Optimizing Autonomous Vehicle Navigation through Reinforcement Learning in Dynamic Urban Environments

Autonomous vehicle (AV) navigation in dynamic urban environments faces challenges such as unpredictable traffic conditions, varying road user behaviors, and complex road networks. This study proposes a novel reinforcement learning-based framework that enhances AV decision making through spatial-temporal context awareness. The framework integrates Proximal Policy Optimization (PPO) and Graph Neural Networks (GNNs) to effectively model urban features like intersections, traffic density, and pedestrian zones. A key innovation is the urban context-aware reward mechanism (UCARM), which dynamically adapts the reward structure based on traffic rules, congestion levels, and safety considerations. Additionally, the framework incorporates a Dynamic Risk Assessment Module (DRAM), which uses Bayesian inference combined with Markov Decision Processes (MDPs) to proactively evaluate collision risks and guide safer navigation. The framework’s performance was validated across three datasets—Argoverse, nuScenes, and CARLA. Results demonstrate significant improvements: an average travel time of 420 ± 20 s, a collision rate of 3.1%, and energy consumption of 11,833 ± 550 J in Argoverse; 410 ± 20 s, 2.5%, and 11,933 ± 450 J in nuScenes; and 450 ± 25 s, 3.6%, and 13,000 ± 600 J in CARLA. The proposed method achieved an average navigation success rate of 92.5%, consistently outperforming baseline models in safety, efficiency, and adaptability. These findings indicate the framework’s robustness and practical applicability for scalable AV deployment in real-world urban traffic conditions.