Abstract: In public road tests of autonomous vehicles in California, rear-end crashes were the most common type of crashes. Autonomous emergency braking (AEB) systems have provided an effective way for autonomous vehicles to avoid front collisions, however, the hard brake of the AEB systems may cause crashes from the rear car. Automatic preventive braking (APB) is a new method proposed by Mobileye that aims to reduce crashes with mild brake and reduce influence on traffic flow, but APB’s safety performance needs to be improved. This study therefore proposed three safety improvement strategies for APB and combined them in different ways into four improved APB systems (IP1-IP4). Using car-following safety-critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study and simulated in MATLAB’s Simulink, the safety performance and conservativeness of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) the proposed system that integrates all three strategies (IP4, with modifications to response time, safe criterion, and minimum following distance) outperformed the baseline APB, IP1-IP3 and prevented all crashes; 2) even with a deceleration rate of 8.1 m/s2, higher than the IP4 rate of 6.7m/s2, the two AEB systems failed to prevent all crashes; 3) IP4 improved safety and was less conservative than human drivers as it prevented the one crash in the extracted events, reduced the average minimum TTC by 1.68 s, reduced the V_dev by 0.64 m/s, and reduced the average activated distance by 2.27 m.
Weixuan Zhou, Xuesong Wang*, Yi Glaser, Xiaoyan Xu. Developing an Improved Automatic Preventive Braking System Based on Safety Critical Car-Following Events from Naturalistic Driving Study Data. Transportation Research Board 101th Annual Meeting, Washington D.C., USA, 2022. 1.9-13.