We propose a novel B-spline trajectory optimization method for autonomous racing. We consider the unavailability of sophisticated race car and race track dynamics in early-stage autonomous motorsports development and derive methods that work with limited dynamics data and additional conservative constraints. We formulate a minimum-curvature optimization problem with only the spline control points as optimization variables. We then compare the current state-of-the-art method with our optimization result, which achieves a similar level of optimality with a 90% reduction on the decision variable dimension, and in addition offers mathematical smoothness guarantee and flexible manipulation options. We concurrently reduce the problem computation time from seconds to milliseconds for a long race track, enabling future online adaptation of the previously offline technique.

翻译：我们提出了一种新颖的基于B样条的轨迹优化方法，用于自主赛车。考虑到在早期自主赛车运动开发阶段缺乏复杂的赛车和赛道动力学信息，我们推导了适用于有限动力学数据且附加保守约束的方法。我们构建了一个仅以样条控制点为优化变量的最小曲率优化问题。随后，我们将当前最先进方法与我们的优化结果进行了比较，结果显示我们的方法在决策变量维度上缩减了90%的同时达到了相似的最优水平，此外还提供了数学上的光滑性保证和灵活的操作选项。同时，我们将长赛道的计算时间从秒级缩短至毫秒级，使得先前离线技术未来可实现在线自适应调整。