HMIs for future AVs. In parallel, the multifaceted nature of these HMI, presented from different locations, both in and outside the vehicles, using a variety of modalities, and engaging drivers in a two-way interaction, means that a new set of design guidelines are required, to ensure that the humans interacting with AVs (inside and outside the vehicle) are not distracted and overloaded, that they remain situation aware and understand the capabilities and limitations of the system, having the right mental model of system capabilities and their responsibilities, as responsible road users, at all times Following a summary of suggested frameworks and design principles which highlight the significant change needed for new AV HMIs, an overview of results from studies investigating human interaction with internal (or iHMIs), and external (or eHMIs), is provided, with examples of new and innovative methods of communication between humans and their vehicles. The Deliverable then provides a summary of the innovative approaches that will be tackled by the ESRs of the project, which focus on factors such as use of AI and AR for future design of more intuitive and transparent HMI, studying how HMI can support the long term interaction of humans with AVs, and the use of neuroergonomic methods for developing safer HMIs. The Deliverable concludes by summarising how each ESR’s project contributes to the development of HMIs for future AVs.
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46. |
- Muhammad, Amna Pir, 1990, et al.
(author)
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Continuous Experimentation and Human Factors An Exploratory Study
- 2024
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In: Lecture Notes in Computer Science. - : Springer. - 0302-9743 .- 1611-3349. ; 14483, s. 511-526
-
Conference paper (peer-reviewed)abstract
- In today's rapidly evolving technological landscape, the success of tools and systems relies heavily on their ability to meet the needs and expectations of users. User-centered design approaches, with a focus on human factors, have gained increasing attention as they prioritize the human element in the development process. With the increasing complexity of software-based systems, companies are adopting agile development methodologies and emphasizing continuous software experimentation. However, there is limited knowledge on how to effectively execute continuous experimentation with respect to human factors within this context. This research paper presents an exploratory qualitative study for integrating human factors in continuous experimentation, aiming to uncover distinctive characteristics of human factors and continuous software experiments, practical challenges for integrating human factors in continuous software experiments, and best practices associated with the management of continuous human factors experimentation.
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47. |
- Muhammad, Amna Pir, 1990, et al.
(author)
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Human factors in developing automated vehicles: A requirements engineering perspective
- 2023
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In: Journal of Systems and Software. - 0164-1212. ; 205
-
Journal article (peer-reviewed)abstract
- Automated Vehicle (AV) technology has evolved significantly both in complexity and impact and is expected to ultimately change urban transportation. Due to this evolution, the development of AVs challenges the current state of automotive engineering practice, as automotive companies increasingly include agile ways of working in their plan-driven systems engineering-or even transition completely to scaled-agile approaches. However, it is unclear how knowledge about human factors (HF) and technological knowledge related to the development of AVs can be brought together in a way that effectively supports today's rapid release cycles and agile development approaches. Based on semi-structured interviews with ten experts from industry and two experts from academia, this qualitative, exploratory case study investigates the relationship between HF and AV development. The study reveals relevant properties of agile system development and HF, as well as the implications of these properties for integrating agile work, HF, and requirements engineering. According to the findings, which were evaluated in a workshop with experts from academia and industry, a culture that values HF knowledge in engineering is key. These results promise to improve the integration of HF knowledge into agile development as well as to facilitate HF research impact and time to market.& COPY; 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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48. |
- Nilsson, Emma, 1982, et al.
(author)
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Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures
- 2022
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In: Frontiers in Neuroergonomics. - : Frontiers Media SA. - 2673-6195. ; 3
-
Journal article (peer-reviewed)abstract
- The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic—that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.
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49. |
- Olleja, Pierluigi, 1995, et al.
