Autonomous Driving Economic Car-Following Motion Strategy Based on Adaptive Rollout Model-Based Policy Optimization

The reinforcement learning (RL) is a powerful framework with significant potential to enhance the autonomous driving (AD) performance. However, its trial-and-error nature presents significant hurdles in terms of safety, efficiency, and stability. To address these challenges, we propose an adaptive rollout model-based policy optimization (AR-MBPO) algorithm tailored for car-following motion planning in autonomous electric vehicles (AEVs). The algorithm improves the overall performance by incorporating an error-aware ensemble environmental model and leveraging branched rollouts for efficient sample collection and policy optimization. A key innovation of AR-MBPO is an adaptive rollout mechanism that dynamically adjusts based on the predictive accuracy, mitigating the impact of model inaccuracies. In addition, the energy efficiency is explicitly integrated into the optimization process to minimize the energy consumption. We evaluate AR-MBPO through AEV car-following simulations, where it demonstrates superior performance, including rapid convergence, and reduced reliance on real-world interactions. The method simultaneously optimizes safety, traffic efficiency, and energy efficiency through dynamic distance adjustments, as evidenced by a 0% collision rate in testing scenarios and an 8.2% energy consumption reduction compared with nonenergy-aware baselines. The results suggest potential applications in AD systems for improved safety and energy efficiency.