| 1. |
- Lübbe, Nils, 1982, et al.
(författare)
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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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Ingår i: Conference proceedings International Research Council on the Biomechanics of Injury, IRCOBI. - 2235-3151. ; 2018-September, s. 17-52
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Konferensbidrag (refereegranskat)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.
(författare)
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Sensor Fusion for Vehicle Positioning in Intersection Active Safety Applications
- 2006
-
Ingår i: 8th International Symposium on Advanced Vehicle Control. - 9860059470
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Offset Eliminative Map Matching Algorithm for Intersection Active Safety
- 2006
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Ingår i: 2006 IEEE Intelligent Vehicles Symposium, IV 2006; Meguro-Ku, Tokyo; Japan; 13 June 2006 through 15 June 2006. - 9784901122863 ; :1689609, s. 82-88
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Konferensbidrag (refereegranskat)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.
(författare)
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Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders
- 2017
-
Ingår i: Journal of Safety Research. - : Elsevier BV. - 0022-4375. ; 62, s. 143-153
-
Tidskriftsartikel (refereegranskat)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.
(författare)
-
ANNEXT - Slutrapport
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)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.
(författare)
-
Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems
- 2017
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 102, s. 165-180
-
Tidskriftsartikel (refereegranskat)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.
(författare)
-
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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Ingår i: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 14:9, s. 1058-1067
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Tidskriftsartikel (refereegranskat)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.
(författare)
-
How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2
- 2015
-
Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 35, s. 152-169
-
Tidskriftsartikel (refereegranskat)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.
(författare)
-
Methodological challenges of scenario generation validation: A rear-end crash-causation model for virtual safety assessment
- 2024
-
Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - 1369-8478. ; 104, s. 374-410
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Tidskriftsartikel (refereegranskat)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
(författare)
-
Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)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.
(författare)
-
On data security and analysis platforms for analysis of naturalistic driving data
- 2011
-
Ingår i: Proceedings of the 8th European Congress and Exhibition on Intelligent Transport Systems and Services, June 2011, Lyon.
-
Konferensbidrag (refereegranskat)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
(författare)
-
On the analysis of naturalistic driving data
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)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.
(författare)
-
Quantifying drivers’ comfort-zone and dread-zone boundaries in left turn across path/opposite direction (LTAP/OD) scenarios
- 2015
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Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 35, s. 170-184
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Tidskriftsartikel (refereegranskat)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.
(författare)
-
The UDrive dataset and key analysis results
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)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.
(författare)
-
Using manual measurements on event recorder video and image processing algorithms to extract optical parameters.
- 2013
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Ingår i: Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. ; , s. 177-183
-
Konferensbidrag (refereegranskat)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.
(författare)
-
Driver Distraction and Inattention
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)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.
(författare)
-
Risk factors, crash causation and everyday driving
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)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.
(författare)
-
Chunking: a procedure to improve naturalistic data analysis
- 2013
-
Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 58, s. 309-317
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Tidskriftsartikel (refereegranskat)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.
(författare)
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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
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Rapport (övrigt vetenskapligt/konstnärligt)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.
(författare)
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Using Wireless Communication to Control Road-user Interactions in the Real World
- 2017
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Ingår i: Proceeding of the Road Safety and Simulation International Conference, RSS2017, 17-19 October 2017.
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Konferensbidrag (refereegranskat)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.
(författare)
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Naturalistic Driving Studies: An Overview and International Perspective
- 2021
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Ingår i: International Encyclopedia of Transportation: Volume 1-7. ; 7, s. 20-38
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Bokkapitel (övrigt vetenskapligt/konstnärligt)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.
(författare)
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The perfect mismatch: Analysis of rear-end crash causation mechanisms based on naturalistic crash data
- 2017
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Ingår i: 6th International Symposium on Naturalistic Driving Research. June 8-9. The Hague, The Netherlands.
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Konferensbidrag (refereegranskat)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.
(författare)
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Analysis of the role of inattention in road crashes based on naturalistic on-board safety monitoring data
- 2013
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Ingår i: PROCEEDINGS of the 3rd International Conference on Driver Distraction and Inattention.
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Konferensbidrag (refereegranskat)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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