Skip to main content
SpringerLink
Log in

Intelligent and connected vehicles: Current status and future perspectives

  • Review
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs. The objective of this study is to present an overview of both the state of the art and future perspectives of key technologies that are needed for future ICVs. It is a challenging task to review all related works and predict their future perspectives, especially for such a complex and interdisciplinary area of research. This article is organized to overview the ICV key technologies by answering three questions: what are the milestones in the history of ICVs; what are the electronic components needed for building an ICV platform; and what are the essential algorithms to enable intelligent driving? To answer the first question, the article has reviewed the history and the development milestones of ICVs. For the second question, the recent technology advances in electrical/electronic architecture, sensors, and actuators are presented. For the third question, the article focuses on the algorithms in decision making, as the perception and control algorithm are covered in the development of sensors and actuators. To achieve correct decision-making, there exist two different approaches: the principle-based approach and data-driven approach. The advantages and limitations of both approaches are explained and analyzed. Currently automotive engineers are concerned more with the vehicle platform technology, whereas the academic researchers prefer to focus on theoretical algorithms. However, only by incorporating elements from both worlds can we accelerate the production of high-level ICVs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. SAE On-Road Automated Vehicle Standards Committee. Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE Standard J, 2014, 3016: 1–16

  2. Ulrich L. Top Ten Tech Cars. IEEE Spectr, 2014, 51: 38–47

    Google Scholar 

  3. Vanholme B, Gruyer D, Lusetti B, et al. Highly automated driving on highways based on legal safety. IEEE Trans Intell Transp Syst, 2013, 14: 333–347

    Google Scholar 

  4. Grisleri P, Fedriga I. The braive autonomous ground vehicle platform. IFAC Proc Volumes, 2010, 43: 497–502

    Google Scholar 

  5. Kato S, Takeuchi E, Ishiguro Y, et al. An open approach to autonomous vehicles. IEEE Micro, 2015, 35: 60–68

    Google Scholar 

  6. Geiger A, Lauer M, Moosmann F, et al. Team AnnieWAY’s entry to the 2011 grand cooperative driving challenge. IEEE Trans Intell Transp Syst, 2012, 13: 1008–1017

    Google Scholar 

  7. Urmson C, Anhalt J, Bagnell D, et al. Autonomous driving in urban environments: Boss and the urban challenge. J Field Robotics, 2008, 25: 425–466

    Google Scholar 

  8. Leonard J, How J, Teller S, et al. A perception-driven autonomous urban vehicle. J Field Robotics, 2008, 25: 727–774

    Google Scholar 

  9. Levinson J, Askeland J, Becker J, et al. Towards fully autonomous driving: Systems and algorithms. In: 2011 IEEE Intelligent Vehicles Symposium (IV). Baden-Baden: IEEE, 2011. 163–168

    Google Scholar 

  10. Montemerlo M, Becker J, Bhat S, et al. Junior: The stanford entry in the urban challenge. J Field Robotics, 2008, 25: 569–597

    Google Scholar 

  11. Bacha A, Bauman C, Faruque R, et al. Odin: Team VictorTango’s entry in the DARPA urban challenge. J Field Robotics, 2008, 25: 467–492

    Google Scholar 

  12. Merrill G P. The First One Hundred Years of American Geology. New York: Hafner Publishing Company, 1924

    Google Scholar 

  13. Kurzweil R, Richter R, Kurzweil R, et al. The Age of Intelligent Machines. Cambridge, MA: MIT Press, 1990

    Google Scholar 

  14. Grimes D M, Jones T O. Automotive radar: A brief review. Proc IEEE, 1974, 62: 804–822

    Google Scholar 

  15. Tsugawa S. Vision-based vehicles in Japan: Machine vision systems and driving control systems. IEEE Trans Ind Electron, 1994, 41: 398–405

    Google Scholar 

  16. Dickmanns E D, Graefe V. Dynamic monocular machine vision. Machine Vis Apps, 1988, 1: 223–240

    Google Scholar 

  17. Leighty R D. DARPA ALV (autonomous land vehicle) summary. Report No. ETL-R-085. Army Engineer Topographic Labs Fort Belvoir VA, 1986

    Google Scholar 

  18. Schwarz B. Mapping the world in 3D. Nat Photon, 2010, 4: 429–430

    Google Scholar 

  19. Turk M A, Morgenthaler D G, Gremban K D, et al. VITS-A vision system for autonomous land vehicle navigation. IEEE Trans Pattern Anal Machine Intell, 1988, 10: 342–361

    Google Scholar 

  20. Lowrie J W, Thomas M, Gremban K, et al. The autonomous land vehicle (ALV) preliminary road-following demonstration. In: Intelligent Robots and Computer Vision IV. Cambridge, 1985. 336–351

    Google Scholar 

  21. Barabba V, Huber C, Cooke F, et al. A multimethod approach for creating new business models: The General Motors OnStar project. Interfaces, 2002, 32: 20–34

    Google Scholar 

  22. IEEE 802.11 Working Group. Part 11-Wireless LAN medium access control (MAC) and physical layer (PHY) specifications: Higherspeed physical layer extension in the 2.4 GHz band. ANSI/IEEE Std 802.11, 1999

    Google Scholar 

  23. Montemerlo M, Thrun S, Dahlkamp H, et al. Winning the DARPA grand challenge with an AI robot. In: The National Conference on Artificial Intelligence. Boston, 2006. 982–987

    Google Scholar 

  24. Urmson C, Ragusa C, Ray D, et al. A robust approach to high-speed navigation for unrehearsed desert terrain. J Field Robotics, 2006, 23: 467–508

    MATH  Google Scholar 

  25. Jung I K, Lacroix S. High resolution terrain mapping using low altitude aerial stereo imagery. In: Proceeding of the Ninth IEEE International Conference on Computer Vision. Nice, 2003. 946

