DSIP: A Dynamic Coordination Planner for Signal-Free Intersections using Diffusion-Model-Based Multi-Agent Motion Planning
arXiv:2606.30694v1 Announce Type: cross Abstract: Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand. With the emergence of connected and automated vehicles (CAVs), trajectory-level coordination has emerged as a high-potential strategy to augment or transcend conventional phase-based management. This paper proposes DSIP (Diffusion-model-based Signal-free Intersection Planner), a multi-agent motion planning framework driven by a generative diffusion process.
The proliferation of connected and automated vehicles (CAVs) coupled with advancements in generative AI models like diffusion models enables more sophisticated and dynamic traffic management solutions.
This development indicates a tangible path towards more autonomous and efficient urban infrastructure, directly impacting traffic flow, energy consumption, and the economic efficiency of transportation systems.
The conventional phase-based traffic light system could be gradually replaced or augmented by dynamic, AI-driven coordination, leading to smoother traffic and reduced delays at intersections.
- · Smart city technology providers
- · Automotive manufacturers (CAVs)
- · Urban planners
- · Logistics companies
- · Traditional traffic signal manufacturers
- · Outdated urban infrastructure maintenance services
Reduced urban congestion and travel times.
Potential for optimized delivery routes and reduced carbon emissions from idling vehicles.
Enhanced urban liveability and increased efficiency for emergency services.
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Read at arXiv cs.AI