Survey of intelligent and connected vehicle technologies: Architectures, functions and applications
CUI Mingyang1, HUANG Heye1, XU Qing1, WANG Jianqiang1, Takaaki SEKIGUCHI2, GENG Lu2, LI Keqiang1
1. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China; 2. Hitachi(China) Research & Development Corporation, Beijing 100190, China
Abstract：The rapid development of intelligent and connected vehicles (ICV) in recent years promotes theoretical research in related fields from driving assistance to automated driving, from single-vehicle intelligent driving to multi-vehicle cooperative driving.ICV systems are expected to improve traffic safety and efficiency, but they face complex challenges in real traffic environment. This paper presents a survey of ICV technologies relating to 3 aspects:system architecture design, functional technology and application. This survey first introduces typical architectures of ICV, and then the development and challenges of three key functional technologies:perception, decision making and control, in consideration of driver-vehicle-road interactions in real traffic environment. Finally, this paper analyzes ICV applications in typical scenarios and the future development of related technologies.
[1] ITS JPO. ITS JPO strategy plan 2020-2025[EB/OL]. (2021-01-20). https://www.its.dot.gov/stratplan2020/index.htm. [2] ERTRAC. Connected automated driving roadmap[EB/OL]. (2019-08-03). https://connectedautomateddriving.eu/wp-content/uploads/2019/04/ERTRAC-CAD-Roadmap-03.04.2019-1.pdf. [3] 国家发展改革委. 关于印发《智能汽车创新发展战略》的通知[R]. 北京:国家发展改革委, 2020. National Development and Reform Commission of China. Notice on printing and distributing innovation and development strategy of intelligent vehicle[R]. Beijing:National Development and Reform Commission of China, 2020. (in Chinese) [4] 中华人民共和国工业和信息化部. 智能网联汽车技术路线图[R]. 北京:工信部, 2016. Ministry of Industry and Information Technology of China. Roadmap of intelligent and connected vehicle[R]. Beijing:Ministry of Industry and Information Technology of China, 2016. (in Chinese) [5] 李克强, 张书玮, 罗禹贡, 等. 智能环境友好型车辆的概念及其最新进展[J]. 汽车安全与节能学报, 2013, 4(2):109-120. LI K Q, ZHANG S W, LUO Y G, et al. Concept of intelligent environment-friendly vehicle and its recent development[J]. Journal of Automotive Safety and Engergy, 2013, 4(2):109-120. (in Chinese) [6] 解来卿. 基于结构共用的智能电动车辆传感器优选配置与节能控制[D]. 北京:清华大学, 2019. XIE L Q. Optimized selection of sensors configuration and energy-saving control for intelligent vehicle based on structure sharing[D]. Beijing:Tsinghua University, 2019. (in Chinese) [7] KELLERMAN H, NÉMRTH G, KOSTELEZKY J, et al. BMW 7 Series architecture[J]. ATZextra Worldwide, 2008, 13(8):30-37. [8] NAVALE V M, WILLIAMS K, LAGOSPIRIS A, et al. (R) evolution of E/E architectures[J]. SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 2015, 8(2):282-288. [9] MISENER J A, SHLADOVER S E. PATH investigations in vehicle-roadside cooperation and safety:A foundation for safety and vehicle-infrastructure integration research[C]//2006 IEEE Intelligent Transportation Systems Conference. Toronto, Canada:IEEE, 2006:9-16. [10] WANG J, CHO J S, LEE S, et al. Real time services for future cloud computing enabled vehicle networks[C]//2011 International Conference on Wireless Communications and Signal Processing (WCSP). Nanjing, China:IEEE, 2011:1-5. [11] LEE E, LEE E K, GERLA M, et al. Vehicular cloud networking:Architecture and design principles[J]. IEEE Communications Magazine, 2014, 52(2):148-155. [12] KAIWARTYA O, ABDULLAH A H, CAO Y, et al. Internet of vehicles:Motivation, layered architecture, network model, challenges, and future aspects[J]. IEEE Access, 2016(4):5356-5373. [13] 李克强, 常雪阳, 李家文, 等. 智能网联汽车云控系统及其实现[J]. 汽车工程, 2020, 42(12):1595-1605. LI K Q, CHANG X Y, LI J W, et al. Cloud control system for intelligent and connected vehicles and its application[J]. Automotive Engineering, 2020, 42(12):1595-1605. (in Chinese) [14] 李克强, 李家文, 常雪阳, 等. 