A Theoretical and Technical Framework for Roboticized Mining and Hauling in Open-pit Mines
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摘要: 露天矿机器人化采运系统面临复杂特定场景数据不足、极端工况测试困难、现场试验与调试安全风险高、高动态变化环境的感知与建模复杂和全场景物理实验周期长等挑战, 亟需突破提效开采演化机理、高精度全域融合感知、高效率稳定协同控制、高可靠安全群体管控4大科学问题. 通过研究极端环境全域多模感知与变载稳健自适应控制、动态装卸区多机装载协同与多车高效卸载规划、采运系统自主学习建模与虚实融合平行仿真、高适用性工程应用方案, 提出露天矿立足机器人化采运“端边感知、平行控制”的智慧生产模式, 从感控、协同、调度、应用等多层次提出机器人化采运关键理论与技术. 通过技术创新和迭代优化, 实现我国露天矿机器人化采运系统技术自主创新和关键技术自主可控, 确保露天煤矿机器人化运输车的大批量安全高效作业运行, 形成我国露天煤矿迈向高水平智能化的“双十 (10项创新技术、10项机器人化采运标准)、双百 (100台车应用示范, 较有人系统的110%的综合运输运行效率)、双千 (
1000 台车容量的监控平台,1000 小时平均无故障运行时间)”的中国方案, 有力支撑我国矿山智能化绿色开采发展战略.Abstract: The roboticized mining and hauling system for open-pit mines faces numerous challenges, including insufficient data for complex, specific scenarios, difficulties in testing under extreme operating conditions, high safety risks in field testing and debugging, complexity in perception and modeling within dynamic environments, and extended periods for full-scale physical experimentation. There is an urgent need to address four major scientific issues: improving mining efficiency and evolutionary mechanisms, achieving high-precision and comprehensive fusion perception, enabling efficient and stable collaborative control, and ensuring reliable and secure group management. This study investigates the global multi-modal perception in extreme environments and robust adaptive control for varying loads, multi-machine collaborative loading and efficient multi-vehicle unloading planning in dynamic loading and unloading zones, autonomous system learning and modeling, as well as virtual-physical integrated parallel simulation. A highly adaptable engineering application scheme is proposed, establishing an intelligent production model for open-pit mines based on robotic mining transportation with “End-Edge Sensing and parallel control”. Key theories and technologies for robotic mining operations are proposed across multiple levels, including sensing and control, collaboration, scheduling, and application. Through technological innovation and iterative optimization, this research aims to achieve independent innovation and controllability of the core technologies in China＇s open-pit mining robotic transportation system, ensuring the large-scale, safe, and efficient operation of robotic transportation vehicles in open-pit coal mines. This study forms a comprehensive “Chinese solution”, supporting the intelligent and green mining development strategy in China, including “Double Tens” (10 innovative technologies, 10 robotic mining transportation standards), “Double Hundreds” (100 vehicle application demonstration, 110% overall transportation efficiency compared to manned systems), and “Double Thousands" (monitoring platform with1000 vehicle capacity,1000 hours of average fault-free operation time). -
图 3 机器人化采运的“端边感知、平行控制”智慧架构
Fig. 3 "End-edge sensing, parallel control" intelligent framework for robotic mining operations
图 6 矿区3D目标检测性能对比图
Fig. 6 3D object detection performance comparison chart for mine sites
图 7 世界模型生成矿区驾驶场景数据
Fig. 7 Generation of world models for mining area driving scene data
图 10 矿区三维高斯重建及投影模型
Fig. 10 3D Gaussian reconstruction and projection model of the mining area
图 12 基于时空置信度的多模态传感器融合定位方法
Fig. 12 Multi-modal sensor fusion localization approach based on spatiotemporal confidence
图 14 基于BEV的多模态端边系统全域感知技术路线
Fig. 14 BEV-based multi-modal edge system for comprehensive perception technology roadmap
图 16 平行仿真平台设计总体技术路线
Fig. 16 Comprehensive technological roadmap for parallel simulation platform design
图 20 虚拟地形场路径跟踪控制原理示意图
Fig. 20 Schematic of virtual terrain path tracking control principle