    Google Scholar 

  26. Chen M, Liu Y. Recognition and extraction high precision digital road map. In: International Conference on Information Technology: Coding and Computing (ITCC’05)-Volume II. Las Vegas, NV: IEEE, 2005. 129–134

    Google Scholar 

  27. Noyer U, Schomerus J, Mosebach H H, et al. Generating high precision maps for advanced guidance support. In: 2008 IEEE Intelligent Vehicles Symposium. Eindhoven: IEEE, 2008. 871–876

    Google Scholar 

  28. Bojarski M, Del Testa D, Dworakowshi D, et al. End to end learning for self-driving cars. arXiv:1604.07316, 2016

    Google Scholar 

  29. Xu H, Gao Y, Yu F, et al. End-to-end learning of driving models from large-scale video datasets. arXiv:preprint, 2017, https://doi.org/openaccess.thecvf.com/content_cvpr_2017/papers/Xu_End-To-End_-Learning_of_CVPR_2017_paper.pdf

    Google Scholar 

  30. Zhang J, Cho K. Query-efficient imitation learning for end-to-end autonomous driving. ArXiv:1605.06450, 2016

    Google Scholar 

  31. Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006, 18: 1527–1554

    MathSciNet  MATH  Google Scholar 

  32. Deng L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process, 2014, 3: e2

    Google Scholar 

  33. Ziegler J, Bender P, Schreiber M, et al. Making bertha drive—An autonomous journey on a historic route. IEEE Intell Transport Syst Mag, 2014, 6: 8–20

    Google Scholar 

  34. Yang D, Kong W, Li B, et al. Intelligent vehicle electrical power supply system with central coordinated protection. Chin J Mech Eng, 2016, 29: 781–791

    Google Scholar 

  35. Haas W, Langjahr P. Cross-domain vehicle control units in modern E/ E architectures. In: Bargende M, Reuss H C, Wiedemann J, Eds. Proceedings of Internationales Stuttgarter Symposium. Fachmedien Wiesbaden: Springer, 2016. 1619–1627

    Google Scholar 

  36. Zeng W, Khalid M A S, Chowdhury S. In-vehicle networks outlook: Achievements and challenges. In: IEEE Communications Surveys & Tutorials. IEEE, 2017. 1552–1571

    Google Scholar 

  37. Afsin M E, Schmidt K W, Schmidt E G. C3: Configurable CAN FD controller: Architecture, design and hardware implementation. In: 12th IEEE International Symposium on Industrial Embedded Systems. Toulouse: IEEE, 2017. 1–9

    Google Scholar 

  38. Hartwich F. CAN with flexible data-rate. In: IEEE International Conference on Communications. Gerlingen, 2012. 1–9

    Google Scholar 

  39. BOSCH. CAN With Flexible Data-Rate Specification. Version 1.0. Gerlingen: BOSCH, 2012

    Google Scholar 

  40. Matheus K, Königseder T. Automotive Ethernet. Cambridge: Cambridge University Press, 2015

    Google Scholar 

  41. FlexRay Consortium. FlexRay Communication System Protocol Specification. Version 3.0.1. 2010

  42. Engelmann B. MOST150-development and production launch from an OEM’s per-stective. In: 11th MOST Interconnectivity Conference. Seoul, 2010. 1–23

    Google Scholar 

  43. Grzemba A. MOST: The Automotive Multimedia Network, from MOST25 to MOST150. Poing: Franzis Verlag GmbH, 2011

    Google Scholar 

  44. Zeeb E. Optical data bus systems in cars: Current status and future challenges. In: Proceedings 27th European Conference on Optical Communication. Amsterdam, 2001. 70–71

    Google Scholar 

  45. Hank P, Suermann T, Müller S. Automotive ethernet, a holistic approach for a next generation in-vehicle networking standard. In: Meyer G, Ed. Advanced Microsystems for Automotive Applications. Berlin, Heidelberg: Springer, 2012. 79–89

    Google Scholar 

  46. Patsakis C, Dellios K. Securing in-vehicle communication and redefining the role of automotive immobilizer. In: International Conference on Security and Cryptography. Rome, 2012. 221–226

    Google Scholar 

  47. Patsakis C, Dellios K, Bouroche M. Towards a distributed secure invehicle communication architecture for modern vehicles. Comput Security, 2014, 40: 60–74

    Google Scholar 

  48. Misener J A, Biswas S, Larson G. Development of V-to-X systems in North America: The promise, the pitfalls and the prognosis. Comput Networks, 2011, 55: 3120–3133

    Google Scholar 

  49. DhilipKumar V, Kandar D, Sarkar C K. Enhancement of inter-vehicular communication to optimize the performance of 3G/4G-VANET. In: International Conference on Optical Imaging Sensor and Security. Coimbatore, 2013. 1–5

    Google Scholar 

  50. Smith S, Razo M. Using traffic microsimulation to assess deployment strategies for the connected vehicle safety pilot. J Intelligent Transpation Syst, 2016, 20: 66–74

    Google Scholar 

  51. Toulminet G, Boussuge J, Laurgeau C. Comparative synthesis of the 3 main European projects dealing with Cooperative Systems (CVIS, SAFESPOT and COOPERS) and description of COOPERS Demonstration Site 4. In: 11th International IEEE Conference on Intelligent Transportation Systems. Beijing, 2008. 809–814

    Google Scholar 

  52. Stahlmann R, Festag A, Tomatis A, et al. Starting European field tests for Car-2-X communication: The DRIVE C2X framework. In: 18th ITS World Congress and Exhibition. Orlando, FL, 2011. 1–9

