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 community

翻译：作为自动驾驶系统的核心组成部分，运动规划已受到学术界和工业界的广泛关注。然而，能够实现时空联合优化的实时轨迹规划面临非完整动力学的挑战，尤其在存在非结构化环境和动态障碍物的情况下。为填补这一空白，我们提出一种实时轨迹优化方法，能够在任意环境约束下生成高质量全身轨迹。通过利用类车机器人的微分平坦特性，我们在保持非完整动力学可行性的同时简化轨迹表示并解析建模规划问题。此外，我们通过针对未建模障碍物的安全行驶走廊和针对动态移动目标的符号距离近似实现高效避障。我们与现有最优方法进行了全面基准测试，证明了所提方法在效率和轨迹质量方面的显著优势。实际实验验证了算法的实用性。我们将为研究社区公开代码。