图 21 车铲多机协同技术
Fig. 21 Multi-machine collaborative technology for vehicle and bucket systems
图 26 无人驾驶技术应用建设体系
Fig. 26 Framework for the application and development of autonomous driving technology
图 27 黑岱沟露天煤矿无人驾驶重型运输矿卡系统作业流程图
Fig. 27 Workflow of autonomous driving heavy-duty mining truck system at heidaigou open-pit coal mine
图 28 特殊工况下无人驾驶重型运输矿卡现场作业图
Fig. 28 On-site operation of autonomous driving heavy-duty mining trucks under special conditions
图 29 露天矿机器人化采运十项创新技术体系
Fig. 29 Ten innovative robotic mining technologies for open-pit mines
图 30 露天矿卡车无人驾驶运输技术要求标准架构
Fig. 30 Technical specifications for autonomous driving transportation systems in open-pit mines
图 31 百台运输机器人运输系统编组运行示意图
Fig. 31 Schematic diagram of formation operation for a system of one hundred transportation robots
图 32 综合运输效率提升技术
Fig. 32 Technology for enhancing comprehensive transportation efficiency
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[1] Kinnunen P H M, Kaksonen A H. Towards circular economy in mining: Opportunities and bottlenecks for tailings valorization. Journal of Cleaner Production, 2019, 228: 163−160 [2] 郑南宁. 无人驾驶也需谨慎. 现代制造, 2020(13): 42Zheng Nan-Ning. Autonomous driving also requires caution. Maschinen Markt, 2020(13): 42 [3] Yang J J, Huang Q K, Ge S R, Wang X, Chen L, Guo Y N, et al. On intelligent mining with parallel intelligence. IEEE Transactions On Intelligent Vehicles, 2023, 8(10): 4296−4300 doi: 10.1109/TIV.2023.3316132 [4] Chen L, Li Y C, Huang C, Li B, Xing Y, Tian D X. Milestones in autonomous driving and intelligent vehicles: survey of surveys. IEEE Transactions on Intelligent Vehicles, 2023, 8(2): 1046−1056 doi: 10.1109/TIV.2022.3223131 [5] 中国煤炭工业协会信息化分会. 露天煤矿无人驾驶技术应用发展报告, 中国, 2024Informationization Branch of China National Coal Association. Development Report on the Application of Unmanned Driving Technology in Open-pit Coal Mines, China, 2024. [6] 葛世荣, 张晞, 薛光辉, 任怀伟, 王宏伟, 庞义辉, 等. 我国煤矿煤机智能技术与装备发展研究. 中国工程科学, 2023, 25(05): 146−156 doi: 10.15302/J-SSCAE-2023.05.013Ge Shi-Rong, Zhang Xi, Xue Guang-Hui, Ren Huai-Wei, Wang Hong-Wei, Pang Yi-Hui, et al. Development of intelligent technologies and machinery for coal mining in china's underground coal mines. Strategic Study of CAE, 2023, 25(05): 146−156 doi: 10.15302/J-SSCAE-2023.05.013 [7] 葛世荣, 胡而已, 裴文良. 煤矿机器人体系及关键技术. 煤炭学报, 2020, 45(01): 455−463 doi: 10.13225/j.cnki.jccs.YG19.1478Ge Shi-Rong, Hu Er-Yi, Pei Wen-Liang. Classification system and key technology of coal mine robot. Journal of China Coal Society, 2020, 45(01): 455−463 doi: 10.13225/j.cnki.jccs.YG19.1478 [8] 鲍久圣, 任强, 葛世荣, 魏聪, 陈超. 露天矿自卸汽车大坡度提升系统设计研究. 煤炭技术, 2019, 38(09): 145−150 doi: 10.13301/j.cnki.ct.2019.09.048Bao Jiu-Sheng, Ren Qiang, Ge Shi-Rong, Wei Cong, Chen Chao. Design of large slope hoisting system for dump truck in open-pit mine. Coal Technology, 2019, 38(09): 145−150 doi: 10.13301/j.cnki.ct.2019.09.048 [9] 王肖, 李克强, 王建强, 徐友春. 基于三维激光雷达的智能车辆目标参数辨识. 汽车工程, 2016, 38(9): 1146−1152 doi: 10.19562/j.chinasae.qcgc.2016.09.017Wang Xiao, Li Ke-Qiang, Wang Jian-Qiang, Xu You-Chun. Parameter identification of intelligent vehicle target based on 3d laser radar. Automotive Engineering, 2016, 38(9): 1146−1152 doi: 10.19562/j.chinasae.qcgc.2016.09.017 [10] 栾博钰, 周伟. 露天煤矿无人驾驶下交叉口协调通行策略研究. 煤炭学报, 2023, 48(S1): 357−367 doi: 10.13225/j.cnki.jccs.2022.0967Luan Bo-Yu, Zhou Wei. Study of coordinated access strategy at intersections in open-pit coal mines with unmanned trucks. Journal of China Coal Society, 2023, 48(S1): 357−367 doi: 10.13225/j.cnki.jccs.2022.0967 [11] Chen C Y, Xu Q, Cai M C, Wang J W, Wang J Q, Li K Q. Conflict-free cooperation method for connected and automated vehicles at unsignalized intersections: Graph-based modeling and optimality analysis. