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.
Cobevt: Cooperative bird’s eye view semantic segmentation with sparse transformers
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A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios while evading defenses.
SHLE is a stereo pipeline with device tracking and temporal depth filtering that estimates height limits with under 10cm average error at 70m distance on the new Disparity Height dataset.
CooperDrive augments autonomous vehicle perception by sharing object-level data from BEV features, enabling earlier conflict anticipation and safer planning with 90 kbps bandwidth and 89 ms latency in real-world NLOS tests.
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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From Stealthy Data Fabrication to Unsafe Driving: Realistic Scenario Attacks on Collaborative Perception
A new online attack framework manipulates object poses in shared CAV perception data below detection thresholds, propagating errors to cause unsafe trajectory predictions and behaviors in up to 50% of tested scenarios while evading defenses.
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SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation
SHLE is a stereo pipeline with device tracking and temporal depth filtering that estimates height limits with under 10cm average error at 70m distance on the new Disparity Height dataset.
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CooperDrive: Enhancing Driving Decisions Through Cooperative Perception
CooperDrive augments autonomous vehicle perception by sharing object-level data from BEV features, enabling earlier conflict anticipation and safer planning with 90 kbps bandwidth and 89 ms latency in real-world NLOS tests.