arXiv:2605.12321v2 Announce Type: replace Abstract: Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoning and multi-agent coordination. These capabilities make them well suited for cooperative driving, where rule-based approaches struggle in complex and dynamic traffic environments. Intersection management remains especially challenging due to conflicting right-of-way demands, heterogeneous vehicle priorities, and vehicle-specific kinematic constraints that must be resolved in real time. However, e
LLMs are advancing rapidly, making their application to complex, real-time control problems like traffic management increasingly feasible and attractive over traditional rule-based systems.
This development indicates a significant step towards fully autonomous, AI-driven infrastructure, which can dramatically improve efficiency and safety in urban environments.
The reliance on pre-defined rules for autonomous traffic control begins to shift towards adaptive, cognitive arbitration by AI agents, offering more dynamic and optimized solutions.
- · AI software developers
- · Smart city infrastructure providers
- · Automotive industry
- · Urban commuters
- · Traditional traffic light manufacturers
- · Rule-based ITS developers
Traffic flow and congestion in urban areas could significantly improve.
Reduced fuel consumption and emissions due to smoother traffic, impacting urban air quality and sustainability efforts.
Enhanced urban development patterns as traffic management becomes highly optimized, potentially reducing the need for extensive road expansion projects.
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Read at arXiv cs.AI