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Original Articles

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

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Pages 34-43 | Received 29 Dec 2019, Accepted 20 May 2020, Published online: 27 Jun 2020
 

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 Matlab® 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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgements

The authors thank the Forschungsvereinigung Automobiltechnik (FAT) and Verkehrsunfallforschung an der TU Dresden GmbH (VUFO) for allowing the access of GIDAS-PCM database for this research project between IFSTTAR and Toyota Motor Europe NV/SA.

Additional information

Funding

This work was supported by Toyota Motor Europe NV/SA.

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