Personalized Decision-Making Framework for Collaborative Lane Change and Speed Control Based on Deep Reinforcement Learning

Autonomous driving (AD) is critically dependent on intelligent decision-making technology, which is the crucial ingredient in driving safety and overall vehicle performance. And comprehensive consideration of driving heterogeneity, decision synergy, and game interaction is also the cornerstones. Accordingly, this paper constructs a cooperative decision-making framework for autonomous vehicles (AVs) that integrates driving styles within a hierarchical architecture based on deep reinforcement learning (DRL). The upper layer adopts the action shielding mechanism-based dueling-double deep Q-network (D3QN) algorithm incorporating the lane advantages into shared state space to complete the prompt lane-changing (LC) decision, the lower layer applies the soft actor-3-critic (SA3C) algorithm based on the clipped triple Q-learning to provide the continuous speed adaptive control. Three personalized collaborative decision strategies are formulated for particular driving styles in multi-objective optimization preference combined with style-incentive prioritized experience replay (SIPER). The experimental results confirm that the proposed framework can satisfy the personalized driving demands in complex traffic scenarios, effectively explore the prospective LC opportunities, and enhance the driving efficiency by 35.40% with aggressive strategy and the comfort by 56.46% with defensive strategy compared with normal strategy, while maintaining the safety.