Multi-Agent Reinforcement Learning for Cooperative Autonomous Driving and Distributed Traffic Optimization
Keywords:
multi-agent reinforcement learning; cooperative autonomous driving; distributed traffic optimization; intelligent transportation systems; socio-technical governance; robustness; fairness; sustainabilityAbstract
Cooperative autonomous driving and distributed traffic optimization represent a convergence of multi-agent reinforcement learning, networked control, transportation engineering, and socio-technical governance. As vehicles gain perception, prediction, and planning capabilities, the central challenge shifts from isolated autonomy to coordinated collective behavior under uncertainty, latency, incomplete information, and conflicting objectives. This paper examines multi-agent reinforcement learning as a system-level architecture for cooperative driving and distributed traffic optimization. It emphasizes structural trade-offs among centralized coordination, edge intelligence, vehicular communication, and cloud-based orchestration. The analysis considers how centralized training with decentralized execution can support scalable intersection management, cooperative adaptive cruise control, signal optimization, and fleet routing, while also exposing risks related to nonstationarity, credit assignment, communication failure, and adversarial interference. The discussion extends beyond algorithmic performance to deployment, maintenance, energy demand, sustainability, accountability, fairness, and policy. It argues that technical success will depend on hybrid governance that combines real-time distributed learning with public-interest oversight, robust safety assurance, and equitable access. Case illustrations from autonomous intersections, traffic signal control, mixed autonomy, and cross-domain infrastructures clarify both opportunities and limits. The conclusion proposes a research agenda centered on verifiable coordination, interoperable standards, resilient infrastructure, and justice-oriented mobility design. Without such integration, multi-agent reinforcement learning may optimize narrow traffic metrics while reproducing congestion, exclusion, and institutional fragility.
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