The paper proposes a unified risk map modeling and learning framework integrated with diffusion-based adversarial scenario generation for risk-aware planning in partially observable autonomous driving, demonstrating improved time-to-collision metrics on the Waymo Open Motion Dataset.
Generating efficient behaviour with predictive visibility risk for scenarios with occlusions
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C-CoT applies VLMs to autonomous driving via five-stage reasoning with a meta-action tree for counterfactuals, yielding 81.9% risk recall, 3.52% collision rate, and 1.98 m L2 error on a new dataset.
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Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
The paper proposes a unified risk map modeling and learning framework integrated with diffusion-based adversarial scenario generation for risk-aware planning in partially observable autonomous driving, demonstrating improved time-to-collision metrics on the Waymo Open Motion Dataset.
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C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Driving
C-CoT applies VLMs to autonomous driving via five-stage reasoning with a meta-action tree for counterfactuals, yielding 81.9% risk recall, 3.52% collision rate, and 1.98 m L2 error on a new dataset.