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
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
fields
cs.CV 3verdicts
UNVERDICTED 3roles
dataset 1polarities
use dataset 1representative citing papers
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.
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.
citing papers explorer
-
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.
-
Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)
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.
-
Trajectory-Aware Adaptive Inference in Object Detection Models
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.