Motion Planning for Urban Autonomous Bus Considering Localization Uncertainty Using Offset-Free MPC

Abstract

This study proposes a novel motion planning strategy to address localization uncertainty in autonomous buses. Conventional motion planning algorithms utilize information from high-definition (HD) maps to overcome the limited detection range of on-board sensors. However, this information contains uncertainty due to the utilization of estimated localization results during reference frame transformation. The wider dimensions of autonomous buses, compared to regular vehicles, amplify the potential dangers associated with localization uncertainty. Therefore, this research focuses on enhancing motion planning for autonomous buses by effectively addressing localization uncertainty. The investigation of manual driving data from autonomous buses highlights the need to handle three issues: heading bias, lateral position error, and longitudinal position error. Firstly, the heading bias was dealt with by implementing an Offset-free Model Predictive Control (OF-MPC) with a Moving Horizon Estimation (MHE) scheme for lateral motion planning. Secondly, the lateral position error was handled by incorporating a drivable corridor to determine the desired path. Lastly, the longitudinal position error was resolved by implementing a chance-constrained MPC for longitudinal motion planning. The proposed approach showed noticeable enhancement in path tracking performance while still securing ride comfort in lateral motion, collision safety, and prevention of stop-line violations. We evaluated the feasibility of the proposed approach through vehicle tests on a test track, and its applicability was further confirmed through fully autonomous driving tests on actual urban bus-only lanes.

Keywords

• Autonomous bus
• autonomous driving
• chance-constrained model predictive control
• localization uncertainty
• moving horizon estimation
• offset-free model predictive control