An Emergency Driving Intervention System Designed for Driver Disability Scenarios Based on Emergency Risk Field

Yuning Wang, Shuocheng Yang, Jinhao Li, Shaobing Xu () and Jianqiang Wang ()
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Yuning Wang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Shuocheng Yang: Xingjian College, Tsinghua University, Beijing 100084, China
Jinhao Li: Xingjian College, Tsinghua University, Beijing 100084, China
Shaobing Xu: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Jianqiang Wang: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

IJERPH, 2023, vol. 20, issue 3, 1-20

Abstract: Driver disability has become an increasing factor leading to traffic accidents, especially for commercial vehicle drivers who endure high mental and physical pressure because of long periods of work. Once driver disability occurs, e.g., heart disease or heat stroke, the loss of driving control may lead to serious traffic incidents and public damage. This paper proposes a novel driving intervention system for autonomous danger avoidance under driver disability conditions, including a quantitative risk assessment module named the Emergency Safety Field (ESF) and a motion-planning module. The ESF considers three factors affecting hedging behavior: road boundaries, obstacles, and target position. In the field-based framework, each factor is modeled as an individual risk source generating repulsive or attractive force fields. Individual risk distributions are regionally weighted and merged into one unified emergency safety field denoting the level of danger to the ego vehicle. With risk evaluation, a path–velocity-coupled motion planning module was designed to generate a safe and smooth trajectory to pull the vehicle over. The results of our experiments show that the proposed algorithms have obvious advantages in success rate, efficiency, stability, and safety compared with the traditional method. Validation on multiple simulation and real-world platforms proves the feasibility and adaptivity of the module in traffic scenarios.

Keywords: driver disability; driving intervention; danger avoidance; risk evaluation; motion planning; automated control (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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