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.
Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review
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REFNet++ aligns raw camera images and radar range-Doppler data into a shared bird's-eye polar view using variational encoders for multi-task vehicle detection and free space segmentation on the RADIal dataset.
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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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REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View
REFNet++ aligns raw camera images and radar range-Doppler data into a shared bird's-eye polar view using variational encoders for multi-task vehicle detection and free space segmentation on the RADIal dataset.