Papers › Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving

Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving

11 Oct 2023arXiv:2310.07602archive 2025-07-28

Xinyu Zhang, Li Wang, Jian Chen, Cheng Fang, Lei Yang, Ziying Song, Guangqi Yang, Yichen Wang, Xiaofei Zhang, Jun Li, Zhiwei Li, Qingshan Yang, Zhenlin Zhang, Shuzhi Sam Ge

Radar has stronger adaptability in adverse scenarios for autonomous driving environmental perception compared to widely adopted cameras and LiDARs. Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception. However, due to the much higher noise than LiDAR, manufacturers choose different filtering strategies, resulting in an inverse ratio between noise level and point cloud density. There is still a lack of comparative analysis on which method is beneficial for deep learning-based perception algorithms in autonomous driving. One of the main reasons is that current datasets only adopt one type of 4D radar, making it difficult to compare different 4D radars in the same scene. Therefore, in this paper, we introduce a novel large-scale multi-modal dataset featuring, for the first time, two types of 4D radars captured simultaneously. This dataset enables further research into effective 4D radar perception algorithms.Our dataset consists of 151 consecutive series, most of which last 20 seconds and contain 10,007 meticulously synchronized and annotated frames. Moreover, our dataset captures a variety of challenging driving scenarios, including many road conditions, weather conditions, nighttime and daytime with different lighting intensities and periods. Our dataset annotates consecutive frames, which can be applied to 3D object detection and tracking, and also supports the study of multi-modal tasks. We experimentally validate our dataset, providing valuable results for studying different types of 4D radars. This dataset is released on https://github.com/adept-thu/Dual-Radar.

PaperPDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2310.07602")

Code

Syntology Ran 12 of 14 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 12 ran with no contract checked.

By repository: official repository: 14 samples from 1 repository, 12 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

adept-thu/dual-radar officialmentioned in papermentioned on GitHubpytorchApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

14 samples harvested; 12 ran; 0 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

12ran
2unverified

Licence: 0 of the 14 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from adept-thu/dual-radar. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

calib_to_matricies adept-thu/dual-radar/pcdet/datasets/dual_radar/dual_radar_utils.py official repository ran Apache-2.0 (permissive) · 12f2abeb508c73e6 · report
clean_data adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/eval1.py official repository ran Apache-2.0 (permissive) · 7a89af4cf8defd62 · report
cls_type_to_id adept-thu/dual-radar/pcdet/utils/object3d_dual_radar.py official repository ran fingerprinted Apache-2.0 (permissive) · 68d59bd1b88d6e20 · report
div_up adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/rotate_iou.py official repository ran fingerprinted Apache-2.0 (permissive) · 20fbfa2279e1ce4b · report
get_calib_from_file adept-thu/dual-radar/pcdet/utils/calibration_dual_radar.py official repository ran Apache-2.0 (permissive) · 35de1acbb1176d93 · report
get_image_index_str adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/kitti_common.py official repository ran fingerprinted Apache-2.0 (permissive) · 3415a0d1c950c1c7 · report
get_image_path adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/kitti_common.py official repository ran Apache-2.0 (permissive) · 8714a17f7bf0c1af · report
get_kitti_info_path adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/kitti_common.py official repository ran Apache-2.0 (permissive) · 9ba61b01f573b5e4 · report
get_objects_from_label adept-thu/dual-radar/pcdet/utils/object3d_dual_radar.py official repository ran Apache-2.0 (permissive) · 745fc20e6b5a2f30 · report
get_thresholds adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/eval1.py official repository ran Apache-2.0 (permissive) · ea171bd4f1407f14 · report
image_box_overlap adept-thu/dual-radar/pcdet/datasets/dual_radar/kitti_object_eval_python/eval1.py official repository ran fingerprinted Apache-2.0 (permissive) · 45f3e1c5466de4e5 · report
transform_annotations_to_kitti_format adept-thu/dual-radar/pcdet/datasets/dual_radar/dual_radar_utils.py official repository ran Apache-2.0 (permissive) · 43e0ea59c03fa906 · report
cfg_from_yaml_file adept-thu/dual-radar/pcdet/config.py official repository unverified Apache-2.0 (permissive) · 44db2351bcc0bffe · report
merge_new_config adept-thu/dual-radar/pcdet/config.py official repository unverified Apache-2.0 (permissive) · 50e8e8cdfc5129f0 · report

Tasks

3D Object DetectionAutonomous DrivingObject Detectionobject-detection

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections