OmniTraffic: A Controllable Generation Pipeline and Benchmark for Spatio-Temporal Traffic Reasoning
arXiv:2606.15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics. However, existing traffic-oriented multimodal benchmarks largely emphasize passive visual recognition or isolated video understanding, offering limited support for evaluating structure-aware traffic reasoning under controlled conditions. We introduce OmniTraffic, a controllable generation pipeline and benchmark for spatio-temporal traffic reasoning. Built around 12 real-wo
The increasing complexity of AI models and real-world autonomy demands more sophisticated and controllable benchmarks for spatio-temporal reasoning, moving beyond passive recognition.
This development addresses a critical gap in evaluating AI systems for complex, dynamic environments like traffic, which is essential for advancing autonomous systems and smart city infrastructure.
The introduction of OmniTraffic enables more rigorous and structured testing of AI models for traffic scene understanding, shifting from isolated visual recognition to integrated spatio-temporal reasoning.
- · Autonomous vehicle developers
- · Smart city solution providers
- · AI researchers in computer vision and control systems
- · Simulation and synthetic data platforms
- · Companies relying solely on passive vision models for traffic understanding
- · Benchmark developers focused only on isolated visual tasks
Improved training and evaluation of AI models for traffic management and autonomous driving.
Faster development and deployment of more robust and reliable autonomous systems in complex traffic scenarios.
Potential for new regulatory standards and certification processes based on comprehensive spatio-temporal reasoning benchmarks.
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Read at arXiv cs.AI