    Google Scholar 

  53. Shenjiang L D W. The design of the controller on automobile taillight based on AT89S52 (in Chinese). Foreign Electronic Meas Technol, 2010: 60–63

    Google Scholar 

  54. Jin X U, Zhong F M. Automotive air conditioning control system based on STC12C5A60S2 singlechip (in Chinese). Auto Electric Parts, 2014, 6: 14–16

    Google Scholar 

  55. Gan H, Zhang J, Lu Q. Study on operating mode control of hybrid electric vehicle based on the high performance32-Bit SCM MPC555. Automob Technol, 2004, 11: 9–12

    Google Scholar 

  56. Yu X, Chen B, Ji T. DSP software design for EQ effect of car multimedia system. Microcompute Its Applications, 2011, 30: 47–50

    Google Scholar 

  57. Yu Y, Fu Z, Rao L, et al. DSP-based advance collision warning system. Process Automat Instrum, 2009, 30: 11–13

    Google Scholar 

  58. Yu J Q, Chen Z Z, Liang P. The design and implementation signal processing system of the automotive collision avoidance based on TMS320vc5402 (in Chinese). Microcomp Inf, 2007: 266–267

    Google Scholar 

  59. Lindholm E, Nickolls J, Oberman S, et al. NVIDIA Tesla: A unified graphics and computing architecture. IEEE Micro, 2008, 28: 39–55

    Google Scholar 

  60. Wübbena G, Bagge A. GNSS multi-station adjustment for permanent deformation analysis networks. In: Symposlum on Geodesy for Geotechnical & Structural Engineering of the IAG Special Commission. Eisenstadt, 1998. 139–144

    Google Scholar 

  61. Jouppi N P, Young C, Patil N, et al. In-datacenter performance analysis of a tensor processing unit. In: 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture. Toronto, ON: IEEE, 2017. 1–12

    Google Scholar 

  62. Chevitarese D S, Dos Santos M N. Real-time face tracking and recognition on IBM neuromorphic chip. In: 2016 IEEE International Symposium on Multimedia. San Jose, CA: IEEE, 2016. 667–672

    Google Scholar 

  63. Rethinagiri S K, Palomar O, Moreno J A, et al. System-level power & energy estimation methodology and optimization techniques for CPU-GPU based mobile platforms. In: 2014 IEEE 12th Symposium on Embedded Systems for Real-time Multimedia. Greater Noida, 2014. 118–127

    Google Scholar 

  64. Liu B L, Sun Y B. OSEK/VDX: An open-architectured platform of vehicle electronics system. Acta Armamentarll the Volume of Tank, Armored Vehicle Engine, 2002, 2: 61–64

    Google Scholar 

  65. Guettier C, Bradai B, Hochart F, et al. Standardization of generic architecture for autonomous driving: A reality check. In: Langheim J, Ed. Energy Consumption and Autonomous Driving. Lecture Notes in Mobility. Cham: Springer, 2016. 57–68

    Google Scholar 

  66. Aly S. Consolidating AUTOSAR with complex operating systems (AUTOSAR on Linux). SAE Technical Paper 2017–01–1617, 2017

    Google Scholar 

  67. Leitner A, Ochs T, Bulwahn L, et al. Open dependable power computing platform for automated driving. In: Watzenig D, Horn M, Eds. Automated Driving. Cham: Springer, 2017. 353–367

    Google Scholar 

  68. Traub M, Maier A, Barbehon K L. Future automotive architecture and the impact of IT trends. IEEE Softw, 2017, 34: 27–32

    Google Scholar 

  69. Sagstetter F, Lukasiewycz M, Steinhorst S, et al. Security challenges in automotive hardware/software architecture design. In: Proceedings of the Conference on Design, Automation and Test in Europe. Grenoble, 2013. 458–463

    Google Scholar 

  70. Risack R, Mohler N, Enkelmann W. A video-based lane keeping assistant. In: Proceedings of the IEEE Intelligent Vehicles Symposium. Dearborn, MI: IEEE, 2000

    Google Scholar 

  71. Kesting A, Treiber M, Schönhof M, et al. Adaptive cruise control design for active congestion avoidance. Transpat Res Part C-Emerg Technol, 2008, 16: 668–683

    Google Scholar 

  72. Kim S W, Qin B, Chong Z J, et al. Multivehicle cooperative driving using cooperative perception: Design and experimental validation. IEEE Trans Intell Transp Syst, 2015, 16: 663–680

    Google Scholar 

  73. Dagan E, Mano O, Stein G P, et al. Forward collision warning with a single camera. In: IEEE Intelligent Vehicles Symposium. Parma: IEEE, 2004. 37–42

    Google Scholar 

  74. Leung K Y K, Barfoot T D, Liu H H T. Decentralized cooperative slam for sparsely-communicating robot networks: A centralizedequivalent approach. J Intell Robot Syst, 2012, 66: 321–342

    MATH  Google Scholar 

  75. Perumal D G, Saravanakumar G, Subathra B, et al. Nonlinear state estimation based predictive path planning algorithm using infrastructure-to-vehicle (I2V) communication for intelligent vehicle. In: Proceedings of the Second International Conference on Emerging Research in Computing, Information, Communication and Applications (ERCICA 2014). NMIT, Yelahanka, Bangalore, 2014. 243–248

    Google Scholar 

  76. Sawant N R. Longitudinal vehicle speed controller for autonomous driving in urban stop-and-go traffic situations. Dissertation of Masteral Degree. Columbus, OH: The Ohio State University, 2010

