Towards deep radar perception for autonomous driving: Datasets, methods, and challenges
With recent developments, the performance of automotive radar has improved significantly.
The next generation of 4D radar can achieve imaging capability in the form of high …
The next generation of 4D radar can achieve imaging capability in the form of high …
Sensing and machine learning for automotive perception: A review
Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …
Simple-bev: What really matters for multi-sensor bev perception?
Building 3D perception systems for autonomous vehicles that do not rely on high-density
LiDAR is a critical research problem because of the expense of LiDAR systems compared to …
LiDAR is a critical research problem because of the expense of LiDAR systems compared to …
SMURF: Spatial multi-representation fusion for 3D object detection with 4D imaging radar
The 4D millimeter-Wave (mmWave) radar is a promising technology for vehicle sensing due
to its cost-effectiveness and operability in adverse weather conditions. However, the …
to its cost-effectiveness and operability in adverse weather conditions. However, the …
Radar-camera fusion for object detection and semantic segmentation in autonomous driving: A comprehensive review
Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …
Proxyformer: Proxy alignment assisted point cloud completion with missing part sensitive transformer
Problems such as equipment defects or limited viewpoints will lead the captured point
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …
clouds to be incomplete. Therefore, recovering the complete point clouds from the partial …
Sparse instance conditioned multimodal trajectory prediction
Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …
multimodality of the future trajectory. Most existing methods predict multimodal trajectories …
RaLiBEV: Radar and LiDAR BEV fusion learning for anchor box free object detection systems
In autonomous driving, LiDAR and radar are crucial for environmental perception. LiDAR
offers precise 3D spatial sensing information but struggles in adverse weather like fog …
offers precise 3D spatial sensing information but struggles in adverse weather like fog …
Azimuth super-resolution for fmcw radar in autonomous driving
We tackle the task of Azimuth (angular dimension) super-resolution for Frequency
Modulated Continuous Wave (FMCW) multiple-input multiple-output (MIMO) radar. FMCW …
Modulated Continuous Wave (FMCW) multiple-input multiple-output (MIMO) radar. FMCW …
Which framework is suitable for online 3d multi-object tracking for autonomous driving with automotive 4d imaging radar?
Online 3D multi-object tracking (MOT) has recently received significant research interests
due to the expanding demand of 3D perception in advanced driver assistance systems …
due to the expanding demand of 3D perception in advanced driver assistance systems …