As a core part of autonomous driving systems, motion planning has received
extensive attention from academia and industry. However, real-time trajectory
planning capable of spatial-temporal joint optimization is challenged by
nonholonomic dynamics, particularly in the presence of unstructured
environments and dynamic obstacles. To bridge the gap, we propose a real-time
trajectory optimization method that can generate a high-quality whole-body
trajectory under arbitrary environmental constraints. By leveraging the
differential flatness property of car-like robots, we simplify the trajectory
representation and analytically formulate the planning problem while
maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve
efficient obstacle avoidance with a safe driving corridor for unmodelled
obstacles and signed distance approximations for dynamic moving objects. We
present comprehensive benchmarks with State-of-the-Art methods, demonstrating
the significance of the proposed method in terms of efficiency and trajectory
quality. Real-world experiments verify the practicality of our algorithm. We
will release our codes for the research communit