Affordable Access

deepdyve-link
Publisher Website

Car-to-cyclist forward collision warning effectiveness evaluation: a parametric analysis on reconstructed real accident cases

Authors
  • CHAR, François
  • Serre, Thierry
  • Compigne, Sabine
  • PUENTE GUILLEN, Pablo
Publication Date
Jan 01, 2020
Identifiers
DOI: 10.1080/13588265.2020.1773740
OAI: oai:HAL:hal-02883693v1
Source
HAL-Descartes
Keywords
Language
English
License
Unknown
External links

Abstract

The objective of the study is to quantify the benefits of an earlier brake activation by the drivers potentially achieved by a Forward Collision Warning (FCW) system in simulated car-to-cyclist accident scenarios. A parametric analysis is performed by varying the detection sensor Field Of View (FOV), the FCW trigger time and the driver's reaction lag time to the FCW. Almost two thousand and three hundred car-to-cyclist accidents are clustered in the following five main scenarios: crossing nearside (33%), crossing farside (22%), longitudinal (5%), turning left (12%) and turning right (22%). The remaining is clustered in Others group (6%). For all accident cases, original accident kinematics are processed through MatlabVR scripts from which FCW FOV, FCW trigger time and driver's reaction can be modified. The Matlab scripts return the new accident kinematics which can result in the accident being avoided or mitigated. This study shows that a 70 FOV, a FCW trigger time of 2.6 s before the impact and a 0.6 s driver's reaction to the FCW has a positive result in 82% of the accident cases with 78% being avoided and 4% mitigated. Concerning the parameters, the FOV has a greater influence on the avoidance rates compared to FCW trigger time and driver's reaction. Our study also reveals that FCW system has a higher benefit in the crossing farside scenario and a lower benefit in the turning right scenario. This paper highlights generic characteristics of FCW systems to optimise safety benefit for the different accident scenarios.

Report this publication

Statistics

Seen <100 times