TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

Article Robotics

Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

Andras Palffy et al.

Summary: In this study, a state-of-the-art object detector (PointPillars) is applied to 3+1D radar data. The benefits of additional elevation information, together with Doppler, radar cross section, and temporal accumulation, are explored in multi-class road user detection. Results show that object detection performance on 64-layer LiDAR data outperforms that on 3+1D radar data, but the gap can be reduced by adding elevation information and integrating successive radar scans. The VoD dataset, a valuable experimental dataset, is provided for scientific benchmarking.

IEEE ROBOTICS AND AUTOMATION LETTERS (2022)

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Review Engineering, Electrical & Electronic

Multi-Object Detection and Tracking, Based on DNN, for Autonomous Vehicles: A Review

Ratheesh Ravindran et al.

Summary: This paper discusses the challenges of multi-object detection and tracking in autonomous vehicles in diverse driving situations, and proposes solutions involving various sensors and DNN technology. Research shows that fusion of sensor data and DNN technology could be a key to addressing this challenge.

IEEE SENSORS JOURNAL (2021)

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Article Telecommunications

V2I based environment perception for autonomous vehicles at intersections

Xuting Duan et al.

China Communications (2021)

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Proceedings Paper Automation & Control Systems

RADIATE A Radar Dataset for Automotive Perception in Bad Weather

Marcel Sheeny et al.

Summary: The study introduces a radar dataset named RADIATE to facilitate research on radar sensing for safe autonomous driving. The dataset includes annotated radar images captured in adverse weather conditions, aimed at object detection, tracking, and scene understanding.

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) (2021)

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Proceedings Paper Automation & Control Systems