RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification

• URL: http://arxiv.org/abs/2608.21414v1
• Date: Wed, 12 Aug 2026 02:52:04 GMT
• Title: RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification
• Abstract summary: RiskWorld is an object-centric latent world model that identifies risk from the imagined evolution of each candidate relative to the ego vehicle.<n>It decodes the rollout into object-level risk scores, supported by auxiliary future-relation and temporal-risk predictions.<n>On RiskBench, RiskWorld achieves the best overall F1 of 63.0% and the lowest false-alarm rate of 2.1%.
• Score: 30.32162964871242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous driving risk identification aims to determine which observed object is likely to become safety-critical to the ego vehicle. Existing approaches typically predict scene-level accidents, infer risk objects indirectly from ego behavior, or apply geometric checks after trajectory forecasting, without directly using predicted ego--object relations for risk-source localization. We propose RiskWorld, an object-centric latent world model that identifies risk from the imagined evolution of each candidate relative to the ego vehicle. RiskWorld combines pretrained predictive video representations with structured ego--object histories, contextualizes observed interactions, and rolls relation-aware object states into the future using RSSM-style latent dynamics. It decodes the rollout into object-level risk scores, supported by auxiliary future-relation and temporal-risk predictions. Inference uses only observations up to the current time, while logged futures provide training supervision. On RiskBench, RiskWorld achieves the best overall F1 of 63.0\% and the lowest false-alarm rate of 2.1\%. Further analyses show that the learned rollout captures the evolution of object-level risk before critical events, while RiskWorld's selections preserve planning-critical information under filtered observation.

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