A Trajectory Planning Approach Incorporating Load Transfer Dynamics and Energy-Based Rollover Risk for Autonomous Truck Platoon

During high-speed platooning, if the leading vehicle experiences sudden failure (e.g., tire blowout or braking loss), the following vehicles must execute simultaneous deceleration and obstacle avoidance within a minimal timeframe. This process induces significant load transfer, leading to drastic variations in vertical load distribution and tire cornering stiffness, thereby critically compromising lateral stability. Conventional planning methods, reliant on static stability boundaries and fixed rollover thresholds, fail to adapt to such dynamic perturbations, resulting in degraded control performance. This paper proposes an intelligent trajectory planning framework for emergency obstacle avoidance in heavy-duty truck platoons. The framework incorporates a physics-informed neural network to predict real-time load transfer effects on tire cornering stiffness, combined with an Energy-based Rollover Index (ERI) for dynamic stability boundary assessment. Innovatively integrating the nonlinear effects of braking-induced load transfer into coordinated trajectory-speed optimization, the method enhances platoon maneuverability while ensuring safety. Simulation results demonstrate that the proposed approach increases maximum safe lane-change speeds by 0.5%-4.3% while reducing false rollover alarms to below 2%. Co-simulation using MATLAB/Simulink and TruckSim verifies a 4.2% improvement in overall planning performance while maintaining dynamic safety standards.