Efficient Safety-Enhanced Velocity Planning for Autonomous Driving With Chance Constraints

Velocity planning is an important module of autonomous driving, which aims to generate the velocity profile given a reference path. However, most existing algorithms fail to adequately address the uncertainty inherent in driving contexts, leading to potentially risky situations. To this end, we propose an efficient safety-enhanced velocity planning algorithm (ESEVP), which uses chance constraints to take uncertainties from trajectory prediction and velocity tracking into account, arising great improvement in driving safety. In addition, ESEVP formulates velocity planning as quadratic programming and explores candidate solutions through a fast planning space construction method, which ensures efficiency and covers all the interaction possibilities. Experimental results obtained from various scenarios demonstrate that ESEVP outperforms recent state-of-the-art methods in terms of safety, comfort, and driving efficiency. Besides, we successfully deploy ESEVP in real traffic, showcasing its competitive capabilities in practice.

Automated summary

Velocity planning is crucial for autonomous driving as it generates the velocity profile based on a reference path. However, existing algorithms often neglect uncertainties in driving contexts, leading to potential risks. To address this, we propose an efficient safety-enhanced velocity planning algorithm (ESEVP) that considers uncertainties from trajectory prediction and velocity tracking using chance constraints. ESEVP formulates velocity planning as quadratic programming and explores candidate solutions through a fast planning space construction method, ensuring efficiency and covering all interaction possibilities. Experimental results prove ESEVP's superiority in terms of safety, comfort, and driving efficiency, and its successful deployment in real traffic demonstrates its practical competitiveness.