227 citations · 426 across the 6 of their papers we have counts for
6 papers
Vision meets mmWave Radar: 3D Object Perception Benchmark for Autonomous Driving
Yizhou Wang, Jen-Hao Cheng, Jui-Te Huang +8
Sensor fusion is crucial for an accurate and robust perception system on autonomous vehicles. Most existing datasets and perception solutions focus on fusing cameras and LiDAR. How…
Learning to Detect Open Carry and Concealed Object with 77GHz Radar
Xiangyu Gao, Hui Liu, Sumit Roy +3
Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on…
Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment
Yizhou Wang, Gaoang Wang, Hung-Min Hsu +2
Radar has long been a common sensor on autonomous vehicles for obstacle ranging and speed estimation. However, as a robust sensor to all-weather conditions, radar's capability has…
RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization
Yizhou Wang, Zhongyu Jiang, Yudong Li +3
Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly used se…
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
Xiangyu Gao, Guanbin Xing, Sumit Roy +1
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object d…
RODNet: Radar Object Detection Using Cross-Modal Supervision
Yizhou Wang, Zhongyu Jiang, Xiangyu Gao +3
Radar is usually more robust than the camera in severe driving scenarios, e.g., weak/strong lighting and bad weather. However, unlike RGB images captured by a camera, the semantic…