{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T08:04:13Z","timestamp":1777363453719,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:00:00Z","timestamp":1766707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100018502","name":"Kanazawa University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018502","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. ITS Res."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13177-025-00598-2","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T16:49:20Z","timestamp":1766767760000},"page":"74-84","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time 3D Object Detection with Distance-Aware Hybrid Point Cloud Representation toward Long Range Detection"],"prefix":"10.1007","volume":"24","author":[{"given":"Keigo","family":"Hariya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroki","family":"Inoshita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukiya","family":"Fukuda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keisuke","family":"Yoneda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Suganuma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,26]]},"reference":[{"key":"598_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122836","volume":"242","author":"J Zhao","year":"2024","unstructured":"Zhao, J., et al.: Autonomous driving system: A comprehensive survey. Expert Systems with Applications 242, 122836 (2024)","journal-title":"Expert Systems with Applications"},{"issue":"9","key":"598_CR2","doi-asserted-by":"publisher","first-page":"3545","DOI":"10.3390\/s22093545","volume":"22","author":"R Yanase","year":"2022","unstructured":"Yanase, R., et al.: LiDAR-and radar-based robust vehicle localization with confidence Estimation of matching results. Sensors. 22(9), 3545 (2022)","journal-title":"Sensors"},{"key":"598_CR3","doi-asserted-by":"crossref","unstructured":"Lang, A.H., et al.: Pointpillars: Fast encoders for object detection from point clouds, in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 12697\u201312705. (2019)","DOI":"10.1109\/CVPR.2019.01298"},{"key":"598_CR4","doi-asserted-by":"crossref","unstructured":"Yin, T., et al.: Center-based 3d object detection and tracking, in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 11784\u201311793. (2021)","DOI":"10.1109\/CVPR46437.2021.01161"},{"key":"598_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Focalformer3d: focusing on hard instance for 3d object detection, in Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8394\u20138405. (2023)","DOI":"10.1109\/ICCV51070.2023.00771"},{"key":"598_CR6","doi-asserted-by":"crossref","unstructured":"Zhou, Z., et al.: CenterFormer: Center-Based Transformer for 3D Object Detection, in Computer Vision \u2013 ECCV 2022.\u00a013698","DOI":"10.1007\/978-3-031-19839-7_29"},{"key":"598_CR7","doi-asserted-by":"crossref","unstructured":"Fukuda, Y., et al.: Dense Traversability Estimation System for Extreme Environments, in 2023 IEEE Intelligent Vehicles Symposium (IV), IEEE, pp. 1\u20136. (2023)","DOI":"10.1109\/IV55152.2023.10186556"},{"issue":"4","key":"598_CR8","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/s10015-024-00973-4","volume":"29","author":"Y Bok","year":"2024","unstructured":"Bok, Y., et al.: Probabilistic model for high-level intention estimation and trajectory prediction in urban environments. Artif. Life Robot. 29(4), 557\u2013566 (2024)","journal-title":"Artif. Life Robot."},{"key":"598_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2024.104630","volume":"174","author":"M Reda","year":"2024","unstructured":"Reda, M., et al.: Path planning algorithms in the autonomous driving system: A comprehensive review. Robotics and Autonomous Systems 174, 104630 (2024)","journal-title":"Robotics and Autonomous Systems"},{"issue":"6","key":"598_CR10","doi-asserted-by":"publisher","first-page":"3692","DOI":"10.1109\/TIV.2023.3274536","volume":"8","author":"S Teng","year":"2023","unstructured":"Teng, S., et al.: Motion planning for autonomous driving: The state of the art and future perspectives. IEEE Trans. Intell. Veh. 8(6), 3692\u20133711 (2023)","journal-title":"IEEE Trans. Intell. Veh."