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.
Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment
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CAVs use a Raft-inspired voting consensus algorithm to agree on passing order at unsignalized intersections in mixed traffic, reaching consensus in 30-40 ms on average with a vision-based fallback.
citing papers explorer
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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.
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A Distributed Consensus Algorithm for Prioritizing Autonomous Vehicle Passing at Unsignalized Intersections under Mixed Traffic
CAVs use a Raft-inspired voting consensus algorithm to agree on passing order at unsignalized intersections in mixed traffic, reaching consensus in 30-40 ms on average with a vision-based fallback.