A Twisted Gaussian Risk Model Considering Target Vehicle Longitudinal-Lateral Motion States for Host Vehicle Trajectory Planning
Zhisong Zhou, Yafei Wang, Guofeng Zhou, Kanghyun Nam, Zhongwei Ji, Chengliang Yin
Abstract
Collision risk modeling with multiple surrounding target vehicles (TVs) is essential for host vehicle (HV) trajectory planning, especially considering challenging TV lateral behaviors. Existing motion-compensated spatial methods ignore TV lateral motion states such as lateral velocity and yaw rate, so that TV lateral behavior cannot be described accurately. Aiming at high-accuracy collision risk modeling, a twisted Gaussian risk model using both longitudinal and lateral motion states for TV behavior description is proposed. Firstly, the HV-TVs system is treated as the superposition of multiple HV-TV units, and a Gaussian risk model is adopted for the collision risk description of the HV-TV unit. Then, by expanding the variances, TV longitudinal and lateral velocities are considered. At last, a twisted Gaussian risk model considering TV yaw rate is constructed based on the projection of the Gaussian risk model. With this twisted Gaussian risk model, TV longitudinal-lateral motion states are considered simultaneously, and TV behaviors can be described for HV-TVs collision risk modeling. For HV trajectory planning, trajectory candidates generated by the maneuver-inspired method are evaluated via the proposed risk model, and the safe and efficient trajectory is selected. Simulation and hardware-in-the-loop experimental results show that the proposed method considering TV longitudinal-lateral motion states allows HV to operate more safely and efficiently than the conventional method.