B-spline-based trajectory optimization has been widely used in robot navigation, especially as quadrotor-like vehicles can easily enjoy the advantage of a B-spline curve (e.g. computational efficiency) with its convex hull property for trajectory optimization. Nevertheless, leveraging the B-splined-based optimization algorithm to generate a collision-free trajectory for autonomous vehicles is still challenging because their complex vehicle kinematics make it difficult to use the convex hull property. In this paper, we propose a novel trajectory optimization algorithm for autonomous vehicles that enables the advantage of a B-spline curve into a B-spline-based optimization algorithm by incorporating vehicle kinematics with two methods. An incremental path flattening is a new method that iteratively increases path curvature weight around vehicle collision points to find a collision-free path by reducing swept volume. A new swept volume estimation method can reduce over-approximation of the swept volume and make the vehicle pass through a narrow corridor without losing safety. Furthermore, a clamped B-spline curvature constraint, which can simplify a B-spline curvature constraint, is added with other feasibility constraints (e.g. longitudinal \& lateral velocity and acceleration) for the vehicle kinodynamic constraints. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments and verifies its valid tracking performance with an autonomous vehicle in a real-world scenario.

翻译：基于B样条的轨迹优化在机器人导航中已被广泛采用，特别是类四旋翼飞行器可凭借B样条曲线的凸包特性有效利用其计算效率等优势进行轨迹优化。然而，将基于B样条的优化算法应用于自动驾驶车辆的无碰撞轨迹生成仍具挑战，因为其复杂的车辆运动学特性使得凸包特性的应用存在困难。本文提出一种面向自动驾驶车辆的新型轨迹优化算法，通过两种方法融合车辆运动学特性，将B样条曲线的优势融入基于B样条的优化框架。其中，增量路径平坦化是一种迭代增大车辆碰撞点周围路径曲率权重的新方法，通过减小扫掠体积来搜索无碰撞路径；而新型扫掠体积估计方法能降低扫掠体积的过估计程度，使车辆在保持安全性的前提下通过狭窄通道。此外，本文还引入夹持B样条曲率约束（可简化B样条曲率约束），并与纵向/横向速度及加速度等其他可行性约束共同构成车辆运动动力学约束条件。实验结果表明，本方法在多种仿真环境中均优于现有最优基线方法，并通过真实场景下的自动驾驶车辆轨迹跟踪实验验证了其有效性能。