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Deployment of autonomous vehicles (AVs) is transforming expressway merging zones into complex mixed-traffic environments. Multi-agent reinforcement learning enables vehicle cooperation but is demanding in computational and infrastructural costs, while prevailing deep reinforcement learning (DRL)-based single-vehicle intelligence approaches rely on single network to generate both car-following and lane-changing decisions, introducing coupled rewards and reducing learning efficiency. This study presents a dual-network DRL approach for AV driving strategy in merging zones. Two dedicated networks independently optimize lane-changing and car-following decisions, supported by coupled and action-specific rewards. Evaluated on Jungong Road on-ramp merging section in Shanghai against four baselines, the DDRL strategy improves efficiency, safety, and comfort by up to 12.4%, 79.8%, and 5.3% over rule-based and single-vehicle intelligence baselines. When compared to multi-vehicle cooperation baselines, it requires only half the computation time with acceptable under-performance of less than 5%, featuring a robust balance between traffic operation performance, computational efficiency and deployment feasibility.
Wang et al. (Fri,) studied this question.