Cloud-based Predictive Path Tracking Control for Global Vehicle Targets with Uncertain Latency

The path tracking performance of global vehicle targets (GVTs) is crucial in closed-field testing for autonomous vehicles (AVs). However, the uncertain latency of 5G communication, when GVTs receive control commands from the cloud server, is likely to impact their path tracking performance adversely. To address this issue, we propose a cloud-based predictive path tracking control strategy considering the uncertain latency of 5G communication. Firstly, the 5G communication dataset from the closed test field is analyzed, and an LSTM-attention latency prediction model is established and validated. Subsequently, a nonlinear model predictive control (NMPC) algorithm that incorporates communication latency is formulated. Finally, real-world tests are conducted, and the results demonstrate that the proposed method significantly improves latency prediction accuracy and path tracking performance of GVTs.