nuScenes provides the first public autonomous-driving dataset that includes synchronized 360-degree data from cameras, radars, and lidar together with 3D bounding-box annotations across 1000 scenes.
Probabilistic 3d multi-object tracking for autonomous driving
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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.
CANMOT replaces global noise covariances in KF-based 3D MOT with class-specific diagonal matrices (optionally object-frame) and shows gains in accuracy and ID-switch reduction on nuScenes plus improved but still inconsistent uncertainty calibration.
citing papers explorer
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nuScenes: A multimodal dataset for autonomous driving
nuScenes provides the first public autonomous-driving dataset that includes synchronized 360-degree data from cameras, radars, and lidar together with 3D bounding-box annotations across 1000 scenes.
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Radar-Informed 3D Multi-Object Tracking under Adverse Conditions
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.
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CANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving
CANMOT replaces global noise covariances in KF-based 3D MOT with class-specific diagonal matrices (optionally object-frame) and shows gains in accuracy and ID-switch reduction on nuScenes plus improved but still inconsistent uncertainty calibration.