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

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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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Data Availability

A publicly available dataset was analyzed in this study. The data can be found here: https://mobility-lab.seas.ucla.edu/opv2v/(access on 17 March 2022).

References

  1. Khatab E, Onsy A, Varley M, Abouelfarag A. Vulnerable objects detection for autonomous driving: a review. Integration. 2021;78:36–48.

    Article  Google Scholar 

  2. Su Z, Hui Y, Luan TH, Liu Q, Xing R. Deep learning based autonomous driving in vehicular networks. 2020. p. 131–50.

  3. 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. 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. 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. 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. 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.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. 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. 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.

    Article  Google Scholar 

  11. 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.

    Google Scholar 

  12. 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.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. 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.

    Google Scholar 

  15. 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.

    Google Scholar 

  16. 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. 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. 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.

    Google Scholar 

  19. 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.

    Google Scholar 

  20. Daniel K, Friedrich F. Cognitive clustering of traffic scenarios for autonomous driving. In: IEEE Transactions on Intelligent Transportation Systems, vol. 21. 2020.

  21. 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.

    Google Scholar 

  22. 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.

    Google Scholar 

  23. 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.

    Google Scholar 

  24. 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. 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. 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.

    Google Scholar 

  27. Li S-S. An improved DBSCAN algorithm based on the neighbor similarity and fast nearest neighbor query. IEEE Access. 2020;8:47468–76.

    Article  Google Scholar 

  28. 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.

    Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. 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. 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.

    Google Scholar 

  32. 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.

    Google Scholar 

  33. Rajamäki J, Mademlis I, Riekki J. A cognitive architecture for multi-vehicle cooperative perception. IEEE Trans Cognit Develop Syst. 2017;9(3):241–53.

    Google Scholar 

  34. 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. 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.

    Article  Google Scholar 

  36. 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. 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. 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. 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.

    Google Scholar 

  40. 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.

    Google Scholar 

  41. 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.

    Google Scholar 

  42. 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. Grubbs FE. Procedures for detecting outlying observations in samples. Technometrics. 1969;11(1):1–21.

    Article  Google Scholar 

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Funding

This work was partially supported by the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant No. Y2021115).

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Authors and Affiliations

  1. Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China

    Xinkai Kuang, Hui Zhu, Biao Yu & Bichun Li

  2. Science Island Branch, University of Science and Technology of China, Hefei, 230031, China

    Xinkai Kuang

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  1. Xinkai Kuang
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  2. Hui Zhu
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  3. Biao Yu
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  4. Bichun Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by XK, HZ, BY, and BL. The first draft of the manuscript was written by XK, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bichun Li.

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Kuang, X., Zhu, H., Yu, B. et al. Fast Clustering for Cooperative Perception Based on LiDAR Adaptive Dynamic Grid Encoding. Cogn Comput 16, 546–565 (2024). https://doi.org/10.1007/s12559-023-10211-x

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  • DOI: https://doi.org/10.1007/s12559-023-10211-x

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