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06-11-2023

Fast Clustering for Cooperative Perception Based on LiDAR Adaptive Dynamic Grid Encoding

Authors: Xinkai Kuang, Hui Zhu, Biao Yu, Bichun Li

Published in: Cognitive Computation | Issue 2/2024

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Abstract

This study introduces a strategy inspired by cooperative behavior in nature to enhance information sharing among autonomous vehicles (AVs), advancing intelligent transportation systems. However, in the context of multiple light detection and ranging (LiDAR)-equipped vehicles cooperating, the generated point cloud data can obstruct real-time environment perception. This research assumes real-time, lossless data transmission, and accurate and reliable pose information sharing between cooperative vehicles. Based on human-inspired principles and computer imaging techniques, a method was proposed to encode dynamic grids for fusion LiDAR point cloud data, contingent upon inter-vehicle distances. Each grid cell corresponds to an image pixel, creating smaller cells for dense point clouds and larger cells for sparse point clouds. This maintains an approximately equal number of point clouds per cell. Additionally, a ground segmentation approach is developed, based on density and elevation differences of adjacent grids to retain significant obstacle points. A grid density-based adjacent clustering approach was proposed, which effectively classified the connected grid cells containing the obstacle points. Experiments using the robot operating system on a standard computer with public data show that the perception processing period for six cooperative vehicles is merely 43.217 ms. This demonstrates the efficacy of our method in handling large volumes of LiDAR point cloud data. Comparative analysis with three alternative methods confirmed the superior accuracy and recall of our clustering approach. This underscores the robustness of our biologically inspired methodology for the design of cooperative perception, thereby promoting efficient and safe vehicle navigation.

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Literature
1.
go back to reference Khatab E, Onsy A, Varley M, Abouelfarag A. Vulnerable objects detection for autonomous driving: a review. Integration. 2021;78:36–48.CrossRef Khatab E, Onsy A, Varley M, Abouelfarag A. Vulnerable objects detection for autonomous driving: a review. Integration. 2021;78:36–48.CrossRef
2.
go back to reference Su Z, Hui Y, Luan TH, Liu Q, Xing R. Deep learning based autonomous driving in vehicular networks. 2020. p. 131–50. Su Z, Hui Y, Luan TH, Liu Q, Xing R. Deep learning based autonomous driving in vehicular networks. 2020. p. 131–50.
3.
go back to reference Tsukada M, Oi T, Ito A, Hirata M, Esaki H. AutoC2X: open-source software to realize V2X cooperative perception among autonomous vehicles. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). 2020. p. 1–6. Tsukada M, Oi T, Ito A, Hirata M, Esaki H. AutoC2X: open-source software to realize V2X cooperative perception among autonomous vehicles. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). 2020. p. 1–6.
4.
go back to reference Cui G, Zhang W, Xiao Y, Yao L, Fang Z. Cooperative perception technology of autonomous driving in the internet of vehicles environment: a review. Sensors. 2022;22(15). Cui G, Zhang W, Xiao Y, Yao L, Fang Z. Cooperative perception technology of autonomous driving in the internet of vehicles environment: a review. Sensors. 2022;22(15).
5.
go back to reference Xu R, Guo Y, Han X, Xia X, Xiang H, Ma J. OpenCDA: an open cooperative driving automation framework integrated with co-simulation. CoRR. abs/2107.06260. 2021. Xu R, Guo Y, Han X, Xia X, Xiang H, Ma J. OpenCDA: an open cooperative driving automation framework integrated with co-simulation. CoRR. abs/2107.06260. 2021.
6.
go back to reference Qian C, Zhang H, Li W, Tang J, Liu H, Li B. Cooperative GNSS-RTK ambiguity resolution with GNSS, INS, and LiDAR data for connected vehicles. Remote Sens. 2020;12(6). Qian C, Zhang H, Li W, Tang J, Liu H, Li B. Cooperative GNSS-RTK ambiguity resolution with GNSS, INS, and LiDAR data for connected vehicles. Remote Sens. 2020;12(6).
