Bézier curve‐based trajectory planning for autonomous vehicles with collision avoidance

This study proposes an effective trajectory planning algorithm based on the quartic Bézier curve and dangerous potential field for automatic vehicles. To generate collision‐free trajectories, potential field functions are introduced to evaluate the collision risk of path candidates. However, many studies on artificial potential field approaches primarily focus on static and straight roads, and attach less importance to more complex driving scenarios, such as curving roads. In this study, a novel method based on the Frenet coordinate system is proposed to address such limitations. Moreover, to balance the driving comfortability and the driving safety of the path candidate, the path‐planning problem is converted to an optimisation problem, and sequential quadratic programming algorithm is employed to tackle this task. Another merit of this algorithm is the curvature of the generated path is continuous even at the joints of adjacent sub‐trajectories by utilising several specific properties of the Bézier curve. Furthermore, to execute the generated trajectory, a framework of velocity generation is proposed while vehicle dynamic constraints are considered. Some typical traffic scenarios, including lane‐changing, lane‐keeping, and collision avoidance have been designed to verify the performance of the proposed algorithm, and simulations demonstrate the validity of this method.