Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception
World models, generative AI systems that simulate how environments evolve, are transforming autonomous driving, yet all existing approaches adopt an ego-vehicle perspective, leaving the infrastructure viewpoint unexplored. We argue that infrastructure-centric world models offer a fundamentally complementary capability: the bird's-eye, multi-sensor, persistent viewpoint that roadside systems uniquely possess. Central to our thesis is a spatio-temporal complementarity: fixed roadside sensors excel at temporal depth, accumulating long-term behavioral distributions including rare safety-critical events, while vehicle-borne sensors excel at spatial breadth, sampling diverse scenes across large...
| Authors | Siyuan Meng, Chengbo Ai |
|---|---|
| Primary category | cs.CV |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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