ReviewSensors (Basel, Switzerland)2022
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Smart Animal Welfare: A Review of Sensing Technologies, Deployment Challenges, and AI-Driven Insights.Sensors (Basel, Switzerland) · 2026Review
- Physics-Guided Multi-GSO Spectral Filtering for Degradation-Aware Automotive Radar Point-Cloud Detection.Sensors (Basel, Switzerland) · 2026Article
- Non-Line-of-Sight Perception Method for Autonomous Haul Trucks in Open-Pit Mines Based on 4D mmWave Radar and LiDAR Fusion.Sensors (Basel, Switzerland) · 2026Article
- Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection.Scientific reports · 2025Article
- Development of Low-Cost Single-Chip Automotive 4D Millimeter-Wave Radar.Sensors (Basel, Switzerland) · 2025Article
- Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autononous Driving.Scientific data · 2025Article
- Improved DeepSORT-Based Object Tracking in Foggy Weather for AVs Using Sematic Labels and Fused Appearance Feature Network.Sensors (Basel, Switzerland) · 2024Article
- A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds.Sensors (Basel, Switzerland) · 2024Review
- Camera-Radar Fusion with Modality Interaction and Radar Gaussian Expansion for 3D Object Detection.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Deep Camera-Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions.Sensors (Basel, Switzerland) · 2023Article
- Current Non-Contact Road Surface Condition Detection Schemes and Technical Challenges.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.