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 21897−21914 doi: 10.1109/TITS.2022.3182403 [12] Berkhahn V, Kleiber M, Langner J, Timmermann C, Weber S. Traffic dynamics at intersections subject to random misperception. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(5): 4501−4511 doi: 10.1109/TITS.2020.3045480 [13] Fang S S, Li H. Multi-vehicle cooperative simultaneous lidar slam and object tracking in dynamic environments. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(09): 11411−11421 doi: 10.1109/TITS.2024.3360259 [14] Ngo H, Fang H, Wang H G. Cooperative perception with V2V communication for autonomous vehicles. IEEE Transactions on Vehicular Technology, 2023, 72(9): 11122−11131 doi: 10.1109/TVT.2023.3264020 [15] Li J L, Xu R S, Liu X Y, Ma J, Chi Z C, Ma J Q, et al. Learning for vehicle-to-vehicle cooperative perception under lossy communication. IEEE Transactions on Intelligent Vehicles, 2023, 8(4): 2650−2260 doi: 10.1109/TIV.2023.3260040 [16] 王祝, 徐广通, 龙腾. 基于定制内点法的多无人机协同轨迹规划. 自动化学报, 2023, 49(11): 2374−2385 doi: 10.16383/j.aas.c200361Wang Zhu, Xu Guang-Tong, Long Teng. Customized interior-point method for cooperative trajectory planning of multiple unmanned aerial vehicles. Acta Automatica Sinica, 2023, 49(11): 2374−2385 doi: 10.16383/j.aas.c200361 [17] Wang K Z, Wang Y F, Wang L, Du H P, Nam K H. Distributed intersection conflict resolution for multiple vehicles considering longitudinal-lateral dynamics. IEEE Transactions on Vehicular Technology, 2021, 70(5): 4166−4177 doi: 10.1109/TVT.2021.3072629 [18] 朱永薪, 李永福, 朱浩, 于树友. 通信延时环境下基于观测器的智能网联车辆队列分层协同纵向控制. 自动化学报, 2023, 49(08): 1785−1798Zhu Yong-Xin, Li Yong-Fu, Zhu Hao, Yu Shu-You. Observer-based longitudinal control for connected and automated vehicles platoon subject to communication delay. Acta Automatica Sinica, 2023, 49(08): 1785−1798 [19] 王峰, 黄子路, 韩孟臣, 刑立宁, 王凌. 基于KnCMPSO算法的异构无人机协同多任务分配. 自动化学报, 2023, 49(2): 399−414 doi: 10.16383/j.aas.c210696Wang Feng, Huang Zi-Lu, Han Meng-Chen, Xing Li-Ning, Wang Ling. A knee point based coevolution multi-objective particle swarm optimization algorithm for heterogeneous UAV cooperative multi-task allocation. Acta Automatica Sinica, 2023, 49(2): 399−414 doi: 10.16383/j.aas.c210696 [20] Faroni M, Umbrico A, Beschi M, Orlandini A, Cesta A, Pedrocchi N. Optimal task and motion planning and execution for multiagent systems in dynamic environments. IEEE Transactions on Cybernetics, 2023, 53(6): 3366−3377 [21] Nooshin A, Hossein M N, Amin M. A genetic algorithm scheme for large scale open-pit mine production scheduling. Mining Technology, 2023, 132(04): 225−236 doi: 10.1080/25726668.2023.2228071 [22] Huo D, Sari Y A, Zhang Q. Smart dispatching for low-carbon mining fleet: A deep reinforcement learning approach. Journal of Cleaner Production, 2024, 435: 1−11 [23] Zhang X T, Xiong G, Ai Y F, Liu K H, Chen L. Vehicle dynamic dispatching using curriculum-driven reinforcement learning. Mechanical Systems and Signal Processing, 2024, 204: 1−16 [24] 陈博言, 沈晴霓, 张晓磊, 张鑫, 李聪, 吴中海. 智能网联汽车的车载网络攻防技术研究进展. 软件学报, 2024, 36(1): 341−370 doi: 10.13328/j.cnki.jos.007196Chen Bo-Yan, Shen Qing-Ni, Zhang Xiao-Lei, Zhang Xin, Li Cong, Wu Zhong-Hai. Research progress on attacks and defenses technologies for in-vehicle network of intelligent connected vehicle. Journal of Software, 2024, 36(1): 341−370 doi: 10.13328/j.cnki.jos.007196 [25] 方万胜, 黄金, 金涛. 基于数字证书的智能网联汽车安全认证技术研究. 道路交通科学技术, 2022(6): 46−51Fang Wan-Sheng, Huang Jin, Jin Tao. Research on security authentication technology for intelligent connected vehicles based on digital certificates. Road Traffic Science & Technology, 2022(6): 46−51 [26] 江枫. 