Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data

Weixuan Zhou; Xuesong Wang; Yi Glaser; Xiangbin Wu; Xiaoyan Xu

doi:10.1016/j.aap.2022.106834

Developing an improved automatic preventive braking system based on safety-critical car-following events from naturalistic driving study data

Accid Anal Prev. 2022 Dec:178:106834. doi: 10.1016/j.aap.2022.106834. Epub 2022 Sep 21.

Authors

Weixuan Zhou¹, Xuesong Wang², Yi Glaser³, Xiangbin Wu⁴, Xiaoyan Xu¹

Affiliations

¹ School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
² School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China. Electronic address: wangxs@tongji.edu.cn.
³ Global Safety Center, GM, Warren, MI 48092-2031, USA.
⁴ Intelligent Driving Lab, Intel Labs China, Beijing 100190, China.

PMID: 36150234
DOI: 10.1016/j.aap.2022.106834

Abstract

In public road tests of autonomous vehicles in California, rear-end crashes have been the most common type of crash. Collision avoidance systems, such as autonomous emergency braking (AEB), have provided an effective way for autonomous vehicles to avoid collisions with the lead vehicle, but to avert false alarms, AEB tends to apply late and hard brake only if a collision becomes unavoidable. Automatic preventive braking (APB) is a new collision avoidance method used in Mobileye's Responsibility-Sensitive Safety (RSS) model that aims to reduce crashes with a milder brake and decreased impact on traffic flow, but APB's safety performance is inferior to that of AEB. This study therefore proposes three safety improvement strategies for APB, the addition of response time, safety buffer, and minimum following distance; and combines them in different ways into four improved APB systems, IP1-IP4. Simulating car-following safety-critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study in MATLAB's Simulink, the safety performance, conservativeness, and driving comfort of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) IP4, the system that integrated all three strategies, outperformed the baseline APB and IP1-IP3 and prevented all SCEs from becoming crashes; 2) IP4 was slightly more conservative than AEB, but less conservative than RSS; 3) APB's jerk-bounded braking profile improved driving comfort; and 4) higher deceleration was found in the two AEB systems (both 8.1 m/s²) than in IP4 (6.7 m/s²), but they failed to prevent all crashes. Our proposed APB system, IP4, can provide safe, efficient, and comfortable braking for AVs in car-following SCEs, and has the potential to be practically applied in vehicle collision avoidance systems.

Keywords: Automatic preventive braking; Autonomous emergency braking; Car-following scenario; Naturalistic driving study; Safety-critical event.

MeSH terms

Accidents, Traffic / prevention & control
Automobile Driving*
Automobiles
China
Deceleration*
Humans
Protective Devices