[{"@type":"PropertyValue","name":"Objective","value":"To build a multi-sensor annotated dataset in Japan for R&D scenarios related to autonomous driving, ADAS, environmental perception, object tracking, and high-definition (HD) maps."},{"@type":"PropertyValue","name":"Data collection equipment","value":"Collected via a real vehicle platform in Japanese road environments, with sensors including LiDAR, RGB cameras, RTK/GNSS, IMU, and CAN bus (wheel speed)."},{"@type":"PropertyValue","name":"Collection scenarios","value":"Urban roads and their adjacent coastal road scenes in Japan, primarily under real daytime traffic conditions and mainly on sunny days."},{"@type":"PropertyValue","name":"Collection content","value":"LiDAR point clouds, 6-view synchronized RGB images, RTK/GNSS, IMU, and vehicle speed information."},{"@type":"PropertyValue","name":"Annotation content","value":"2D traffic sign annotation, 3D object tracking annotation, and 4D lane line annotation."},{"@type":"PropertyValue","name":"Application scenarios","value":"Can be used for perception model training, object tracking, lane recognition, map construction, algorithm verification, and other scenarios."},{"@type":"PropertyValue","name":"2D information","value":"189 clips / 2,301 frames / 4,893 boxes"},{"@type":"PropertyValue","name":"3D information","value":"258 clips / 10,320 frames / 195,782 boxes"},{"@type":"PropertyValue","name":"4D information","value":"251 clips / 132.373kilometer"}]
{"id":2184,"datatype":"1","titleimg":"https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp","type1":"147","type1str":null,"type2":"151","type2str":null,"dataname":"Japan Autonomous Driving Dataset with Multi-Sensor Annotations for ADAS & Autonomous Vehicles","datazy":[{"title":"Objective","content":"To build a multi-sensor annotated dataset in Japan for R&D scenarios related to autonomous driving, ADAS, environmental perception, object tracking, and high-definition (HD) maps."},{"title":"Data collection equipment","content":"Collected via a real vehicle platform in Japanese road environments, with sensors including LiDAR, RGB cameras, RTK/GNSS, IMU, and CAN bus (wheel speed)."},{"title":"Collection scenarios","content":"Urban roads and their adjacent coastal road scenes in Japan, primarily under real daytime traffic conditions and mainly on sunny days."},{"title":"Collection content","content":"LiDAR point clouds, 6-view synchronized RGB images, RTK/GNSS, IMU, and vehicle speed information."},{"title":"Annotation content","content":"2D traffic sign annotation, 3D object tracking annotation, and 4D lane line annotation."},{"title":"Application scenarios","content":"Can be used for perception model training, object tracking, lane recognition, map construction, algorithm verification, and other scenarios."},{"title":"2D information","content":"189 clips / 2,301 frames / 4,893 boxes"},{"title":"3D information","content":"258 clips / 10,320 frames / 195,782 boxes"},{"title":"4D information","content":"251 clips / 132.373kilometer"}],"datatag":"Autonomous Driving,Multi-Sensor,LiDAR,Point Cloud,Multi-view Images,Traffic Sign,3D Object Detection,Tracking,4D Lane Annotation,HD Map,Japan Road Dataset","technologydoc":null,"downurl":null,"datainfo":null,"standard":null,"dataylurl":null,"flag":null,"publishtime":null,"createby":null,"createtime":null,"ext1":null,"samplestoreloc":null,"hosturl":null,"datasize":null,"industryPlan":null,"keyInformation":null,"samplePresentation":[],"officialSummary":"This dataset contains high-precision multi-sensor autonomous driving data collected from real vehicles operating in Japan. The dataset supports perception model development, sensor fusion, 3D object detection, multi-object tracking, lane detection, HD map construction, localization, and algorithm validation. This dataset is well suited for autonomous vehicle perception and automotive AI model training.","dataexampl":null,"datakeyword":["autonomous driving dataset","autonomous vehicle dataset","adas dataset","autonomous driving data","multi sensor dataset","sensor fusion dataset","lidar camera dataset","multimodal driving dataset"],"isDelete":null,"ids":null,"idsList":null,"datasetCode":null,"productStatus":null,"tagTypeEn":"Task