| 1. |
- Åkerberg Boda, Christian-Nils, 1989, et al.
(författare)
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Modelling how drivers respond to a bicyclist crossing their path at an intersection: How do test track and driving simulator compare?
- 2018
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 111, s. 238-250
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Tidskriftsartikel (refereegranskat)abstract
- Bicyclist fatalities are a great concern in the European Union. Most of them are due to crashes between motorized vehicles and bicyclists at unsignalised intersections. Different countermeasures are currently being developed and implemented in order to save lives. One type of countermeasure, active safety systems, requires a deep understanding of driver behaviour to be effective without being annoying. The current study provides new knowledge about driver behaviour which can inform assessment programmes for active safety systems such as Euro NCAP. This study investigated how drivers responded to bicyclists crossing their path at an intersection. The influences of car speed and cyclist speed on the driver response process were assessed for three different crossing configurations. The same experimental protocol was tested in a fixed-base driving simulator and on a test track. A virtual model of the test track was used in the driving simulator to keep the protocol as consistent as possible across testing environments. Results show that neither car speed nor bicycle speed directly influenced the response process. The crossing configuration did not directly influence the braking response process either, but it did influence the strategy chosen by the drivers to approach the intersection. The point in time when the bicycle became visible (which depended on the car speed, the bicycle speed, and the crossing configuration) and the crossing configuration alone had the largest effects on the driver response process. Dissimilarities between test-track and driving-simulator studies were found; however, there were also interesting similarities, especially in relation to the driver braking behaviour. Drivers followed the same strategy to initiate braking, independent of the test environment. On the other hand, the test environment affected participants' strategies for releasing the gas pedal and regulating deceleration. Finally, a mathematical model, based on both experiments, is proposed to characterize driver braking behaviour in response to bicyclists crossing at intersections. This model has direct implications on what variables an in-vehicle safety system should consider and how tests in evaluation programs should be designed.
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| 2. |
- Batkovic, Ivo, 1992, et al.
(författare)
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A Computationally Efficient Model for Pedestrian Motion Prediction
- 2018
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Konferensbidrag (refereegranskat)abstract
- We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving. The model is based on a road map structure, and assumes a rational pedestrian behavior. We compare our model with the state-of-the art and discuss its accuracy, and limitations, both in simulations and in comparison to real data.
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| 3. |
- Kovaceva, Jordanka, 1980, et al.
(författare)
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A new framework for modelling road-user interaction and evaluating active safety systems
- 2018
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Ingår i: 7th International Cycling Safety Conference 2018.
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Konferensbidrag (refereegranskat)abstract
- In the road transport system, cyclists account for a significant share of fatalities and serious injuries. Advance driver assistance systems (ADAS) that address potential crashes of passenger cars with cyclists are being developed and introduced to the market. Safety benefit evaluation of these ADAS is important to verify if current ADAS actually reduce real-world crashes and to determine the extent to which novel algorithms may improve current ADAS, before entering the market. ADAS safety benefit evaluation requires an agreed framework with defined concepts for target scenario specification, models of road‑user behaviour and road‑user interaction, and performance metrics.
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| 4. |
- 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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| 5. |
- Sander, Ulrich, 1971, et al.
(författare)
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Market penetration of intersection AEB: Characterizing avoided and residual straight crossing path accidents
- 2018
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 115:June 2018, s. 178-188
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Tidskriftsartikel (refereegranskat)abstract
- Car occupants account for one third of all junction fatalities in the European Union. Driver warning can reduce intersection accidents by up to 50 percent; adding Autonomous Emergency Braking (AEB) delivers a reduction of up to 70 percent. However, these findings are based on an assumed 100 percent equipment rate, which may take decades to achieve. Our study investigates the relationship between intersection AEB market penetration rates and avoidance of accidents and injuries in order to guide implementation strategies. Additionally, residual accident characteristics (impact configurations and severity) are analyzed to provide a basis for future in-crash protection requirements. We determined which accidents would have been avoided through the use of an Intersection AEB system with different sensor field-of-view (180°) by means of re-simulating the pre-crash phase of 792 straight crossing path (SCP) car-to-car accidents recorded in the German In-Depth Accident Study (GIDAS) and the associated Pre-Crash Matrix (PCM). Intersection AEB was activated when neither of the conflict opponents could avoid the crash through reasonable braking or steering reactions. For not-avoided accidents, we used the Kudlich-Slibar rigid body impulse model to calculate the change of velocity during the impact as a measure of impact severity and the principal direction of force. Accident avoidance over market penetration is not linear but exponential, with higher gains at low penetration rates and lower gains at higher rates. A wide field-of-view sensor (180°) substantially increased accident avoidance and injury mitigation rates compared to a 120° field-of-view sensor. For a 180° field-of-view sensor at 100 percent market penetration, about 80 percent of the accidents and 90 percent of the MAIS2+F injuries could be avoided. For the remaining accidents, AEB intervention rarely affected side of impact. The median change of velocity (delta-V) of the remaining crashes reduces only marginally at low penetration rates but this reduction increases with higher penetration rates. With 100 percent market penetration, one quarter of the vehicles still involved in straight crossing path accidents will sustain a delta-V higher than 17 km/h. Intersection AEB is very effective. Enabling a fast initial implementation of systems with wide field-of-view sensor(s) and ensuring a high market penetration over the longer term is essential to achieve high crash avoidance and injury mitigation rates over time. The standards for in-crash protection must be high to mitigate injury in the unavoidable, residual accidents.
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| 6. |
- Sander, Ulrich, 1971, et al.
(författare)
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The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB
- 2018
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Ingår i: Accident Analysis and Prevention. - : Elsevier BV. - 0001-4575. ; 113, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Intersection accidents are frequent and harmful. The accident types ‘straight crossing path’ (SCP), ‘left turn across path – oncoming direction’ (LTAP/OD), and ‘left-turn across path – lateral direction’ (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The usage of different sets of clustering variables resulted in substantially different numbers of clusters. The stability of the resulting clusters increased with prioritization of categorical over continuous variables. For each different set of cluster variables, a strong in-cluster variance of avoided versus non-avoided accidents for the specified Intersection AEB was present. The medoids did not predict the most common Intersection AEB behavior in each cluster. Despite thorough analysis using various cluster methods and variable sets, it was impossible to reduce the diversity of intersection accidents into a set of test scenarios without compromising the ability to predict real-life performance of Intersection AEB. Although this does not imply that other methods cannot succeed, it was observed that small changes in the definition of a scenario resulted in a different avoidance outcome. Therefore, we suggest using limited physical testing to validate more extensive virtual simulations to evaluate vehicle safety.
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