    Google Scholar 

  77. Eskandarian A. Handbook of Intelligent Vehicles. London: Springer, 2012

    Google Scholar 

  78. Marek J, Trah H P, Suzuki Y, et al. Sensors for Automotive Technology. Weinheim: Wiley-VCH, 2003

    Google Scholar 

  79. Landau H, Vollath U, Chen X. Virtual reference station systems. J GPS, 2002, 1: 137–143

    Google Scholar 

  80. Brown N, Geisler I, Troyer L. RTK rover performance using the master-auxiliary concept. Positioning, 2006, 5: 135–144

    Google Scholar 

  81. Wanninger L. Improved ambiguity resolution by regional differential modelling of the ionosphere. In: Proceedings of the ION GPS 95. Palm Springs, 1995. 55–62

    Google Scholar 

  82. Bertozzi M, Broggi A, Fascioli A. Vision-based intelligent vehicles: State of the art and perspectives. Robotics Autonomous Syst, 2000, 32: 1–16

    MATH  Google Scholar 

  83. Maurer M, Behringer R, Furst S, et al. A compact vision system for road vehicle guidance. In: Proceedings of 13th International Conference on Pattern Recognition. Vienna: IEEE, 1996. 313–317

    Google Scholar 

  84. Bertozzi M, Broggi A, Conte G, et al. Vision-based automated vehicle guidance: The experience of the ARGO vehicle. Tecniche di Intelligenza Artificiale e Pattern Recognition per la Visione Artificiale, 1998: 35–40

    Google Scholar 

  85. Broggi A, Bertozzi M, Fascioli A. Architectural issues on visionbased automatic vehicle guidance: The experience of the ARGO project. Real-Time Imag, 2000, 6: 313–324

    MATH  Google Scholar 

  86. Campbell M, Egerstedt M, How J P, et al. Autonomous driving in urban environments: Approaches, lessons and challenges. Philos Trans R Soc A-Math Phys Eng Sci, 2010, 368: 4649–4672

    Google Scholar 

  87. Göhring D, Latotzky D, Wang M, et al. Semi-autonomous car control using brain computer interfaces. In: Lee S, Cho H, Yoon KJ, Eds. Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing. Berlin, Heidelberg: Springer, 393–408

  88. van Nunen E, Koch R, Elshof L, et al. Sensor safety for the european truck platooning challenge. In: 23rd World Congress on Intelligent Transport Systems. Melbourne, 2016. 306–311

    Google Scholar 

  89. Johnson D G. Development of a high resolution MMW radar employing an antenna with combined frequency and mechanical scanning. In: 2008 IEEE Radar Conference. Rome: IEEE, 2008. 1–5

    Google Scholar 

  90. Han S, Wang X, Xu L, et al. Frontal object perception for Intelligent Vehicles based on radar and camera fusion. In: 35th Chinese Control Conference. Chengdu, China: IEEE, 2016

    Google Scholar 

  91. Song S, Chandraker M. Robust scale estimation in real-time monocular SFM for autonomous driving. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH: IEEE, 2014. 1566–1573

    Google Scholar 

  92. Park K Y, Hwang S Y. Robust range estimation with a monocular camera for vision-based forward collision warning system. Sci World J, 2014, 2014: 1–9

    Google Scholar 

  93. Dong Y, Hu Z, Uchimura K, et al. Driver inattention monitoring system for intelligent vehicles: A review. IEEE Trans Intell Transp Syst, 2011, 12: 596–614

    Google Scholar 

  94. Tawari A, Sivaraman S, Trivedi M M, et al. Looking-in and lookingout vision for urban intelligent assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings. Dearborn, MI: IEEE, 2014. 115–120

    Google Scholar 

  95. Klette R, Kruger N, Vaudrey T, et al. Performance of correspondence algorithms in vision-based driver assistance using an online image sequence database. IEEE Trans Veh Technol, 2011, 60: 2012–2026

    Google Scholar 

  96. Lazaros N, Sirakoulis G C, Gasteratos A. Review of stereo vision algorithms: From software to hardware. Int J Optomechatron, 2008, 2: 435–462

    Google Scholar 

  97. Tippetts B, Lee D J, Lillywhite K, et al. Review of stereo vision algorithms and their suitability for resource-limited systems. J Real- Time Image Proc, 2016, 11: 5–25

    Google Scholar 

  98. Benet G, Blanes F, Simae J E, et al. Using infrared sensors for distance measurement in mobile robots. Robotics Autonomous Syst, 2002, 40: 255–266

    Google Scholar 

  99. Takagi K, Morikawa K, Ogawa T, et al. Road environment recognition using on-vehicle LIDAR. In: 2006 IEEE Intelligent Vehicles Symposium. Tokyo: IEEE, 2006. 120–125

    Google Scholar 

  100. Himmelsbach M, Hundelshausen F V, Wuensche H J. Fast segmentation of 3d point clouds for ground vehicles. In: 2010 IEEE Intelligent Vehicles Symposium (IV). San Diego, CA: IEEE, 2010. 560–565

    Google Scholar 

  101. Lee J H, Tsubouchi T, Yamamoto K, et al. People tracking using a robot in motion with laser range finder. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China: IEEE, 2006. 2936–2942

    Google Scholar 

  102. Brscic D, Kanda T, Ikeda T, et al. Person tracking in large public spaces using 3-D range sensors. IEEE Trans Human-Mach Syst, 2013, 43: 522–534

    Google Scholar 

  103. Pathak K, Birk A, Vaskevicius N, et al. Online three-dimensional SLAM by registration of large planar surface segments and closedform pose-graph relaxation. J Field Robotics, 2010, 27: 52–84

    Google Scholar 

  104. Zhang J, Singh S. LOAM: Lidar odometry and mapping in real-time. In: Robotics: Science and Systems. Berkeley, CA: 2014. 9

    Google Scholar 

  105. Park J, Kim H, Tai Y W, et al. High quality depth map upsampling for 3D-TOF cameras. In: 2011 International Conference on Computer Vision. Barcelona: IEEE, 2011. 1623–1630

