Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
importance 6/10
ai assisted
Radar Systems and Signal Processing autonomous driving stage: research
Problem
Deep learning for automotive radar perception is spread across tasks without a holistic overview, and challenges like multi-path effects, uncertainty, and adverse weather remain.
Why it matters
Reliable radar perception is needed for autonomous driving safety.
What the AI did
The review surveys the deep radar perception stack, including datasets, labelling, augmentation, and tasks such as depth/velocity estimation, object detection, and sensor fusion.
Results
not stated in source
Impact
The paper summarises three overlooked challenges and presents some attempts to solve them.
Record
| Venue | Sensors |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Yi Zhou, Lulu Liu, Haocheng Zhao, Miguel López‐Benítez, Limin Yu, Yutao Yue |
| Citations | 154 (OpenAlex, at import time) |
| References | 247 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s22114208 |
| Open access | gold |
| Keywords | radar perception, deep learning, autonomous driving, 4D radar, sensor fusion, review |
Importance rating
6
significant — thousands to millions affected
Radar perception is needed for autonomous driving safety, a major industry; abstract gives no quantified scale or results.
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