智能网联汽车云控系统原理及其典型应用[J]. 汽车安全与节能学报, 2020, 11(3):261-275. LI K Q, LI J W, CHANG X Y, et al. Principles and typical applications of cloud control system for intelligent and connected vehicles[J]. Journal of Automotive Safety and Energy, 2020, 11(3):261-275. (in Chinese) [15] AMDITIS A, ANDREONE L, PAGLE K, et al. Towards the automotive HMI of the future:Overview of the AIDE-integrated project results[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3):567-578. [16] WANG J Q, HUANG H Y, LI K Q, et al. Towards the unified principles for level 5 autonomous vehicles[J/OL]. Engineering, 2021. https://doi.org/10.1016/j.eng.2020.10.018 [17] XIE S S, CHEN S T, ZHENG N N, et al. Modeling methodology of driver-vehicle-environment system dynamics in mixed driving situation[C]//2020 IEEE Intelligent Vehicles Symposium (IV). Las Vegas, USA:IEEE, 2020:1984-1991. [18] SALVUCCI D D. Modeling driver behavior in a cognitive architecture[J]. Human Factors:The Journal of the Human Factors and Ergonomics Society, 2006, 48(2):362-380. [19] GUO K, GUAN H. Modelling of driver/vehicle directional control system[J]. Vehicle System Dynamics, 1993, 22(3-4):141-184. [20] 王建强, 郑讯佳, 黄荷叶. 驾驶人驾驶决策机制遵循最小作用量原理[J]. 中国公路学报, 2020, 33(4):155-168. WANG J Q, ZHENG X J, HUANG H Y. Decision-making mechanism of the drivers following the principle of least action[J]. China Journal of Highway and Transport, 2020, 33(4):155-168. (in Chinese) [21] MARKKULA G, MADIGAN R, NATHANAEL D, et al. Defining interactions:A conceptual framework for understanding interactive behaviour in human and automated road traffic[J]. Theoretical Issues in Ergonomics Science, 2020, 21(6):728-752. [22] Safespot. Cooperative vehicles and road infrastructure for road safety[EB/OL]. 2010. http://www.safespot-eu.org. [23] REDMILL K A, KITAJIMA T, OZGUNER U. DGPS/INS integrated positioning for control of automated vehicle[C]//ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585). Oakland, USA:IEEE, 2001:172-178. [24] 张新钰, 高洪波, 赵建辉, 等. 基于深度学习的自动驾驶技术综述[J]. 清华大学学报(自然科学版), 2018, 58(4):438-444. ZHANG X Y, GAO H B, ZHAO J H, et al. Overview of deep learning intelligent driving methods[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(4):438-444. (in Chinese) [25] ZHANG H, HONG X G. Recent progresses on object detection:A brief review[J]. Multimedia Tools and Applications, 2019, 78(19):27809-27847. [26] 陈文强, 熊辉, 李克强, 等. 基于深度神经网络的行人及骑车人联合检测[J]. 汽车工程, 2018, 40(6):726-732, 725. CHEN W Q, XIONG H, LI K Q, et al. Concurrent pedestrian and cyclist detection based on deep neural networks[J]. Automotive Engineering, 2018, 40(6):726-732, 725. (in Chinese) [27] FORTMANN T E, BAR-SHALOM Y, SCHEFFE M. Multi-target tracking using joint probabilistic data association[C]//19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes. Albuquerque, USA:IEEE, 1980:807-812. [28] SADEGHIAN A, ALAHI A, SAVARESE S. Tracking the untrackable:Learning to track multiple cues with long-term dependencies[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy:IEEE, 2017:300-311. [29] ZHOU X Y, KOLTUN V, KRäHENBüHL P. Tracking objects as points[J/OL]. arXiv, (2020-08-21). http://arxiv.org/abs/2004.01177. [30] MEINHARDT T, KIRILLOV A, LEAL-TAIXE L, et al. TrackFormer:Multi-object tracking with transformers[J/OL]. arXiv, (2021-04-28). http://arxiv.org/abs/2101.02702. [31] KIM S W, QIN B X, CHONG Z J, et al. Multivehicle cooperative driving using cooperative perception:Design and experimental validation[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2):663-680. [32] VORA S, LANG A H, HELOU B, et al. PointPainting:Sequential fusion for 3D object detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA:IEEE, 2020:4603-4611. [33] CHEN X Z, MA H M, WAN J, et al. Multi-view 3D object detection network for autonomous driving[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, USA:IEEE. 2017:6526-6534. [34] CHANG X T, XU Q, LI K Q. Road network sensor deployment optimization method for road user perception[C]//Proceedings of China SAE Congress 2020. Beijing, China:China Machine Press, 2020. [35] XIE G T, GAO H B, QIAN L J, et al. Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7):5999-6008. [36] HUANG H Y, WANG J Q, FEI C, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction[J]. Science China Information Sciences, 2020, 63(9):190203. [37] LI Y, LU X Y, WANG J Q, et al. Pedestrian trajectory prediction combining probabilistic reasoning and sequence learning[J]. IEEE Transactions on Intelligent Vehicles, 2020, 5(3):461-474. [38] LIU J X, XIONG H, WANG T H, et al. Probabilistic vehicle trajectory prediction via driver characteristic and intention estimation model under uncertainty[J]. Industrial Robot, 2020(10):3526-3532. [39] WU H R, WANG L K, ZHENG S F, et al. Crossing-road pedestrian trajectory prediction based on intention and behavior identification[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). Rhodes, Greece:IEEE, 2020:1-6. [40] LI Y, ZHENG Y, MORYS B, et al. Threat assessment techniques in intelligent vehicles:A comparative survey[J]. IEEE Intelligent Transportation Systems Magazine, 2020(99):1-21. [41] WANG J Q, WU J, LI Y. The driving safety field based on driver-vehicle-road interactions[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4):2203-2214. [42] DELLING D, GOLDBERG A V, PAJOR T, et al. Customizable route planning[C]//Proceedings of the 10th International Conference on Experimental Algorithms. Berlin, Germany:Springer-Verlag, 2011:376-387. [43] URMSON C, ANHALT J, BAGNELL D, et al. Autonomous driving in urban environments:Boss and the urban challenge[J]. Journal of Field Robotics, 2008, 25(8):425-466. [44] 王晓原, 杨新月. 基于决策树的驾驶行为决策机制研究[J]. 系统仿真学报, 2008, 20(2):415-419, 448. WANG X Y, YANG X Y. Study on decision mechanism of driving behavior based on decision tree[J]. Journal of System Simulation, 2008, 20(2):415-419, 448. (in Chinese) [45] HUBMANN C, SCHULZ J, XU G, et al. A belief state planner for interactive merge maneuvers in congested traffic[C]//21st International Conference on Intelligent Transportation Systems (ITSC). Maui, USA:IEEE, 2018:1617-1624. [46] 孙浩文. 规划控制中的优化算法, 让你坐上安全又舒适的无人车[EB/OL]. (2019-10-18). https://mp.weixin.qq.com/s/G1hpXmO5243Ld6U3c-6Rvw. SUN H W. Optimization algorithm in planning and control for developing safe and comfort automated driving vehicle.[EB/OL]. (2019-10-18). https://mp.weixin.qq.com/s/G1hpXmO5243Ld6U3c-6Rvw. (in Chinese) [47] 百度Apollo. Apollo自动驾驶入门课程第8讲-规划(下)[EB/OL]. (2018-09-20). https://mp.weixin.qq.com/s/1yA-kgS_rL4o9I4OWeI4-A. Baidu Apollo. Introductory course for automated driving 8-planning (2)[EB/OL]. (2018-09-20). https://mp.weixin.qq.com/s/1yA-kgS_rL4o9I4OWeI4-A. (in Chinese) [48] BATKOVIC I, ZANON M, ALI M, et al. Real-time constrained trajectory planning and vehicle control for proactive autonomous driving with road users[C]//2019 18th European Control Conference (ECC). Naples, Italy:IEEE, 2019:256-262. [49] SALLAB A E L, ABDOU M, PEROT E, et al. Deep reinforcement learning framework for autonomous driving[J]. Electronic Imaging, Autonomous Vehicles and Machines, 2017(19):70-76. [50] BOJARSKI M, DEL TESTA D, DWORAKOWSKI D, et al. End to end learning for self-driving cars[J/OL]. arXiv, (2016-04-25). http://arxiv.org/abs/1604.07316v1. [51] SHALEV-SHWARTZ S, SHAMMAH S, SHASHUA A. On a formal model of safe and scalable self-driving cars[J/OL]. arXiv, (2018-10-27). http://arxiv.org/abs/1708.06374. [52] LIU Y C, BUCKNALL R. A survey of formation control and motion planning of multiple unmanned vehicles[J]. Robotica, 2018, 36(7):1019-1047. [53] LUO Y G, YANG