A Control Policy based Driving Safety System for Autonomous Vehicles

• Published in: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS) pp. 464 - 472
• Main Authors: Kang, Liuwang, Shen, Haiying
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2021
• Subjects: Autonomous vehicle; Conferences; Control policy extraction; Driving safety; Optimal control; Optimal control policy determination; Real-time systems; Safety; Sensors; Simulation; Web and internet services
• ISSN: 2155-6814
• DOI: 10.1109/MASS52906.2021.00064

Autonomous vehicles (AVs) follow their control policies and make real-time decisions based on measured signals from sensors to ensure driving safety. With the development of sensor measurement technologies, an AV usually adopts more sensors to measure its driving environments and develops more control policies to satisfy people's expectations on driving safety. However, these control policies are usually implemented with codes that are not open to the public and usually difficult to understand for people, which causes people's strong concerns about AVs' driving safety. In this paper, we propose a control policy based driving safety system (Polsa) to help improve \overline{\mathrm{d}\mathrm{r}\mathrm{i}}ving safety of a given AV. For a given AV, Polsa extracts its control policies and determines the safest control behavior among multiple control behaviors for each given trigger condition, which can be used by AV companies that produce the AV to improve the AV's driving safety. Accordingly, first, Polsa has a control policy extraction method that uses dynamic time warping and k-means clustering technologies to cluster historical driving data with the same control behavior type together and then analyzes positions and driving speeds in each cluster to extract control policies of a target AV. Second, Polsa has an optimal control policy determination method to determine the safest control behavior for each given trigger condition. Unlike previous works that consider that the state of the target AV's nearby vehicle is constant during a time period, Polsa considers time-varying driving state of its nearby vehicle, thus deriving more safer control behavior. We use an industry-standard AV platform (Baidu Apollo) to evaluate optimal control policy success rate of Polsa in comparison with two state-of-the-art methods. The comparison results show that Polsa can extract control policies with 83% accuracy, and improve optimal control policy success rate by 28% compared with existing methods, which demonstrates high performance of Polsa in extracting control policies and determining optimal control behavior.