7.
go back to reference Zeng Y, Qin H, Wang K, Li Q. A survey of LiDAR-based perception for autonomous driving: from detection to segmentation. IEEE Trans Intell Transp Syst. 2021;22(1):449–69. Zeng Y, Qin H, Wang K, Li Q. A survey of LiDAR-based perception for autonomous driving: from detection to segmentation. IEEE Trans Intell Transp Syst. 2021;22(1):449–69.
8.
go back to reference Yuan C, Lyu L, Sun H, Li X. LiDAR-based pedestrian detection in autonomous driving: recent advances and future research directions. IEEE Trans Intell Transp Syst. 2021;22(1):2–19. Yuan C, Lyu L, Sun H, Li X. LiDAR-based pedestrian detection in autonomous driving: recent advances and future research directions. IEEE Trans Intell Transp Syst. 2021;22(1):2–19.
9.
go back to reference Zhang Z, Han S, Yi H, Duan F, Kang F, Sun Z, Solé-Casals J, Caiafa C. A brain-controlled vehicle system based on steady state visual evoked potentials. Cognit Comput. 2022. Zhang Z, Han S, Yi H, Duan F, Kang F, Sun Z, Solé-Casals J, Caiafa C. A brain-controlled vehicle system based on steady state visual evoked potentials. Cognit Comput. 2022.
10.
go back to reference An Y, Shi J, Gu D, Liu Q. Visual-LiDAR SLAM based on unsupervised multi-channel deep neural networks. Cogn Comput. 2022;14(4):1496–508.CrossRef An Y, Shi J, Gu D, Liu Q. Visual-LiDAR SLAM based on unsupervised multi-channel deep neural networks. Cogn Comput. 2022;14(4):1496–508.CrossRef
11.
go back to reference Yumer E, Abdel-Qader Y. Multi-vehicle cooperative perception using LiDAR: a comprehensive review and future directions. IEEE Trans Intell Veh. 2021;6(2):164–81. Yumer E, Abdel-Qader Y. Multi-vehicle cooperative perception using LiDAR: a comprehensive review and future directions. IEEE Trans Intell Veh. 2021;6(2):164–81.
12.
go back to reference Abdel-Qader Y, Yumer E. Multi-vehicle cooperative perception using LiDAR: low-level fusion, feature-level fusion, and high-level fusion. Sensors. 2021;21(6):2114. Abdel-Qader Y, Yumer E. Multi-vehicle cooperative perception using LiDAR: low-level fusion, feature-level fusion, and high-level fusion. Sensors. 2021;21(6):2114.
13.
go back to reference Nguyen C, de Lucas M, Dario P. Multi-vehicle cooperative perception using LiDAR-based low-level sensor fusion and graph optimization. Robot Autonom Syst. 2021;143:104235. Nguyen C, de Lucas M, Dario P. Multi-vehicle cooperative perception using LiDAR-based low-level sensor fusion and graph optimization. Robot Autonom Syst. 2021;143:104235.
14.
go back to reference Liu K, Huang Y, Zhao F, Wang Z. Cooperative perception for autonomous vehicles using LiDAR and V2X communication. IEEE Trans Veh Technol. 2020;69(3):2821–33. Liu K, Huang Y, Zhao F, Wang Z. Cooperative perception for autonomous vehicles using LiDAR and V2X communication. IEEE Trans Veh Technol. 2020;69(3):2821–33.
15.
go back to reference Zhang C, Zhang X, Wang B. A feature-level fusion approach for multi-vehicle cooperative perception using LiDAR and radar sensors. Sens Actuators A. 2023;324:112859. Zhang C, Zhang X, Wang B. A feature-level fusion approach for multi-vehicle cooperative perception using LiDAR and radar sensors. Sens Actuators A. 2023;324:112859.