G, XU M C, et al. Cooperative lane-change maneuver for multiple automated vehicles on a highway[J]. Automotive Innovation, 2019, 2(3):157-168. [54] XU Q, CAI M C, LI K Q, et al. Coordinated formation control for intelligent and connected vehicles in multiple traffic scenarios[J]. IET Intelligent Transport Systems, 2021, 15(1):159-173. [55] XU B, BAN X J, BIAN Y G, et al. Cooperative method of traffic signal optimization and speed control of connected vehicles at isolated intersections[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(4):1390-1403. [56] BIAN Y G, LI S E, REN W, et al. Cooperation of multiple connected vehicles at unsignalized intersections:Distributed observation, optimization, and control[J]. IEEE Transactions on Industrial Electronics, 2019, 67(12):10744-10754. [57] XU B, LI S E, BIAN Y G, et al. Distributed conflict-free cooperation for multiple connected vehicles at unsignalized intersections[J]. Transportation Research Part C:Emerging Technologies, 2018, 93:322-334. [58] KONG J, PFEIFFER M, SCHILDBACH G, et al. Kinematic and dynamic vehicle models for autonomous driving control design[C]//2015 IEEE Intelligent Vehicles Symposium (IV). Seoul, Korea (South):IEEE, 2015:1094-1099. [59] LI S B, LI K Q, RAJAMANI R, et al. Model predictive multi-objective vehicular adaptive cruise control[J]. IEEE Transactions on Control Systems Technology, 2010, 19(3):556-566. [60] 百度Apollo. Apollo自动驾驶入门课程第10讲-控制(下)[EB/OL]. (2018-10-25). https://mp.weixin.qq.com/s/AnWWnD3ScSW6GhrwRGpRDw. Baidu Apollo. Introductory course for automated driving 10-control (2)[EB/OL]. (2018-10-25). https://mp.weixin.qq.com/s/AnWWnD3ScSW6GhrwRGpRDw. (in Chinese) [61] LI X Y, XU N, GUO K H, et al. An adaptive SMC controller for EVs with four IWMs handling and stability enhancement based on a stability index[J]. Vehicle System Dynamics, 2020(10):1-24. [62] ZHANG F, GONZALES J, LI S E, et al. Drift control for cornering maneuver of autonomous vehicles[J]. Mechatronics, 2018, 54:167-174. [63] 郑洋. 基于四元素构架的车辆队列动力学建模与分布式控制[D]. 北京:清华大学, 2015. ZHENG Y. Dynamic modeling and distributed control of vehicular platoon under the four-component framework[D]. Beijing:Tsinghua University, 2015. (in Chinese) [64] XU B, BAN X J, BIAN Y G, et al. V2I based cooperation between traffic signal and approaching automated vehicles[C]//2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, USA:IEEE, 2017:1658-1664. [65] STERN R E, CUI S M, MONACHE M L D, et al. Dissipation of stop-and-go waves via control of autonomous vehicles:Field experiments[J]. Transportation Research Part C:Emerging Technologies, 2018, 89:205-221. [66] ZHENG Y, WANG J W, LI K Q. Smoothing traffic flow via control of autonomous vehicles[J]. IEEE Internet of Things Journal, 2020, 7(5):3882-3896. [67] PAN J, XU Q, LI K Q, et al. Controller design for V2X application under unreliable feedback channel[C]//2019 IEEE Intelligent Transportation Systems Conference (ITSC). Auckland, New Zealand:IEEE, 2019:2496-2502. [68] 常雪阳, 许庆, 李克强, 等. 通信时延与丢包下智能网联汽车控制性能分析[J]. 中国公路学报, 2019, 32(6):216-225. CHANG X Y, XU Q, LI K Q, et al. Analysis of intelligent and connected vehicle control under communication delay and packet loss[J]. China Journal of Highway and Transport, 2019, 32(6):216-225. (in Chinese) [69] 虞辰霏. 自适应驾驶人特性的前撞预警方法研究[D]. 北京:清华大学, 2013. YU C F. Study on forward collision warning method adaptive to driver characteristics[D]. Beijing:Tsinghua University, 2013. (in Chinese) [70] NA X X, COLE D J. Game-theoretic modeling of the steering interaction between a human driver and a vehicle collision avoidance controller[J]. IEEE Transactions on Human-Machine Systems, 2015, 45(1):25-38. [71] 孙逢春, 李克强. 电动汽车工程手册:第六卷[M]. 北京:机械工业出版社, 2019. SUN F C, LI K Q. Handbook of electric vehicle:Volume 6[M]. Beijing:China Machine Press, 2019. (in Chinese)
2022, Vol. 62 
