智能网联车辆在复杂交通场景中的高效、安全运动规划是自动驾驶领域的关键挑战。该研究基于Transformer提出了ST-Trans交通预测模型，并通过ST-Trans开发了用于智能网联车辆的预测性运动规划器。ST-Trans利用Transformer从动态高精度地图提供的实时车辆数据和车道段结构信息中挖掘交通时空演化规律来预测车道段的未来交通状态，并利用车道段连通性和交叉口信号相位信息进一步提升预测准确性。模型采用编码器-解码器架构，通过车道编码器融合车辆与车道特征，道路编码器建模动态拓扑关系，解码器迭代生成未来交通状态序列。预测结果显示ST-Trans在平均绝对误差、均方根误差和准确率上分别比最优基准线模型高出12.2%、12.1%和3.55个百分点。基于ST-Trans的预测结果，预测性运动规划器采用双层结构，底层路径规划器动态选择目标点并融合动态规划与二次规划生成平滑路径，上层速度规划器构建时空走廊以压缩解空间，并同样结合动态规划与二次规划生成安全高效的速度曲线，从而显著降低运动规划任务的求解复杂度。该研究结合SUMO和CARLA对预测运动规划器进行了仿真实验，结果表明，基于ST-Trans的预测运动规划器能够实现预测性路径和速度规划，并在安全性、效率、舒适性和计算速度方面优于传统运动规划器。实验验证了所提方法能有效缩短高风险状态持续时间，提高通行效率，并保持较低的计算延迟。

Efficient and safe motion planning for intelligent connected vehicles in complex traffic scenarios remains a pivotal challenge in the field of autonomous driving. This research proposed ST-Trans traffic prediction model based on the Transformer architecture and developed a predictive motion planner for intelligent connected vehicles leveraging ST-Trans. The ST-Trans model utilizes the Transformer architecture to mine spatial-temporal evolution patterns from real-time vehicle data and lane segment structural information provided by dynamic high-definition maps, thereby predicting future traffic states of lane segments. It further enhances prediction accuracy by incorporating lane segment connectivity and intersection signal phase information. The model adopts an encoder-decoder framework, where a lane encoder fuses vehicle and lane features, a road encoder models dynamic topological relationships, and a decoder iteratively generates future traffic state sequences. Experimental results demonstrate that ST-Trans outperforms the optimal baseline model by 12.2%, 12.1%, and 3.55 percentage points in terms of mean absolute error(MAE), root mean square error(RMSE), and accuracy, respectively. Based on the predictions from ST-Trans, the proposed predictive motion planner employs a two-layer structure. The lower-layer path planner dynamically selects target points and integrates dynamic programming with quadratic programming to generate smooth paths. The upper-layer speed planner constructs spatio-temporal corridors to compress the solution space and similarly combines dynamic programming and quadratic programming to generate safe efficient, and comfortable speed profiles. This structure significantly reduces the computational complexity of the motion planning task. Simulation experiments were conducted using SUMO and CARLA to evaluate the predictive motion planner. The results indicate that the ST-Trans-based predictive motion planner successfully implements predictive path and speed planning, and outperforms traditional motion planners in terms of safety, efficiency, comfort, and computational speed. The experiments verify that the proposed method effectively shortens the duration of high-risk states, improves traffic throughput and maintains low computational latency.