基于LTE-V2X的网联车队信息安全的研究与实现, 北京, 北京邮电大学, 2020Jiang Feng. Research and implementation on information security of the LTE-V2X connected platoon [Master thesis], Beijing University of Posts and Telecommunications, China, 2020 [27] 王晓燕, 杨晶晶, 黄铭, 吴季达, 彭子箫. GNSS干扰和欺骗检测研究现状与展望. 信号处理, 2023, 39(12): 2131−2152 doi: 10.16798/j.issn.1003-0530.2023.12.003Wang Xiao-Yan, Yang Jing-Jing, Huang Ming, Wu Ji-Da, Peng Zi-Xiao. Research status and prospect of GNSS jamming and spoofing detection. Journal of Signal Processing, 2023, 39(12): 2131−2152 doi: 10.16798/j.issn.1003-0530.2023.12.003 [28] 刘登越. 考虑网络攻击影响的智能网联汽车安全防御综述. 综合运输, 2024, 46(1): 79−84 doi: 10.20164/j.cnki.cn11-1197/u.2024.01.014Liu Deng-Yue. A review of security defense of connected automated vehicle considering the impact of cyberattack. China Transportation Review, 2024, 46(1): 79−84 doi: 10.20164/j.cnki.cn11-1197/u.2024.01.014 [29] 陈俊平, 张益泽, 于超, 丁君生. 北斗卫星导航系统精密定位报告算法与性能评估. 测绘学报, 2022, 51(04): 511−521Chen Jun-Ping, Zhang Yi-Ze, Yu Chao, Ding Jun-Sheng. Processing algorithms and performance evaluation of BDS RDSS location reporting service. Acta Geodaetica et Cartographica Sinica, 2022, 51(04): 511−521 [30] 杨元喜. 北斗卫星导航系统的进展、贡献与挑战. 测绘学报, 2010, 39(01): 1−6Yang Yuan-Xi. Progress, contribution and challenges of compass/beidou satellite navigation system. Acta Geodaetica et Cartographica Sinica, 2010, 39(01): 1−6 [31] Gu Y, Hsu L T, Kamijo S. GNSS/onboard inertial sensor integration with the aid of 3-D building map for lane-level vehicle self-localization in urban canyon. IEEE Transactions on Vehicular Technology, 2015, 65(6): 4274−4287 [32] Zhe X, Li B, Zhang X, Chen L, Huang K. Online cooperative 3D mapping for autonomous driving. In: Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV). Changshu, China: IEEE, 2018. 256-261 [33] Bloesch M, Czarnowski J, Clark R, Leutenegger S, Davison A. Codeslam—learning a compact, optimisable representation for dense visual SLAM. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, USA: IEEE, 2018. 2560-2568 [34] Koestler L, Yang N, Zeller N, Cremers D. Tandem: Tracking and dense mapping in real-time using deep multi-view stereo. In: Proceedings of 5th Conference on Robot Learning (CoRL). London, England, 2022. 34-45 [35] Mildenhall B, Srinivasan P P, Tancik M, Barron J T, Ramamoorthi R, Ng R. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 2021, 65(1): 99−106 [36] Rosinol A, Leonard J J, Carlone L. Nerf-slam: Real-time dense monocular slam with neural radiance fields. In: Proceedings of 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Detroit, USA: IEEE, 2023. 3437-3444 [37] Suhr J K, Jang J, Min D, Jung H G. Sensor fusion-based low-cost vehicle localization system for complex urban environments. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(5): 1078−1086 [38] Ramezani M, Khoshelham K. Vehicle positioning in GNSS-deprived urban areas by stereo visual-inertial odometry. IEEE Transactions on Intelligent Vehicles, 2018, 3(2): 208−217 doi: 10.1109/TIV.2018.2804168 [39] Liu H, Chen M, Zhang G, Bao H, Bao Y. Ice-ba: Incremental, consistent and efficient bundle adjustment for visual-inertial slam. In: Proceedings of 31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, USA: IEEE/CVF, 2018. 1974-1982 [40] Ye H, Chen Y, Liu M. Tightly coupled 3d lidar inertial odometry and mapping. In: Proceedings of 2019 International Conference on Robotics and Automation (ICRA). Montreal, Canada: IEEE, 2019. 3144-3150 [41] Hu A, Murez Z, Mohan N, Dudas S, Hawke J, Badrinarayanan V, et al. Fiery: Future instance prediction in bird's-eye view from surround monocular cameras. In: Proceedings of the 18th IEEE/CVF International Conference on Computer Vision (CVPR). Electr Network: IEEE/CVF, 2021. 