Pith. sign in

REVIEW 2 cited by

4D Millimeter-Wave Radar in Autonomous Driving: A Survey

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2306.04242 v4 pith:PSF2AKPH submitted 2023-06-07 eess.SP cs.RO

Zeyu Han , Jiahao Wang , Zikun Xu , Shuocheng Yang , Lei He , Shaobing Xu , Jianqiang Wang , Keqiang Li This is my paper
classification eess.SPcs.RO
keywords radarautonomousdrivingmmwavesurveyapplicationelevationfuture
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The 4D millimeter-wave (mmWave) radar, proficient in measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest within the autonomous driving community. This is attributed to its robustness in extreme environments and the velocity and elevation measurement capabilities. However, despite the rapid advancement in research related to its sensing theory and application, there is a conspicuous absence of comprehensive surveys on the subject of 4D mmWave radar. In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving. Initially, the paper provides reviews on the theoretical background and progress of 4D mmWave radars, encompassing aspects such as the signal processing workflow, resolution improvement approaches, and extrinsic calibration process. Learning-based radar data quality improvement methods are present following. Then, this paper introduces relevant datasets and application algorithms in autonomous driving perception, localization and mapping tasks. Finally, this paper concludes by forecasting future trends in the realm of 4D mmWave radar in autonomous driving. To the best of our knowledge, this is the first survey specifically dedicated to the 4D mmWave radar in autonomous driving.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ObsDriveBench: Benchmarking Multimodal Understanding under Adverse Weather with Observability Awareness

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Real adverse-weather camera–LiDAR–radar MCQs expose VLM failures from observability estimation through spatial grounding to trajectory safety, partially mitigated by SFT+RL.

  2. Structure-Aware Radar-Camera Depth Estimation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A radar-camera depth estimation framework that uses monocular depth to define adaptive regions of interest for radar points, improving dense metric depth on nuScenes.

Pith tools