    Google Scholar 

  106. Hwang S, Kim N, Choi Y, et al. Fast multiple objects detection and tracking fusing color camera and 3D LIDAR for intelligent vehicles. In: 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). Xi’an, China: IEEE, 2016. 234–239

    Google Scholar 

  107. Zhong Y, Wang S, Xie S, et al. 3D scene reconstruction with sparse LiDAR data and monocular image in single frame. SAE Int J Passeng Cars-Electron Electr Syst, 2017, 11: 48–56

    Google Scholar 

  108. Hofmann U, Senger F, Soerensen F, et al. Biaxial resonant 7mm- MEMS mirror for automotive LIDAR application. In: 2012 International Conference on Optical MEMS and Nanophotonics. Banff, AB: IEEE, 2012. 150–151

    Google Scholar 

  109. Ye L, Zhang G, You Z. 5 V compatible two-axis PZT driven MEMS scanning mirror with mechanical leverage structure for miniature LiDAR application. Sensors, 2017, 17: 521

    Google Scholar 

  110. McManamon P F, Bos P J, Escuti M J, et al. A review of phased array steering for narrow-band electrooptical systems. Proc IEEE, 2009, 97: 1078–1096

    Google Scholar 

  111. Yoo B W, Megens M, Chan T, et al. Optical phased array using high contrast gratings for two dimensional beamforming and beamsteering. Opt Express, 2013, 21: 12238–12248

    Google Scholar 

  112. Sugimoto S, Tateda H, Takahashi H, et al. Obstacle detection using millimeter-wave radar and its visualization on image sequence. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. Cambridge, UK: IEEE, 2004. 342–345

    Google Scholar 

  113. Song Y, Nuske S, Scherer S. A multi-sensor fusion MAV state estimation from long-range stereo, IMU, GPS and barometric sensors. Sensors, 2016, 17: 11

    Google Scholar 

  114. Chen Z. Bayesian filtering: From Kalman filters to particle filters, and beyond. Statistics, 2003, 182: 1–69

    Google Scholar 

  115. Zhang Z, Li Y, Wang F, et al. A novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. Sensors, 2016, 16: 848

    Google Scholar 

  116. Weiß C. V2X communication in Europe—From research projects towards standardization and field testing of vehicle communication technology. Comput Networks, 2011, 55: 3103–3119

    Google Scholar 

  117. Kalman R E. A new approach to linear filtering and prediction problems. J Basic Eng, 1960, 82: 35–45

    Google Scholar 

  118. Arulampalam M S, Maskell S, Gordon N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process, 2002, 50: 174–188

    Google Scholar 

  119. Ghods A, Severi S, Abreu G. Localization in V2X communication networks. In: 2016 IEEE Intelligent Vehicles Symposium (IV). Gothenburg: IEEE, 2016. 5–9

    Google Scholar 

  120. Rohani M, Gingras D, Vigneron V, et al. A new decentralized Bayesian approach for cooperative vehicle localization based on fusion of GPS and VANET based inter-vehicle distance measurement. IEEE Intell Transp Syst Mag, 2015, 7: 85–95

    Google Scholar 

  121. Obst M, Hobert L, Reisdorf P. Multi-sensor data fusion for checking plausibility of V2V communications by vision-based multiple-object tracking. In: 2014 IEEE Vehicular Networking Conference (VNC). Paderborn: IEEE, 2014. 143–150

    Google Scholar 

  122. Liu W, Kim S W, Marczuk K, et al. Vehicle motion intention reasoning using cooperative perception on urban road. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). Qingdao, China: IEEE, 2014, 424–430

    Google Scholar 

  123. Kim S W, Liu W, Ang M H, et al. The impact of cooperative perception on decision making and planning of autonomous vehicles. IEEE Intell Transp Syst Mag, 2015, 7: 39–50

    Google Scholar 

  124. Luthardt S, Han C, Willert V, et al. Efficient graph-based V2V free space fusion. In: Intelligent Vehicles Symposium (IV). Los Angeles, CA: IEEE

  125. Bétaille D, Toledo-Moreo R. Creating enhanced maps for lane-level vehicle navigation. IEEE Trans Intell Transp Syst, 2010, 11: 786–798

    Google Scholar 

  126. Jo K, Sunwoo M. Generation of a precise roadway map for autonomous cars. IEEE Trans Intell Transp Syst, 2014, 15: 925–937

    Google Scholar 

  127. Joshi A, James M R. Generation of accurate lane-level maps from coarse prior maps and lidar. IEEE Intell Transp Syst Mag, 2015, 7: 19–29

    Google Scholar 

  128. Billah M, Maskooki A, Rahman F, et al. Roadway feature mapping from point cloud data: A graph-based clustering approach. In: 2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, CA: IEEE, 2017

    Google Scholar 

  129. Guan H, Li J, Yu Y, et al. Using mobile laser scanning data for automated extraction of road markings. ISPRS J Photogramm Remote Sens, 2014, 87: 93–107

    Google Scholar 

  130. Zeng W, Church R L. Finding shortest paths on real road networks: The case for A*. Int J Geogr Inf Sci, 2009, 23: 531–543

    Google Scholar 

  131. Vu A, Ramanandan A, Chen A, et al. Real-time computer vision/ DGPS-aided inertial navigation system for lane-level vehicle navigation. IEEE Trans Intell Transp Syst, 2012, 13: 899–913

    Google Scholar 

  132. Levinson J, Montemerlo M, Thrun S. Map-based precision vehicle localization in urban environments. In: Robotics: Science and Systems. Georgia, 2007. 1

    Google Scholar 

  133. Wolcott R W, Eustice R M. Visual localization within LIDAR maps for automated urban driving. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, IL: IEEE, 2014. 176–183

    Google Scholar 

  134. Xu Y, John V, Mita S, et al. 3D point cloud map based vehicle localization using stereo camera. In: 2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, CA: IEEE, 2017. 487–492

    Google Scholar 

  135. Crane C D. The 2005 DARPA grand challenge. In: International Symposium on Computational Intelligence in Robotics and Automation. Jacksonville, 2007. 1

    Google Scholar 

  136. Hodge K E, Kellogg Y. Proceedings of the F-8 digital fly-by-wire and supercritical wing first flight’s 20th anniversary celebration. Volume 1. Technical Report NASA-CP-3256-Vol-1. Edwards, CA: National Aeronautics and Space Administration, Dryden Flight Research Center, 1996

    Google Scholar 

  137. Stjärne K, Werner P. Brake by wire system for construction vehicles. Dissertation of Masteral Degree. Göteborg: Chalmers University of Technology, 2014

    Google Scholar 

  138. He L, Ma B, Zong C. Fault-tolerance control strategy for the steering wheel angle sensor of a steer-by-wire vehicle. Automot Eng, 2015, 37: 327–330, 345

    Google Scholar 

  139. Fahimi F. Full drive-by-wire dynamic control for four-wheel-steer all-wheel-drive vehicles. Vehicle Syst Dyn, 2013, 51: 360–376

    Google Scholar 

  140. Janbakhsh A A, Bayani Khaknejad M, Kazemi R. Simultaneous vehicle-handling and path-tracking improvement using adaptive dynamic surface control via a steer-by-wire system. Proc Instit Mech Eng Part D-J Automobile Eng, 2013, 227: 345–360

    Google Scholar 

  141. Abeysiriwardhana W S P, Abeykoon A H S. Simulation of brake by wire system with dynamic force control. In: 7th International Conference on Information and Automation for Sustainability. Colombo: IEEE, 2014. 1–6

    Google Scholar 

  142. Pisu P, Serrani A, You S, et al. Adaptive threshold based diagnostics for steer-by-wire systems. J Dyn Sys Meas Control, 2006, 128: 428–435

    Google Scholar 

  143. Cetin A E, Adli M A, Barkana D E, et al. Compliant control of steerby- wire systems. In: 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Singapore: IEEE, 2009. 636–643

    Google Scholar 

  144. Kang J, Hindiyeh R Y, Moon S W, et al. Design and testing of a controller for autonomous vehicle path tracking using GPS/INS sensors. In: Proceedings of the 17th IFAC World Congress. Seoul, 2008. 6–11

    Google Scholar 

  145. Xiong L, Teng G W, Yu Z P, et al. Novel stability control strategy for distributed drive electric vehicle based on driver operation intention. Int J Automot Technol, 2016, 17: 651–663

    Google Scholar 

  146. Yin G D, Jin X J, Zhang Y. Overview for chassis vehicle dynamics control of distributed drive electric vehicle. J Chongqing Univ Technol, 2016: 13–19

    Google Scholar 

  147. Liu H, Chen X, Wang X. Overview and prospects on distributed drive electric vehicles and its energy saving strategy. Prz Elektrotechniczn, 2012, 88: 122–125

    Google Scholar 

  148. Wilwert C, Song Y Q, Simonot-Lion F, et al. Evaluating quality of service and behavioral reliability of steer-by-wire systems. In: 9th IEEE International Conference on Emerging Technologies and Factory Automation-EFTA’2003. Lisbonne: IEEE, 2003. 193–200

    Google Scholar 

  149. He L, Xiang H O, Chen D X, et al. Emergency obstacle avoidance control method based on driver steering intention recognition for steer-by-wire vehicle. In: Liu X H, Zhang K F, Li M Z, Eds. Manufacturing Process and Equipment. Volumes 694–697. Advanced Materials Research. Switzerland: Trans Tech Publications, 2013. 2738–2741

    Google Scholar 

  150. Hirano Y. Integrated vehicle control of an in-wheel-motor vehicle to optimize vehicle dynamics and energy consumption. In: 2012 10th World Congress on Intelligent Control and Automation. Beijing, China: IEEE, 2012. 2335–2339

    Google Scholar 

  151. Pei X, Zhou Y, Sheng Z. Torque ripple suppression of a new in-wheel motor based on quantum genetic algorithm. In: 23rd International Conference on Mechatronics and Machine Vision in Practice. Nanjing, China: IEEE, 2016. 1–6

    Google Scholar 

  152. Thrun S, Montemerlo M, Dahlkamp H, et al. Stanley: The robot that won the DARPA grand challenge. J Field Robotics, 2006, 23: 661–692

    Google Scholar 

  153. Sivaraman S, Trivedi M M. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Trans Intell Transp Syst, 2013, 14: 1773–1795

    Google Scholar 

  154. Gwon G P, Hur W S, Kim S W, et al. Generation of a precise and efficient lane-level road map for intelligent vehicle systems. IEEE Trans Veh Technol, 2017, 66: 4517–4533

    Google Scholar 

  155. Khodayari A, Ghaffari A, Ameli S, et al. A historical review on lateral and longitudinal control of autonomous vehicle motions. In: 2nd International Conference on Mechanical and Electrical Technology. Singapore: IEEE, 2010. 421–429

    Google Scholar 

  156. Souissi O, Benatitallah R, Duvivier D, et al. Path planning: A 2013 survey. In: Proceedings of 2013 International Conference on Industrial Engineering and Systems Management. Rabat: IEEE, 2013. 1–8

    Google Scholar 

  157. Katrakazas C, Quddus M, Chen W H, et al. Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions. Transpat Res Part C-Emerg Technol, 2015, 60: 416–442

