Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
Summary
Proposes a unified risk map modeling framework for autonomous driving that integrates traffic flow and collision risks in partially observable environments, using spatiotemporal modeling and diffusion-based scenario generation. Outperforms state-of-the-art occlusion-aware baselines on the Waymo Open Motion Dataset.
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Paper page - Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
Source: https://huggingface.co/papers/2605.22189
Abstract
A unified risk map modeling framework addresses occlusion challenges in autonomous driving by integrating traffic flow and collision risks through spatiotemporal modeling and diffusion-based scenario generation.
Occlusion-aware predictionremains a critical challenge in autonomous driving due to the inherent uncertainty of unobserved regions. Existing approaches either overestimate risk based on reachable states or struggle to predict accurate trajectories under high occlusion uncertainty. To address these limitations, we propose a unifiedrisk map modelingand learning framework forpartially observable environments. Our method integrates traffic flow risk and collision risk throughspatiotemporal modeling, enabling fine-grained assessment of occlusion-induced hazards. To address the scarcity of scenarios involving occluded interactions, we introduce adiffusion-based scenario generationframework that produces realistic yet adversarial scenarios. We integrate the modeling and learning of a unified risk map into a framework that supportsrisk-aware planningunder partial observability. Experiments on the Waymo Open Motion Dataset show that our method significantly outperforms the state-of-the-art occlusion-aware baseline, improving minimum time-to-collision by 0.78 times and average time-to-collision by 1.67 times. The proposed framework offers a comprehensive and practical solution forrisk-aware planninginpartially observable environments.
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