Dynamic Obstacle Avoidance Planning for Autonomous Driving Based on Relative Velocity Potential Field and Kinematic Constraints

Abstract

Addressing the challenges of obstacle avoidance for autonomous vehicles in complex dynamic environments, traditional Artificial Potential Field (APF) methods often suffer from delayed responses to dynamic obstacles and generate paths that violate vehicle kinematic constraints. To overcome these limitations, this paper proposes an improved path planning algorithm, the Relative Velocity Potential Field (RVPF), which fuses relative velocity information with kinematic constraints. First, a relative velocity sensitivity factor is introduced to construct a dynamic potential field. By dynamically reshaping the repulsive field distribution based on the relative velocity vector, this approach endows the algorithm with a predictive capability regarding collision risks, facilitating a transition from passive reaction to active defense. Second, a vehicle kinematic model is established incorporating Ackermann steering geometry. A virtual tangential force strategy is employed to map the resultant potential forces into control variables that adhere to wheelbase and steering angle limits, thereby ensuring the generation of smooth and feasible trajectories. Simulation results demonstrate the superior performance of the proposed algorithm in scenarios involving high-speed oncoming traffic, overtaking, and lateral crossing. Notably, in the lateral crossing scenario—where traditional APF failed due to collisions—the RVPF algorithm achieved collision-free passage by actively decelerating and yielding, increasing the minimum safety distance to 5.02 m. These results confirm that the proposed algorithm significantly enhances the safety and stability of autonomous vehicles across diverse traffic situations.