    Google Scholar 

  158. Gonzalez D, Perez J, Milanes V, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst, 2016, 17: 1135–1145

    Google Scholar 

  159. Veres S M, Molnar L, Lincoln N K, et al. Autonomous vehicle control systems: A review of decision making. Proc Inst Mech Eng Part I-J Syst Control Eng, 2011, 225: 155–195

    Google Scholar 

  160. Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. Robomech J, 2014, 1: 1

    Google Scholar 

  161. Bila C, Sivrikaya F, Khan M A, et al. Vehicles of the future: A survey of research on safety issues. IEEE Trans Intell Transp Syst, 2017, 18: 1046–1065

    Google Scholar 

  162. Eichenbaum H. A cortical-hippocampal system for declarative memory. Nat Rev Neurosci, 2000, 1: 41

    Google Scholar 

  163. Cohen M D, Bacdayan P. Organizational routines are stored as procedural memory: Evidence from a laboratory study. Organ Sci, 1994, 5: 554–568

    Google Scholar 

  164. Weng J. Artificial intelligence: Autonomous mental development by robots and animals. Science, 2001, 291: 599–600

    Google Scholar 

  165. Mitchell T M. Machine Learning. Burr Ridge, IL: McGraw Hill, 1997. 870–877

    MATH  Google Scholar 

  166. Samuel A L. Some studies in machine learning using the game of checkers. IBM J Res Dev, 1959, 3: 210–229

    MathSciNet  Google Scholar 

  167. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Rev, 1958, 65: 386–408

    Google Scholar 

  168. Ackley D, Hinton G, Sejnowski T. A learning algorithm for Boltzmann machines. Cogn Sci, 1985, 9: 147–169

    Google Scholar 

  169. Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436–444

    Google Scholar 

  170. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: PereIra F, Burges C J C, Bottou L, et al, Eds. Advances in Neural Information Processing Systems 25. Cambridge, MA: MIT Press, 2012. 1097–1105

    Google Scholar 

  171. Everingham M, Eslami S M A, Van Gool L, et al. The pascal visual object classes challenge: A retrospective. Int J Comput Vis, 2015, 111: 98–136

    Google Scholar 

  172. Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529: 484–489

    Google Scholar 

  173. Pacejka H. Tire and Vehicle Dynamics. 3rd Ed. Amsterdam: Elsevier, 2012

    Google Scholar 

  174. Moravec H. Sensor Fusion in Certainty Grids for Mobile Robots. Berlin Heidelberg: Springer, 1989. 61–74

    Google Scholar 

  175. Hoskins S R, Tefend M F. Steering angle sensor. USA Patent, US8164327, 2012

    Google Scholar 

  176. Santana E, Hotz G. Learning a driving simulator. arXiv:1608.01230, 2016

    Google Scholar 

  177. Bojarski M, Yeres P, Choromanska A, et al. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv:1704.07911, 2017

    Google Scholar 

  178. Chen C, Seff A, Kornhauser A, et al. Deep driving: Learning affordance for direct perception in autonomous driving. In: IEEE International Conference on Computer Vision. Santiago, 2015. 2722–2730

    Google Scholar 

  179. Liu W, Li Z, Li L, et al. Parking like a human: A direct trajectory planning solution. IEEE Trans Intell Transp Syst, 2017, 99: 1–10

    Google Scholar 

  180. Yang S, Wang W, Liu C, et al. Feature analysis and selection for training an end-to-end autonomous vehicle controller using the deep learning approach. In: 2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, CA: 2017. 1033–1038

    Google Scholar 

  181. Kisacanin B. Deep learning for autonomous vehicles. In: IEEE International Symposium on Multiple-Valued Logic. San Francisco, 2017. 142

    Google Scholar 

  182. Shapiro D. Accelerating the race to autonomous cars. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Novi Sad, 2016. 415

    Google Scholar 

  183. Friedman N, Geiger D, Goldszmidt M. Goldszmidt. Bayesian network classifiers. Mach Learn, 1997, 29: 131–163

    MATH  Google Scholar 

  184. Ontañón S, Montaña J L, Gonzalez A J. A dynamic bayesian network framework for learning from observation. In: Bielza C, Eds. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science, Vol 8109. Berlin Heidelberg: Springer, 2013. 373–382

    Google Scholar 

  185. Forbes J, Huang T, Kanazawa K, et al. The Batmobile: Towards a Bayesian automated taxi. In: International Joint Conference on Artificial Intelligence. Montreal, 1995. 1878–1885

    Google Scholar 

  186. Dan N V, Kameyama M. Bayesian-networks-based motion estimation for a highly-safe intelligent vehicle. In: 2006 SICE-ICASE International Joint Conference. Busan, 2007. 6023–6026

    Google Scholar 

  187. Hamlet A J, Crane C D. Joint belief and intent prediction for collision avoidance in autonomous vehicles. arXiv:1504.00060, 2015

    Google Scholar 

  188. Eilers M, Möbus C. Learning of a bayesian autonomous driver mixture-of-behaviors (BAD MoB) model. In: International Conference on Applied Human Factors and Ergonomics. Boca Raton, 2011. 436–445

    Google Scholar 

  189. Möbus C, Eilers M. Further steps towards driver modeling according to the bayesian programming approach. In: Duffy V G, Ed. Digital Human Modeling. ICDHM 2009. Lecture Notes in Computer Science, Vol 5620. Berlin Heidelberg: Springer, 2009. 413–422

    Google Scholar 

  190. Eilers M, Möbus C, Tango F, et al. The learning of longitudinal human driving behavior and driver assistance strategies. Transpation Res Part F-Traffic Psychology Behaviour, 2013, 21: 295–314