},{"issue":"4","key":"598_CR11","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.iatssr.2019.11.005","volume":"43","author":"K Yoneda","year":"2019","unstructured":"Yoneda, K., et al.: Automated driving recognition technologies for adverse weather conditions. IATSS Research 43(4), 253\u2013262 (2019)","journal-title":"IATSS Research"},{"key":"598_CR12","doi-asserted-by":"crossref","unstructured":"Zhu, M., et al.: A systematic survey of Transformer-Based 3D object detection for autonomous driving: Methods, challenges and trends. Drones, 8, 8, Art. 8, Aug. 2024.","DOI":"10.3390\/drones8080412"},{"key":"598_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2024.1212070","author":"M Contreras","year":"2024","unstructured":"Contreras, M., et al.: A survey on 3D object detection in real time for autonomous driving. Front. Robot. AI (2024). https:\/\/doi.org\/10.3389\/frobt.2024.1212070","journal-title":"Front. Robot. AI"},{"issue":"5","key":"598_CR14","doi-asserted-by":"publisher","first-page":"3537","DOI":"10.1109\/TPAMI.2023.3346386","volume":"46","author":"X Ma","year":"2023","unstructured":"Ma, X., et al.: 3d object detection from images for autonomous driving: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 46(5), 3537\u20133556 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"598_CR15","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1007\/s11263-023-01790-1","volume":"131","author":"J Mao","year":"2023","unstructured":"Mao, J., et al.: 3D Object Detection for Autonomous Driving: A Comprehensive Survey. Int. J. Comput. Vis. 131(8), 1909\u20131963 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"598_CR16","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Voxelnet: End-to-end learning for point cloud based 3d object detection, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4490\u20134499. (2018)","DOI":"10.1109\/CVPR.2018.00472"},{"issue":"10","key":"598_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/s18103337","volume":"18","author":"Y Yan","year":"2018","unstructured":"Yan, Y., et al.: Second: Sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)","journal-title":"Sensors"},{"key":"598_CR18","unstructured":"Qi, C.R., et al.: Pointnet: Deep learning on point sets for 3d classification and segmentation, in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 652\u2013660. (2017)"},{"key":"598_CR19","unstructured":"Qi, C.R., et al.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv. Neural Inf. Process. Syst.\u00a030.\u00a0(2017)"},{"key":"598_CR20","unstructured":"Yu, Z., et al.: PolarBEVDet: Exploring Polar representation for Multi-View 3D object detection in Bird\u2019s-Eye-View. Dec 04, 2024, arXiv: arXiv:2408.16200."},{"key":"598_CR21","doi-asserted-by":"crossref","unstructured":"Gupta, S., et al.: Far3det: Towards far-field 3d detection, in Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 692\u2013701. (2023)","DOI":"10.1109\/WACV56688.2023.00076"},{"key":"598_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, X., et al.: Far3d: Expanding the horizon for surround-view 3d object detection, in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 2561\u20132569. (2024)","DOI":"10.1609\/aaai.v38i3.28033"},{"key":"598_CR23","unstructured":"Huang, J., et al.: BEVDet: High-performance Multi-camera 3D object detection in Bird-Eye-View. Jun 16, (2022). arXiv: arXiv:2112.11790."},{"key":"598_CR24","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Bevdepth: Acquisition of reliable depth for multi-view 3d object detection, in Proceedings of the AAAI conference on artificial intelligence, pp. 1477\u20131485. (2023)","DOI":"10.1609\/aaai.v37i2.25233"},{"issue":"20","key":"598_CR25","doi-asserted-by":"publisher","first-page":"8367","DOI":"10.3390\/s23208367","volume":"23","author":"K Hariya","year":"2023","unstructured":"Hariya, K., et al.: ExistenceMap-PointPillars: A multifusion network for robust 3D object detection with object existence probability map. Sensors. 23(20), 8367 (2023)","journal-title":"Sensors"},{"key":"598_CR26","unstructured":"Hu, H., et al.: EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection, Aug. 30, arXiv: arXiv:2303.17895. (2023)"},{"key":"598_CR27","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection, in Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 17182\u201317191. (2022)","DOI":"10.1109\/CVPR52688.2022.01667"},{"key":"598_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Bevfusion: Multi-task multi-sensor fusion with unified bird\u2019s-eye view representation, in., IEEE international conference on robotics and automation (ICRA), IEEE, 2023, pp. 2774\u20132781. (2023)","DOI":"10.1109\/ICRA48891.2023.10160968"},{"issue":"1","key":"598_CR29","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1109\/TIV.2023.3322729","volume":"9","author":"J Liu","year":"2023","unstructured":"Liu, J., et al.: SMURF: Spatial multi-representation fusion for 3D object detection with 4D imaging radar. IEEE Trans. Intell. Veh. 9(1), 799\u2013812 (2023)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"598_CR30","doi-asserted-by":"crossref","unstructured":"Yoneda, K., et al.: Fast 3D Object Detection for 4D Imaging Radar integrating Image Map features using Semi-supervised Learning, in 2024 IEEE Intelligent Vehicles Symposium (IV), IEEE, pp. 1367\u20131372. (2024)","DOI":"10.1109\/IV55156.2024.10588420"},{"issue":"1","key":"598_CR31","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/TIV.2023.3321240","volume":"9","author":"W Xiong","year":"2023","unstructured":"Xiong, W., et al.: LXL: LiDAR excluded lean 3D object detection with 4D imaging radar and camera fusion. IEEE Trans. Intell. Veh. 9(1), 79\u201392 (2023)","journal-title":"IEEE Trans. Intell. Veh."},{"key":"598_CR32","doi-asserted-by":"crossref","unstructured":"Chadwick, S., et al.: Distant vehicle detection using radar and vision, in 2019 International Conference on Robotics and Automation (ICRA), Ieee, pp. 8311\u20138317. (2019)","DOI":"10.1109\/ICRA.2019.8794312"},{"key":"598_CR33","doi-asserted-by":"crossref","unstructured":"Khoche, A., et al.: Towards long-range 3d object detection for autonomous vehicles, in 2024 IEEE Intelligent Vehicles Symposium (IV), IEEE, pp. 2206\u20132212. (2024)","DOI":"10.1109\/IV55156.2024.10588513"},{"key":"598_CR34","first-page":"26871","volume":"34","author":"Q Chen","year":"2021","unstructured":"Chen, Q., et al.: Polarstream: Streaming object detection and segmentation with Polar pillars. Adv. Neural Inf. Process. Syst. 34, 26871\u201326883 (2021)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"598_CR35","doi-asserted-by":"crossref","unstructured":"Nie, M., et al.: Partner: Level up the polar representation for lidar 3d object detection, in Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3801\u20133813. (2023)","DOI":"10.1109\/ICCV51070.2023.00352"},{"key":"598_CR36","doi-asserted-by":"crossref","unstructured":"Song, W., et al.: Jan., DGPolarNet: Dynamic graph Convolution network for lidar point cloud semantic segmentation on Polar BEV. Remote Sens.\u00a014(15), Art. 15.\u00a0(2022)","DOI":"10.3390\/rs14153825"},{"key":"598_CR37","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Range adaptation for 3d object detection in lidar, in Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops. (2019)","DOI":"10.1109\/ICCVW.2019.00285"},{"key":"598_CR38","doi-asserted-by":"crossref","unstructured":"Qiu, S., et al.: PC-BEV: An Efficient Polar-Cartesian BEV Fusion Framework for LiDAR Semantic Segmentation, in Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6612\u20136620. (2025)","DOI":"10.1609\/aaai.v39i6.32709"},{"key":"598_CR39","unstructured":"Zhou, Y., et al.: End-to-end multi-view fusion for 3d object detection in lidar point clouds, in Conference on Robot Learning, PMLR, pp. 923\u2013932. (2020)"},{"key":"598_CR40","unstructured":"TensorRT Documentation \u2014 NVIDIA TensorRT Documentation. Accessed: May 22, 2025. [Online]. Available: https:\/\/docs.nvidia.com\/deeplearning\/tensorrt\/latest\/index.html"},{"key":"598_CR41","unstructured":"Jetson, A.G.X., Orin Developer Kit User Guide,, Developer, N.V.I.D.I.A., Accessed: May 22, 2025. [Online]. Available: https:\/\/developer.nvidia.com\/embedded\/learn\/jetson-agx-orin-devkit-user-guide\/index.html"},{"key":"598_CR42","doi-asserted-by":"crossref","unstructured":"Milioto, A., et al.: Rangenet++: Fast and accurate lidar semantic segmentation, in 2019 IEEE\/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp. 4213\u20134220. (2019)","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"598_CR43","doi-asserted-by":"crossref","unstructured":"Lim, H., et al.: Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor, Mar. 10, arXiv: arXiv:2108.05560. (2022)","DOI":"10.1109\/LRA.2021.3093009"}],"container-title":["International Journal of Intelligent Transportation Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-025-00598-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13177-025-00598-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-025-00598-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T07:23:09Z","timestamp":1777360989000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13177-025-00598-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,26]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["598"],"URL":"https:\/\/doi.org\/10.1007\/s13177-025-00598-2","relation":{},"ISSN":["1348-8503","1868-8659"],"issn-type":[{"value":"1348-8503","type":"print"},{"value":"1868-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,26]]},"assertion":[{"value":"16 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}