A simulator-trained neural semantic field plus a hierarchical risk tree estimates per-agent collision risk and time-to-collision from monocular video, with foundation-model features used to close the sim-to-real gap without retraining.
Uncertainty-based traffic accident anticipation with spatio-temporal relational learning
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NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
A simulator-trained neural semantic field plus a hierarchical risk tree estimates per-agent collision risk and time-to-collision from monocular video, with foundation-model features used to close the sim-to-real gap without retraining.