| 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
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Ingår i: 8th International Symposium on Advanced Vehicle Control. - 9860059470
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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
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Ingår i: Journal of Safety Research. - : Elsevier BV. - 0022-4375. ; 62, s. 143-153
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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)
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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)
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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
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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)
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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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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)
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How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2
- 2015
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Ingår i: Transportation Research Part F: Traffic Psychology and Behaviour. - : Elsevier BV. - 1369-8478. ; 35, s. 152-169
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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
(författare)
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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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| 10. |
- Bärgman, Jonas, 1972, et al.
(författare)
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On data security and analysis platforms for analysis of naturalistic driving data
- 2011
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Ingår i: Proceedings of the 8th European Congress and Exhibition on Intelligent Transport Systems and Services, June 2011, Lyon.
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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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