MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.
CooperRisk: A driving risk quantification pipeline with multi-agent cooperative perception and prediction
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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.
citing papers explorer
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MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems
MDrive benchmark shows multi-agent cooperative driving systems generally outperform single-agent ones in closed-loop settings but perception sharing does not always improve planning and negotiation can harm performance in complex traffic.
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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.