15273-15282 [42] Wen Y, Zhao Y, Liu Y, Jia F, Wang Y, Luo C, et al. Panacea: Panoramic and controllable video generation for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2024. 17347-17363 [43] Wang Y, He J, Fan L, Li H, Chen Y, Zhang Z. Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2024. 19354-19374 [44] Caesar H, Bankiti V, Lang A H, Vora S, Liong V E, Xu Q, et al. Nuscenes: A multimodal dataset for autonomous driving In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Electr Network: IEEE, 2020. 11621-11631 [45] Liang T, Xie H, Yu K, Xia Z, Lin Z, Wang Y, et al. Bevfusion: A simple and robust lidar-camera fusion framework. In: Proceedings of 36th Conference on Neural Information Processing Systems (NeurIPS). Electr Network, 2022. 10421-10434 [46] Liu Z, Tang H, Amini A, Yang X, Mao H, Rus D L, et al. Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In: Proceedings of 2023 IEEE international conference on robotics and automation (ICRA). London, England, 2023. 2774-2781 [47] Philion J, Fidler S. Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In: Proceedings of 16th European Conference on Computer Vision-ECCV-Biennial. Electr Network, 2020. 194-210 [48] Laddha A, Gautam S, Palombo S, Pandey S, Vallespi-Gonzalez C. Mvfusenet: Improving end-to-end object detection and motion forecasting through multi-view fusion of lidar data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Electr Network: IEEE, 2021. 2865-2874 [49] Paigwar A, Baranov E, Renzaglia A, Laugier C, Legay A. Probabilistic collision risk estimation for autonomous driving: Validation via statistical model checking. In: Proceedings of 31st IEEE Intelligent Vehicles Symposium (IV). Electr Network: IEEE, 2020. 737-743 [50] LaValle S M, Kuffner J J, Donald B R. Rapidly-exploring random trees: Progress and prospects. In: Proceedings of 4th International Workshop on the Algorithmic Foundations of Robotics (WAFR). Hanover, USA, 2001. 293-308 [51] Li Q, Wang Y, Wang Y, Zhao H. Hdmapnet: An online hd map construction and evaluation framework. In: Proceedings of the International Conference on Robotics and Automation (ICRA). Philadelphia, USA: IEEE, 2022. 4628-4634 [52] Liao B, Chen S, Wang X, Cheng T, Zhang Q, Liu W, et al. Maptr: Structured modeling and learning for online vectorized hd map construction. arXiv: 2208.14437, 2023. [53] Zhang X, Fu J, Chen S, Zheng N. Real-time, secure, and computationally efficient navigation: a prediction-planning framework using occupancy grid map. In: Proceedings of the IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Bilbao, SPAIN: IEEE, 2023. 4352-4359 [54] Xiong L, Zhang Y, Liu Y, Xiao H, Tang C. Integrated decision making and planning based on feasible region construction for autonomous vehicles considering prediction uncertainty. IEEE Transactions on Intelligent Vehicles, 2023, 8(11): 4515−4523 doi: 10.1109/TIV.2023.3299845 [55] Chen L, Hu X, Tian W, Wang H, Cao D, Wang F. Parallel planning: A new motion planning framework for autonomous driving. IEEE/CAA Journal of Automatica Sinica, 2018, 6(1): 236−246 [56] 郑南宁. 人工智能新时代. 智能科学与技术学报, 2019, 1(1): 1−3Zheng Nan-Ning. The new era of artificial intelligence. Chinese Journal of Intelligent Science and Technology, 2019, 1(1): 1−3 [57] Wang T, Zhu X, Pang J, Lin D. Fcos3d: Fully convolutional one-stage monocular 3d object detection. In: Proceedings of International Conference on Computer Vision Workshops. Virtual Event: IEEE, 2021. 913-922 [58] Lang A H, Vora S, Caesar H, Zhou L, Yang J, Beijbom O. Pointpillars: Fast encoders for object detection from point clouds. In: Proceedings of 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, USA: IEEE, 2019. 