Type,Modalities","tagTypeZh":null,"website":null,"samplePresentationList":null,"datazyList":null,"keyInformationList":null,"dataexamplList":null,"bgimg":null,"datazyScriptList":null,"datakeywordListString":null,"sourceShowPage":"computer","dataShowType":"[{\"code\":\"2\",\"language\":\"EN,JP\"}]","productNameEn":"Japan Autonomous Driving Multi-Sensor Annotated Dataset","BGimg":"","voiceBg":["/shujutang/static/image/comm/audio_bg.webp","/shujutang/static/image/comm/audio_bg2.webp","/shujutang/static/image/comm/audio_bg3.webp","/shujutang/static/image/comm/audio_bg4.webp","/shujutang/static/image/comm/audio_bg5.webp"]}
https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp
[]
Japan Autonomous Driving Dataset with Multi-Sensor Annotations for ADAS & Autonomous Vehicles
autonomous driving dataset
autonomous vehicle dataset
adas dataset
autonomous driving data
multi sensor dataset
sensor fusion dataset
lidar camera dataset
multimodal driving dataset
This dataset contains high-precision multi-sensor autonomous driving data collected from real vehicles operating in Japan. The dataset supports perception model development, sensor fusion, 3D object detection, multi-object tracking, lane detection, HD map construction, localization, and algorithm validation. This dataset is well suited for autonomous vehicle perception and automotive AI model training.
This is a paid dataset licensed for commercial use. Ready-made datasets are available for immediate integration into AI projects.
![Specifications]()
Specifications
Objective
To build a multi-sensor annotated dataset in Japan for R&D scenarios related to autonomous driving, ADAS, environmental perception, object tracking, and high-definition (HD) maps.
Data collection equipment
Collected via a real vehicle platform in Japanese road environments, with sensors including LiDAR, RGB cameras, RTK/GNSS, IMU, and CAN bus (wheel speed).
Collection scenarios
Urban roads and their adjacent coastal road scenes in Japan, primarily under real daytime traffic conditions and mainly on sunny days.
Collection content
LiDAR point clouds, 6-view synchronized RGB images, RTK/GNSS, IMU, and vehicle speed information.
Annotation content
2D traffic sign annotation, 3D object tracking annotation, and 4D lane line annotation.
Application scenarios
Can be used for perception model training, object tracking, lane recognition, map construction, algorithm verification, and other scenarios.
2D information
189 clips / 2,301 frames / 4,893 boxes
3D information
258 clips / 10,320 frames / 195,782 boxes
4D information
251 clips / 132.373kilometer
![Sample]()
Sample
![Recommended Datasets]()
Recommended Dataset
Tell Us Your Special Needs
Dataset FAQs

What types of computer vision applications can Nexdata’s datasets support?

Nexdata’s computer vision datasets support a wide range of AI applications, including image classification, object detection, image segmentation, facial and human-related recognition, scene understanding, autonomous driving, and other visual perception tasks. Depending on the dataset, data may include images, videos, bounding boxes, polygons, keypoints, segmentation masks, text annotations, and other structured labels.

Can Nexdata customize Computer Vision datasets based on our specific requirements?

Yes. If our off-the-shelf Computer Vision datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, annotation, and curation services. We can customize data based on your target objects, environments, scenarios, camera specifications, geographic locations, data volume, annotation formats, and quality standards to support specific model training and evaluation needs.

How does Nexdata ensure the quality and scalability of its Computer Vision datasets?

Nexdata applies multi-stage quality control throughout data collection, annotation, validation, and delivery. Depending on project requirements, we can implement customized annotation guidelines, multi-level reviews, consistency checks, and quality sampling to ensure dataset accuracy and consistency. Our data collection and processing capabilities can also be scaled to support large-volume Computer Vision projects.
39b5b157-b33a-4933-a831-90a11c2f38fe