(author)
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A reference-driver model for overtaking a cyclist
- 2023
-
Conference paper (other academic/artistic)abstract
- An expected benefit of the introduction of Autonomous Vehicles (AVs) on public roads is improved traffic safety. One approach to assessing and verifying the improvements provided by AVs that has recently gained attention uses computational models of reference drivers as a safety benchmark. These models typically aim to describe a competent and careful driver’s driving strategy and capability to respond to external stimuli (hence the expression “reference model”). Models for AV assessment can be divided into crash avoidance and conflict avoidance models. Crash avoidance is here any evasive maneuver aimed at avoiding an imminent crash. Differently, conflict avoidance is here any action (or, rather, driving strategy) aimed at minimizing the risk of ending up in a conflict. The model developed in this study focuses on driver conflict avoidance behaviors, and the simulations compare the performance of the AV system under assessment with that of the reference-driver model: the system should perform at least as well as the driver model. If an AV performs better than the model, the rationale is that it would also respond better than a careful and competent driver. Currently, reference-driver models are primarily being developed for highway driving. Scenarios such as cyclist overtaking, in which a driver overtakes and passes a cyclist, have not yet gained much attention in this field of research. This study aims to create and demonstrate a novel reference-driver model to assess AVs, where the model represents a careful and competent human driver when approaching and overtaking a cyclist. This model, which can be included in virtual simulations, is intended to keep the virtual driver within the comfort-zone boundaries of a careful and competent driver. These boundaries characterize the limits of cyclists’ and drivers’ perceived safety (in terms of both time and space) during overtaking. An existing computational driver model of the overtaking maneuver was used as the starting point for this work. This model describes the safety metrics that characterize the maneuver. These metrics include the lateral distance to the cyclist (which quantifies the objective and perceived safety mainly from the cyclist’s perspective), and time-to-collision to an oncoming vehicle (which relates more to the objective and perceived safety from the driver’s perspective). In this study, building on existing models, we present a model that can complete the overtaking, even as it is constrained to maintain a lateral distance that would be considered safe by a competent and careful driver, and while adhering to the constraints of the cyclist’s perceived safety. The performance of the model is demonstrated by applying it to a set of normal driving data from a naturalistic driving study, as well as synthetically generated critical bicycleovertaking situations. The expected results include the documentation of a reference model of a careful and competent driver that aims to minimize the risk of conflicts (and, consequently, crashes) during cyclist overtaking. The model can be used as a safety target when assessing AVs in that scenario. The model’s performance will be demonstrated by its application to naturalistic driving/riding data of cyclist overtaking and to a set of synthetically generated critical events. Additionally, a sensitivity analysis will investigate the influence of model parameters and input variables (such as the speeds of the overtaking vehicle and the bicycle) on the model’s performance. We aim to make the model open-source as a controller in the esmini virtual simulation software, to complement the existing highway-driving car-to-car models that currently exist in esmini. In conclusion, this study will provide an open-source computational reference-driver model representing a careful and competent driver overtaking a cyclist, for use as a safety target for AVs in the cyclist-overtaking scenario.
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50. |
- Olleja, Pierluigi, 1995, et al.
(author)
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Can non-crash naturalistic driving data be an alternative to crash data for use in virtual assessment of the safety performance of automated emergency braking systems?
- 2022
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In: Journal of Safety Research. - : Elsevier BV. - 0022-4375. ; 83, s. 139-151
-
Journal article (peer-reviewed)abstract
- Introduction: Developers of in-vehicle safety systems need to have data allowing them to identify traffic safety issues and to estimate the benefit of the systems in the region where it is to be used, before they are deployed on-road. Developers typically want in-depth crash data. However, such data are often not available. There is a need to identify and validate complementary data sources that can complement in-depth crash data, such as Naturalistic Driving Data (NDD). However, few crashes are found in such data. This paper investigates how rear-end crashes that are artificially generated from two different sources of non-crash NDD (highD and SHRP2) compare to rear-end in-depth crash data (GIDAS). Method: Crash characteristics and the performance of two conceptual automated emergency braking (AEB) systems were obtained through virtual simulations – simulating the time-series crash data from each data source. Results: Results show substantial differences in the estimated impact speeds between the artificially generated crashes based on both sources of NDD, and the in-depth crash data; both with and without AEB systems. Scenario types also differed substantially, where the NDD have many fewer scenarios where the following-vehicle is not following the lead vehicle, but instead catches-up at high speed. However, crashes based on NDD near-crashes show similar pre-crash criticality (time-to-collision) to in-depth crash data. Conclusions: If crashes based on near-crashes are to be used in the design and assessment of preventive safety systems, it has to be done with great care, and crashes created purely from small amounts of everyday driving NDD are not of much use in such assessment. Practical applications: Researchers and developers of in-vehicle safety systems can use the results from this study: (a) when deciding which data to use for virtual safety assessment of such systems, and (b) to understand the limitations of NDD.
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