File(s) under embargo
Reason: Publisher requirement.
Pedestrian crash causation analysis and active safety system calibration
Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers’ own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle–pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.
Funding
OTH Research England: International Investment Initiative
National Key R&D Program of China (2021YFF0602700)
Belt and Road Cooperation Program under the 2023 Shanghai Action Plan for Science, Technology and Innovation (No. 23210750500)
History
School
- Design and Creative Arts
Department
- Design
Published in
Accident Analysis & PreventionVolume
195Publisher
ElsevierVersion
- VoR (Version of Record)
Rights holder
© ElsevierPublisher statement
This paper was accepted for publication in the journal Accident Analysis & Prevention and the definitive published version is available at https://doi.org/10.1016/j.aap.2023.107404Acceptance date
2023-11-21Publication date
2023-12-01Copyright date
2023ISSN
0001-4575eISSN
1879-2057Publisher version
Language
- en