Barrier Costs-Embedded MPPI Control for Rollover-Safe Cooperative Payload Transport with All-Wheel-Steering Distributed Electric-Drive Carriers

Autonomous driving control of heavy-duty vehicles remains highly challenging, particularly for cooperative transport systems (CTSs) carrying oversized payloads with multiple vehicle carriers. A primary difficulty arises from the frequent occurrence of stability-related accidents, such as rollover, during critical maneuvers. In this paper, a novel rollover prevention control pipeline for CTSs equipped with all-wheel-steering (AWS) distributed-electric-drive carriers is proposed. The framework is built upon an innovative iterative model predictive path integration (i-MPPI) method, which exhibits distinct advantages over common model predictive control (MPC) in dealing with the complex, nonlinear, high-dimension coupled dynamics of CTSs. To ensure effective rollover prevention performance, a control-barrier-function (CBF)-inspired safety cost is embedded into the sampling-based trajectory evaluation process, and a one-step safety modification is further applied to the i-MPPI control command. Extensive simulation results demonstrate that the proposed pipeline successfully prevents rollover while outperforming multiple baseline approaches in typical critical situations. The maximum LTRs decrease 13.33% and 18.57% compared with applying the conventional ReLU cost in two testing scenarios, respectively, and the computation speed reaches 7 – 10 times faster than common MPC. Moreover, the results reveal that the AWS distributed-drive architecture enables flexible pose regulation of both the payload and the carriers, allowing rollover mitigation to be achieved without significant degradation of trajectory tracking accuracy. The path tracking error of carriers decreases more than 50% and 80% in the two scenarios compared with using front-wheel-steering-and-driving FWSD carriers.