Multi-Agent Reinforcement Learning for Interactive Trajectory Planning in Autonomous Driving
Keywords:
multi-agent reinforcement learning; autonomous driving; interactive trajectory planning; socio-technical systems; safety assurance; governance; fairness; deployment; sustainabilityAbstract
Interactive trajectory planning in autonomous driving is not merely a motion optimization problem but a socio-technical coordination problem involving uncertain human intent, mixed-motive traffic agents, real-time infrastructure constraints, and public accountability. This paper examines multi-agent reinforcement learning as a system-level architecture for interactive trajectory planning, emphasizing how learning agents can model, anticipate, and respond to other road users while remaining compatible with safety assurance, governance, and deployment realities. The discussion positions multi-agent reinforcement learning within the broader evolution of motion planning, decision-making, and safe control for autonomous vehicles. It then analyzes architectural choices such as centralized training with decentralized execution, opponent modeling, communication, value decomposition, and simulation-based curriculum design. The paper argues that technical performance alone cannot determine deployment viability. Instead, interactive planners must be evaluated through structural trade-offs involving robustness, fairness, interpretability, energy use, edge-cloud computation, liability, and regulatory oversight. Case illustrations from highway merging, urban intersections, and mixed traffic reveal that multi-agent reinforcement learning can improve coordination but also introduces new risks of nonstationarity, reward hacking, distributional shift, and opaque responsibility. The paper concludes that durable progress requires an integrated agenda combining safe reinforcement learning, formal runtime monitoring, standardized benchmarks, human factors research, and policy frameworks that treat autonomous vehicles as participants in public infrastructure rather than isolated commercial products.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
