RadarMOT improves 3D multi-object tracking accuracy by using radar point clouds as direct observations to refine states and recover missed objects, achieving 12.7% higher AMOTA at long range and up to 10.3% in adverse weather on the MAN-TruckScenes dataset.
nuScenes: A Multimodal Dataset for Autonomous Driving
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DTPQA is a new VQA benchmark consisting of synthetic and real-world traffic images with distance annotations to isolate and measure VLM perception capabilities for driving decisions.
SPARK applies keypoint detection with YOLO models to monocular images for low-latency 3D pose estimation of racing opponents, claiming better accuracy and speed than prior camera methods on real racing data.
Introduces an early-exit mechanism in YOLOv8 that uses inter-vessel distance and closing speed from trajectories to adapt computation depth per frame in maritime scenes.
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