16.
go back to reference Chen Q, Zhang J, Chen R, Shen W. Multi-vehicle cooperative perception and localization based on high-level fusion of LiDAR and map data. J Adv Transport. 2022. Chen Q, Zhang J, Chen R, Shen W. Multi-vehicle cooperative perception and localization based on high-level fusion of LiDAR and map data. J Adv Transport. 2022.
17.
go back to reference Zhao J, Chen Q, Shen W. Multi-vehicle cooperative perception and localization based on high-level fusion of LiDAR and camera data. In: Proceedings of the 15th International Conference on Machine Vision (ICMV 2022); 2022. vol. 8934, p. 893401. Zhao J, Chen Q, Shen W. Multi-vehicle cooperative perception and localization based on high-level fusion of LiDAR and camera data. In: Proceedings of the 15th International Conference on Machine Vision (ICMV 2022); 2022. vol. 8934, p. 893401.
18.
go back to reference Pratibha C, Kumar A, Kamboj V. Partition-based clustering for real-time processing of LiDAR point clouds in autonomous vehicles. Sensors. 2021;21(9):3083. Pratibha C, Kumar A, Kamboj V. Partition-based clustering for real-time processing of LiDAR point clouds in autonomous vehicles. Sensors. 2021;21(9):3083.
19.
go back to reference Shang R, Ara B, Zada I, Nazir S, Ullah Z, Khan SU. Analysis of simple k-mean and parallel k-mean clustering for software products and organizational performance using education sector dataset. Sci Program. 2021;1–20:2021. Shang R, Ara B, Zada I, Nazir S, Ullah Z, Khan SU. Analysis of simple k-mean and parallel k-mean clustering for software products and organizational performance using education sector dataset. Sci Program. 2021;1–20:2021.
20.
go back to reference Daniel K, Friedrich F. Cognitive clustering of traffic scenarios for autonomous driving. In: IEEE Transactions on Intelligent Transportation Systems, vol. 21. 2020. Daniel K, Friedrich F. Cognitive clustering of traffic scenarios for autonomous driving. In: IEEE Transactions on Intelligent Transportation Systems, vol. 21. 2020.
21.
go back to reference Liu S, Wang Y, Zhang T, Huang H. Real-time multi-LiDAR-based dynamic object detection with hierarchical clustering. IEEE Trans Intell Veh. 2021;6(2):148–58. Liu S, Wang Y, Zhang T, Huang H. Real-time multi-LiDAR-based dynamic object detection with hierarchical clustering. IEEE Trans Intell Veh. 2021;6(2):148–58.
22.
go back to reference Zhang X, Zhang L, Zhang Y, Yingjie W, Jiao L. A cognitive hierarchical clustering algorithm for object detection on autonomous driving scenes. IEEE Access. 2020;8:99216–26. Zhang X, Zhang L, Zhang Y, Yingjie W, Jiao L. A cognitive hierarchical clustering algorithm for object detection on autonomous driving scenes. IEEE Access. 2020;8:99216–26.
23.
go back to reference Yoo S, Kim S, Kim K. A novel distance-based clustering algorithm for LiDAR point cloud in autonomous driving systems. Sensors. 2021;21(8):2896. Yoo S, Kim S, Kim K. A novel distance-based clustering algorithm for LiDAR point cloud in autonomous driving systems. Sensors. 2021;21(8):2896.
24.
go back to reference Lin C, Yu W. Real-time obstacle detection and avoidance for autonomous vehicles using LiDAR and distance-based clustering algorithm. In: 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE; 2020. p. 963–7. Lin C, Yu W. Real-time obstacle detection and avoidance for autonomous vehicles using LiDAR and distance-based clustering algorithm. In: 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE; 2020. p. 963–7.
25.
go back to reference Ester M, Kriegel H-P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, AAAI Press; 1996. p. 226–31. Ester M, Kriegel H-P, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96, AAAI Press; 1996. p. 226–31.