12697-12705 [59] Yin T, Zhou X, Krahenbuhl P. Center-based 3d object detection and tracking. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Electr Network: IEEE, 2021. 11784-11793 [60] Sindagi V A, Zhou Y, Tuzel O. Mvx-net: Multimodal voxelnet for 3d object detection. In: Proceedings of International Conference on Robotics and Automation. Montreal, Canada: IEEE, 2019. 7276-7272 [61] Vora S, Lang A H, Helou B, Beijbom O. Pointpainting: sequential fusion for 3d object detection. In: Proceedings of Conference on Computer Vision and Pattern Recognition. Electr Network: IEEE, 2020. 4604-4612 [62] Wang C, Ma C, Zhu M, Yang X. Pointaugmenting: Cross-modal augmentation for 3d object detection. In: Proceedings of Conference on Computer Vision and Pattern Recognition. Electr Network: IEEE, 2021. 11794-11803 [63] Liu Z, Huang T, Li B, Chen X, Wang X, Bai X. Epnet++: Cascade bi-directional fusion for multi-modal 3d object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(7): 8324−8341 [64] Bai X, Hu Z, Zhu X, Huang Q, Chen Y, Fu H, et al. Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. In: Proceedings of Conference on Computer Vision and Pattern Recognition. New Orleans, USA: IEEE, 2022. 1090-1099 [65] Liu Z, Tang H, Amini A, Yang X, Mao H, Rus D, et al. Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA). London, England: IEEE, 2023. 2774-2781 [66] Chen L, Xie J K, Zhang X T, Deng J L, Ge S.R, Wang F Y. Mining 5.0: Concept and framework for intelligent mining systems in CPSS. IEEE Transactions on Intelligent Vehicles, 2023, 8(6): 3533−3536 doi: 10.1109/TIV.2023.3285417 [67] 王飞跃. 平行控制: 数据驱动的计算控制方法. 自动化学报, 2013, 39(4): 293−302Wang F Y. Parallel control: A method for data-driven and computational control. Acta Automatica Sinica, 2013, 39(4): 293−302 [68] Chen L, Li Y C, Huang C, Li B, Xing Y, Tian D X, et al. Milestones in autonomous driving and intelligent vehicles: survey of surveys. IEEE Transactions on Intelligent Vehicles, 2023, 8(2): 1046−1056 doi: 10.1109/TIV.2022.3223131 [69] Chen L, Li Y C, Huang C, Li B, Xing Y, Tian D X, et al. Milestones in autonomous driving and intelligent vehicles—part I: Control, computing system design, communication, hd map, testing, and human behaviors. IEEE Transactions on Systems Man Cybernetics-Systems, 2023, 53(9): 5831−5847 doi: 10.1109/TSMC.2023.3276218 [70] Hu X, Li S, Huang T, Tang B, Huai R and Chen L. How simulation helps autonomous driving: A survey of sim2real, digital twins, and parallel intelligence. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 593−612 doi: 10.1109/TIV.2023.3312777 [71] Ai Y F, Liu Y H, Gao Y, Zhao C, Cheng X, Han J P, et al. PMWorld: A parallel testing platform for autonomous driving in mines. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 1402−1411 doi: 10.1109/TIV.2023.3332739 [72] Chen L, Li Y C, Huang C, Li B, Xing Y, Tian D X, et al. Milestones in autonomous driving and intelligent vehicles—part II: Perception and planning. IEEE Transactions on Systems, Man, and Cybernetics, 2023, 53(10): 6401−6415 doi: 10.1109/TSMC.2023.3283021 [73] Chen L, Zhang Y, Tian B, Ai Y, Cao D, Wang F Y. Parallel driving OS: A ubiquitous operating system for autonomous driving in CPSS. IEEE Transactions on Intelligent Vehicles, 2022, 7(4): 886−895 doi: 10.1109/TIV.2022.3223728 [74] Li L, Wang X, Wang K F, Lin Y L, Xin J M, Chen L, et al. Parallel testing of vehicle intelligence via virtual-real interaction. Science Robotics, 2019, 4(28): eaaw4106 doi: 10.1126/scirobotics.aaw4106 -
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