A method for predicting crash configurations using counterfactual simulations and real-world data

Traffic safety technologies revolve around two principle ideas; crash avoidance and injury mitigation for inevitable crashes. The development of relevant vehicle injury mitigating technologies should consider the interaction of those two technologies, ensuring that the inevitable crashes can be adequately managed by the occupant and vulnerable road user (VRU) protection systems. A step towards that is the accurate description of the expected crashes remaining when crash-avoiding technologies are available in vehicles. With the overall objective of facilitating the assessment of future traffic safety, this study develops a method for predicting crash configurations when introducing crash-avoiding countermeasures. The predicted crash configurations are one important factor for prioritizing the evaluation and development of future occupant and VRU protection systems. By using real-world traffic accident data to form the baseline and performing counterfactual model-in-the-loop (MIL) pre-crash simulations, the change in traffic situations (vehicle crashes) provided by vehicles with crash-avoiding technologies can be predicted. The method is built on a novel crash configuration definition, which supports further analysis of the in-crash phase. By clustering and grouping the remaining crashes, a limited number of crash configurations can be identified, still representing and covering the real-world variation. The developed method was applied using Swedish national- and in-depth accident data related to urban intersections and highway driving, and a conceptual Autonomous Emergency Braking system (AEB) computational model. Based on national crash data analysis, the conflict situations Same-Direction rear-end frontal (SD-ref) representing 53 % of highway vehicle-to-vehicle (v2v) crashes, and Straight Crossing Path (SCP) with 21 % of urban v2v intersection crashes were selected for this study. Pre-crash baselines, for SD-ref (n = 1010) and SCP (n = 4814), were prepared based on in-depth accident data and variations of these. Pre-crash simulations identified the crashes not avoided by the conceptual AEB, and the clustering of these revealed 5 and 52 representative crash configurations for the highway SD-ref and urban intersection SCP conflict situations, respectively, to be used in future crashworthiness studies. The results demonstrated a feasible way of identifying, in a predictive way, relevant crash configurations for in-crash testing of injury prevention capabilities.

• Record URL:
  https://doi.org/10.1016/j.aap.2020.105932
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0001457520317528
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00014575
• Supplemental Notes:
  • © 2020 Alexandros Leledakis, et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Leledakis, Alexandros
  • 0000-0003-2090-9986
  • Lindman, Magdalena
  • Östh, Jonas
  • Wågström, Linus
  • Davidsson, Johan
  • Jakobsson, Lotta
• Publication Date: 2021-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 105932
• Serial:
  • Accident Analysis & Prevention
  • Volume: 150
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0001-4575
  • Serial URL: http://www.sciencedirect.com/science/journal/00014575

Subject/Index Terms

• TRT Terms: Countermeasures; Crash data; Crash risk forecasting; Mathematical prediction; Technological innovations; Traffic safety; Traffic simulation
• Geographic Terms: Sweden
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01762089
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 23 2020 3:09PM