26.
go back to reference Yoon H, Jeong J, Jang M, Kim J, Lee K, Lee S. Cognitive-based cluster analysis for improving object detection performance in autonomous vehicles. IEEE Trans Intell Transp Syst. 2019;20(7):2718–30. Yoon H, Jeong J, Jang M, Kim J, Lee K, Lee S. Cognitive-based cluster analysis for improving object detection performance in autonomous vehicles. IEEE Trans Intell Transp Syst. 2019;20(7):2718–30.
27.
go back to reference Li S-S. An improved DBSCAN algorithm based on the neighbor similarity and fast nearest neighbor query. IEEE Access. 2020;8:47468–76.CrossRef Li S-S. An improved DBSCAN algorithm based on the neighbor similarity and fast nearest neighbor query. IEEE Access. 2020;8:47468–76.CrossRef
28.
go back to reference Chen W, Li C, Huang F, Liu Y, El-Sheimy N. Efficient real-time detection of pedestrians using 3D LiDAR and grid-based clustering algorithm. IEEE Trans Veh Technol. 2021;70(9):8833–43. Chen W, Li C, Huang F, Liu Y, El-Sheimy N. Efficient real-time detection of pedestrians using 3D LiDAR and grid-based clustering algorithm. IEEE Trans Veh Technol. 2021;70(9):8833–43.
29.
go back to reference Yang H, Wang Z, Lin L, Liang H, Huang W, Xu F. Two-layer-graph clustering for real-time 3D LiDAR point cloud segmentation. Appl Sci. 2020;10(23):11.CrossRef Yang H, Wang Z, Lin L, Liang H, Huang W, Xu F. Two-layer-graph clustering for real-time 3D LiDAR point cloud segmentation. Appl Sci. 2020;10(23):11.CrossRef
30.
go back to reference Klasing K, Wollherr D, Buss M. A clustering method for efficient segmentation of 3D laser data. In: 2008 IEEE International Conference on Robotics and Automation. 2008. p. 4043–8. Klasing K, Wollherr D, Buss M. A clustering method for efficient segmentation of 3D laser data. In: 2008 IEEE International Conference on Robotics and Automation. 2008. p. 4043–8.
31.
go back to reference Zhu L, Zhang K, Ma L, Liu W. Cognitive inspired clustering for scene segmentation in autonomous driving. IEEE Trans Intell Transp Syst. 2019;20(2):596–606. Zhu L, Zhang K, Ma L, Liu W. Cognitive inspired clustering for scene segmentation in autonomous driving. IEEE Trans Intell Transp Syst. 2019;20(2):596–606.
32.
go back to reference Li Y, Zhang K, Zhu L, Liu W. A cognitive clustering algorithm for multi-layer object detection in autonomous driving. IEEE Trans Intell Transp Syst. 2019;21(4):1672–82. Li Y, Zhang K, Zhu L, Liu W. A cognitive clustering algorithm for multi-layer object detection in autonomous driving. IEEE Trans Intell Transp Syst. 2019;21(4):1672–82.
33.
go back to reference Rajamäki J, Mademlis I, Riekki J. A cognitive architecture for multi-vehicle cooperative perception. IEEE Trans Cognit Develop Syst. 2017;9(3):241–53. Rajamäki J, Mademlis I, Riekki J. A cognitive architecture for multi-vehicle cooperative perception. IEEE Trans Cognit Develop Syst. 2017;9(3):241–53.
34.
go back to reference Hurl B, Cohen R, Czarnecki K, Waslander S. TruPercept: trust modelling for autonomous vehicle cooperative perception from synthetic data. In: 2020 IEEE Intelligent Vehicles Symposium (IV). 2020. p. 341–7. Hurl B, Cohen R, Czarnecki K, Waslander S. TruPercept: trust modelling for autonomous vehicle cooperative perception from synthetic data. In: 2020 IEEE Intelligent Vehicles Symposium (IV). 2020. p. 341–7.
