Trajectory planning is crucial for the safe driving of autonomous vehicles in highway traffic flow. Currently, some advanced trajectory planning methods utilize spatio-temporal voxels to construct feasible regions and then convert trajectory planning into optimization problem solving based on the feasible regions. However, these feasible region construction methods cannot adapt to the changes in dynamic environments, making them difficult to apply in complex traffic flow. In this paper, we propose a trajectory planning method based on adaptive spatio-temporal voxels which improves the construction of feasible regions and trajectory optimization while maintaining the quadratic programming form. The method can adjust feasible regions and trajectory planning according to real-time traffic flow and environmental changes, realizing vehicles to drive safely in complex traffic flow. The proposed method has been tested in both open-loop and closed-loop environments, and the test results show that our method outperforms the current planning methods.

翻译：轨迹规划对于自动驾驶车辆在高速公路交通流中的安全行驶至关重要。当前，一些先进的轨迹规划方法利用时空体素构建可行区域，并基于这些可行区域将轨迹规划转化为优化问题求解。然而，这些可行区域构建方法无法适应动态环境的变化，难以应用于复杂交通流。本文提出一种基于自适应时空体素的轨迹规划方法，该方法在保持二次规划形式的同时，改进了可行区域构建与轨迹优化。该方法能根据实时交通流及环境变化调整可行区域和轨迹规划，实现车辆在复杂交通流中的安全行驶。所提方法已在开环和闭环环境中进行测试，结果表明该方法优于当前规划方法。