CrashSight is a new infrastructure-focused benchmark showing that state-of-the-art vision-language models can describe crash scenes but fail at temporal and causal reasoning.
DriveQA: Passing the driving knowledge test
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
OpenVO estimates ego-motion from monocular dashcam footage with varying observation rates and uncalibrated cameras by encoding temporal dynamics in a two-frame regression framework and using 3D priors from foundation models, delivering over 20% gains and 46-92% lower errors on KITTI, nuScenes, and A
Alpamayo-R1 introduces a VLA model with a Chain of Causation dataset and multi-stage SFT-plus-RL training that reports 12% better planning accuracy and 35% fewer close encounters versus trajectory-only baselines in driving tasks.
citing papers explorer
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CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning
CrashSight is a new infrastructure-focused benchmark showing that state-of-the-art vision-language models can describe crash scenes but fail at temporal and causal reasoning.
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OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness
OpenVO estimates ego-motion from monocular dashcam footage with varying observation rates and uncalibrated cameras by encoding temporal dynamics in a two-frame regression framework and using 3D priors from foundation models, delivering over 20% gains and 46-92% lower errors on KITTI, nuScenes, and A
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Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
Alpamayo-R1 introduces a VLA model with a Chain of Causation dataset and multi-stage SFT-plus-RL training that reports 12% better planning accuracy and 35% fewer close encounters versus trajectory-only baselines in driving tasks.