Signal Temporal Logic-Constrained Motion Control of Electric Vehicles Under Slippery Roads Using an MPPI Approach

Driving on icy roads poses significant challenges for automated electric vehicles (AEVs) motion planning and control due to uncertain dynamics, multiple constraints, and temporal rules satisfying problems.This paper proposes a hybrid data-driven Model Predictive Path Integral (MPPI) control framework with temporal logic constraints for real-time safe motion planning and trajectory tracking on slippery roads. To handle modeling uncertainties and mismatches in vehicle dynamics, a hybrid model is constructed by combining a nominal physics-based baseline with a 1-Lipschitz residual neural network pretrained via the collected vehicle data. This model captures both nominal dynamics and unmodeled residual effects, providing a reliable system representation of AEVs for controller design with robustness performance guaranteed. The pretrained model is embedded into a sampling-based predictive controller, where state constraints and task specifications expressed by temporal logic formulas are incorporated into the cost function. So that, our control strategy ensures that the generated trajectories adhere to both safety constraints and high-level behavioral objectives with real time solving guaranteed. Real electric vehicle experiments validate the effectiveness of the proposed method and demonstrate its superiority in achieving safe and reliable planning and tracking control on icy roads.