35.
go back to reference Duan X, Jiang H, Tian D, Zou T, Zhou J, Cao Y. V2I based environment perception for autonomous vehicles at intersections. China Commun. 2021;18(7):1–12.CrossRef Duan X, Jiang H, Tian D, Zou T, Zhou J, Cao Y. V2I based environment perception for autonomous vehicles at intersections. China Commun. 2021;18(7):1–12.CrossRef
36.
go back to reference Chen Q, Tang S, Hochstetler J, Guo J, Li Y, Xiong J, Yang Q, Fu S. Low-latency high-level data sharing for connected and autonomous vehicular networks. In: 2019 IEEE International Conference on Industrial Internet (ICII). 2019. p. 287–96. Chen Q, Tang S, Hochstetler J, Guo J, Li Y, Xiong J, Yang Q, Fu S. Low-latency high-level data sharing for connected and autonomous vehicular networks. In: 2019 IEEE International Conference on Industrial Internet (ICII). 2019. p. 287–96.
37.
go back to reference Metzner A, Wickramarathne T. Exploiting vehicle-to-vehicle communications for enhanced situational awareness. In: 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). 2019. p. 88–92. Metzner A, Wickramarathne T. Exploiting vehicle-to-vehicle communications for enhanced situational awareness. In: 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). 2019. p. 88–92.
38.
go back to reference Xu R, Xiang H, Xia X, Han X, Liu J, Ma J. OPV2V: an open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. CoRR. abs/2109.07644. 2021. Xu R, Xiang H, Xia X, Han X, Liu J, Ma J. OPV2V: an open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication. CoRR. abs/2109.07644. 2021.
39.
go back to reference Ma Y, Liu Y, Dai M, Yang Y. A LiDAR based height difference threshold segmentation method for ground extraction in autonomous driving. Sensors. 2021;21(7):2569. Ma Y, Liu Y, Dai M, Yang Y. A LiDAR based height difference threshold segmentation method for ground extraction in autonomous driving. Sensors. 2021;21(7):2569.
40.
go back to reference Guo H, Wang Y, Mao K, Li T, Zhou J, Mao J. Ground feature extraction from LiDAR data using height difference threshold segmentation. Remote Sens Lett. 2022;13(4):382–90. Guo H, Wang Y, Mao K, Li T, Zhou J, Mao J. Ground feature extraction from LiDAR data using height difference threshold segmentation. Remote Sens Lett. 2022;13(4):382–90.
41.
go back to reference Li C, Zhang X, Zhao Q, Tong X. Improved height difference threshold segmentation method for LiDAR-based ground extraction. Remote Sens. 2023;15(2):389. Li C, Zhang X, Zhao Q, Tong X. Improved height difference threshold segmentation method for LiDAR-based ground extraction. Remote Sens. 2023;15(2):389.
42.
go back to reference Shen Z, Liang H, Lin L, Wang Z, Huang W, Yu J. Fast ground segmentation for 3D LiDAR point cloud based on jump-convolution-process. Remote Sens. 2021;13(16). Shen Z, Liang H, Lin L, Wang Z, Huang W, Yu J. Fast ground segmentation for 3D LiDAR point cloud based on jump-convolution-process. Remote Sens. 2021;13(16).
43.
go back to reference Grubbs FE. Procedures for detecting outlying observations in samples. Technometrics. 1969;11(1):1–21.CrossRef Grubbs FE. Procedures for detecting outlying observations in samples. Technometrics. 1969;11(1):1–21.CrossRef
Metadata
Title
Fast Clustering for Cooperative Perception Based on LiDAR Adaptive Dynamic Grid Encoding
Authors
Xinkai Kuang
Hui Zhu
Biao Yu
Bichun Li
Publication date
06-11-2023
Publisher
Springer US
Published in
Cognitive Computation / Issue 2/2024
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-023-10211-x

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