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 unreal
Record details
Published: 30 June 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (30 June 2026), “Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling,” evidence record 386, https://ethics.ai/record/386 (originally published by arXiv).
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