Vehicle-Cloud Cooperative Trajectory Planning via Switched Model Predictive Control for UGVs in Unstructured Transportation Environment

Abstract

Recent advances in cloud computing and vehicle-to-everything (V2X) communication have created significant opportunities for intelligent transportation systems (ITS), particularly for tackling complex, high-level autonomous navigation tasks in unstructured environment that exceed the capabilities of onboard computing alone. However, the inherent uncertainty and latency of current wireless networks present a critical challenge to the safe deployment of cloud-based computation in safety-critical vehicular applications. To overcome these limitations, we propose a vehicle–cloud cooperative planning framework built on a parallel planning architecture. This approach simultaneously harnesses onboard and cloud resources: a lightweight linear model predictive control (LMPC) runs locally on the vehicle for fast, low-latency responses, while a more computationally intensive nonlinear model predictive control (NMPC) executes on the cloud for enhanced foresight and optimality. A risk-based switching policy dynamically selects the optimal outputs of both planners, ensuring real-time adaptability and robustness in complex, unstructured traffic scenarios. Extensive simulation studies demonstrate that the proposed cooperative framework reduces collision probabilities by 32%–77% compared to standalone planners across diverse scenarios. The framework’s practical feasibility and real-world viability are further substantiated through experiments conducted on a purpose-built, scalable real-vehicle testing platform, marking a critical extension of our collaborative research from simulation to physical implementation.