Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling
A local-to-global context mismatch arises when autoregressive traffic simulators trained on ego-centric driving logs are deployed in globally observable closed-loop environments. In such logs, the ego vehicle has rich local observations, while surrounding agents are only partially observed due to perception limits and occlusions. As a result, simulators may learn incomplete context--action mappings that remain hidden in log-based training but emerge during closed-loop rollouts, leading to unrealistic behaviors such as abnormal stops, unsafe interactions, and rule violations. We propose CRAFT, a Contextual pReference Alignment Framework for Traffic Simulation, to mitigate this mismatch via...
| Authors | Ziyan Wang, Tan Xiang, Peng Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 30 June 2026 |
| Last updated at source | 30 June 2026 |
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