Safe and Efficient Trajectory Optimization for Autonomous Vehicles Using B-Spline With Incremental Path Flattening

Abstract

Gradient-based trajectory optimization with B-spline curves is widely used for unmanned aerial vehicles (UAVs) due to its fast convergence and continuous trajectory generation. However, the application of B-spline curves for path-velocity coupled trajectory planning in autonomous vehicles (AVs) has been highly limited because it is challenging to reduce the over-approximation of the vehicle shape and to create a collision-free trajectory using B-spline curves while satisfying kinodynamic constraints. To address these challenges, this paper proposes novel disc-type swept volume (SV), incremental path flattening (IPF), and kinodynamic feasibility penalty methods. The disc-type SV estimation method is a new technique to reduce SV over-approximation and is used to find collision points for IPF. In IPF, the collision points are used to push the trajectory away from obstacles and to iteratively increase the curvature weight, thereby reducing SV and generating a collision-free trajectory. Additionally, to satisfy kinodynamic constraints for AVs using B-spline curves, we apply a clamped B-spline curvature penalty along with longitudinal and lateral velocity and acceleration penalties. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments. We also conducted a real-world experiment using an AV, and our results validate the simulated tracking performance of the proposed approach.