Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

• Published in: IEEE access Vol. 11; pp. 5772 - 5788
• Main Authors: Kim, Changhee, Yoon, Youngmin, Kim, Sangyoon, Yoo, Michael Jinsoo, Yi, Kyongsu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous driving; autonomous vehicle; Autonomous vehicles; Avoidance; Behavioral sciences; Control algorithms; Control theory; Decision making; Driving; Evasive actions; Lane changing; model predictive control; Motion planning; Performance prediction; Planning; Prediction algorithms; Predictive control; Risk; Road traffic; Test vehicles; Traffic flow; Traffic planning; Trajectory; Trajectory control; Trajectory planning; Urban environments; Vehicle dynamics; vehicle dynamics and control
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3236816

This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.