Enhanced Integrated Decision and Control for High-Level Automated Vehicles and Its Experiment Verification

Learning through experience is essential for high-level autonomous driving systems, as it has the potential to enhance driving performance in corner cases. However, current decision and control modules adopt an empirical design paradigm for engineering efficiency, relying heavily on expert rules or real-vehicle data, failing to fully cover and optimize edge scenarios. To address this gap, we propose an enhanced integrated decision and control method that leverages reinforcement learning as the optimal control problem solver, endowing high-level automated vehicles with experience data usage. Specifically, a constrained mixed policy gradient algorithm is developed, which combines and dynamically adjusts the application ratio of experience data and the environmental model during training. This approach achieves fast convergence while maintaining high performance even with inaccurate analytic models. Furthermore, an attention based encoding network is designed to accommodate diverse driving states in urban traffic, integrating an embedding network for feature extraction and a weighting network for feature fusion, realizing order-insensitive encoding and importance differentiation of road users. The trained policy is deployed on a fully functional autonomous vehicle. Experiments at a signalized intersection show that the proposed method can accurately identify critical surrounding obstacles and execute safe, efficient, and intelligent driving behaviors across 32 scenarios.