    Google Scholar 

  191. Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 2005

    Google Scholar 

  192. Kaelbling L P, Littman M L, Moore A W. Reinforcement learning: A survey. J Artif Intell Res, 1996, 4: 237–285

    Google Scholar 

  193. Koutník J, Schmidhuber J, Gomez F. Online evolution of deep convolutional network for vision-based reinforcement learning. In: International Conference on Simulation of Adaptive Behavior. Castellón, 2014. 260–269

    Google Scholar 

  194. Xia W, Li H, Li B. A control strategy of autonomous vehicles based on deep reinforcement learning. In: International Symposium on Computational Intelligence and Design. Zhejiang, China, 2017. 198–201

    Google Scholar 

  195. Xiong X, Wang J, Zhang F, et al. Combining deep reinforcement learning and safety based control for autonomous driving. ar- Xiv:1612.00147, 2016

    Google Scholar 

  196. Chae H, Kang C M, Kim B D, et al. Autonomous braking system via deep reinforcement learning. In: IEEE International Conference on Intelligent Transportation Systems. Yokohama, 2017. 1–6

    Google Scholar 

  197. Isele D, Rahimi R, Cosgun A, et al. Navigating occluded intersections with autonomous vehicles using deep reinforcement learning. arXiv:1705.01196, 2018

    Google Scholar 

  198. Koenig S, Simmons R G. Xavier: A robot navigation architecture based on partially observable Markov decision process models. In: Artificial Intelligence Based Mobile Robotics: Case Studies of Successful Robot Systems. Cambridge, MA: MIT Press, 1998. 91–122

    Google Scholar 

  199. Liu W, Kim S W, Pendleton S, et al. Situation-aware decision making for autonomous driving on urban road using online POMDP. In: Intelligent Vehicles Symposium. Seoul, 2015. 1126–1133

    Google Scholar 

  200. Agussurja L, Lau H C. A POMDP model for guiding taxi cruising in a congested urban city. In: Batyrshin I, Sidorov G, Eds. Advances in Artificial Intelligence. MICAI 2011. Lecture Notes in Computer Science, vol 7094. Berlin, Heidelberg: Springer, 2011, 7094. 415–428

    Google Scholar 

  201. Brechtel S, Gindele T, Dillmann R. Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs. In: IEEE International Conference on Intelligent Transportation Systems. Qingdao, China, 2014. 392–399

    Google Scholar 

  202. Amato C, Konidaris G D, Kaelbling L P. Planning with macro-actions in decentralized POMDPs. In: International Conference on Autonomous Agents and Multi-Agent Systems. Paris, 2014. 331–333

    Google Scholar 

  203. Deng J, Dong W, Socher R, et al. ImageNet: A large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition. Miami, 2009. 248–255

    Google Scholar 

  204. Griffin G, Holub A, Perona P. Caltech-256 object category dataset. Technical Report 7694. Caltrch: California Institute of Technology, 2007

    Google Scholar 

  205. Lin TY, Maire M, Belongie S, et al. Microsoft COCO: Common objects in context. In: Fleet D, Pajdla T, Schiele B, et al, Eds. Computer Vision—ECCV 2014. Lecture Notes in Computer Science, Vol. 8693. Cham: Springer, 2014. 740–755

    Google Scholar 

  206. Chen C, Self A, Kornhauser A, et al. Deepdriving: Learning affordance for direct perception in autonomous driving. In: IEEE International Conference on Computer Vision. Santiago: IEEE, 2015. 2722–2730

    Google Scholar 

  207. Maddern W, Pascoe G, Linegar C, et al. 1 year, 1000 km: The Oxford RobotCar dataset. Int J Robotics Res, 2017, 36: 3–15

    Google Scholar 

  208. Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, 2012. 3354–3361

    Google Scholar 

  209. Cordts M, Mohamed O, Sebastian R, et al. The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 2016. 3213–3223

    Google Scholar 

  210. de Charette R, Nashashibi F. Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates. In: 2009 IEEE Intelligent Vehicles Symposium. Xi’an, China: IEEE, 2009. 358–363

    Google Scholar 

  211. Wu B, Nevatia R. Cluster boosted tree classifier for multi-view, multi-pose object detection. In: IEEE 11th International Conference on 2007 ICCV. Rio de Janeiro: IEEE, 2007. 1–8

    Google Scholar 

  212. Ess A, Leibe B, Schindler K, et al. A mobile vision system for robust multi-person tracking. In: Computer Vision and Pattern Recognition, 2008. Anchorage: IEEE, 2008. 1–8

    Google Scholar 

  213. Varaiya P. Smart cars on smart roads: problems of control. IEEE Trans Automat Contr, 1993, 38: 195–207

    MathSciNet  Google Scholar 

  214. Wei J, Snider J M, Gu T, et al. A behavioral planning framework for autonomous driving. In: Intelligent Vehicles Symposium Proceedings. Dearborn, MI: IEEE, 2014. 458–464

    Google Scholar 

  215. Noh S, An K. Decision-making framework for automated driving in highway environments. IEEE Trans Intell Transp Syst, 2017, 19: 58–71

    Google Scholar 

  216. Buehler M, Iagnemma K, Singh S. The DARPA Urban Challenge. Tracts in Advanced Robotics. Berlin: Springer, 2010

    Google Scholar 

  217. Reeds J, Shepp L. Optimal paths for a car that goes both forwards and backwards. Pac J Math, 1990, 145: 367–393

    MathSciNet  Google Scholar 

  218. Fraichard T, Scheuer A. From reeds and shepp’s to continuous-curvature paths. IEEE Trans Robot, 2004, 20: 1025–1035

    Google Scholar 

  219. Petrov P, Nashashibi F. Modeling and nonlinear adaptive control for autonomous vehicle overtaking. IEEE Trans Intell Transp Syst, 2014, 15: 1643–1656

    Google Scholar 

  220. Rastelli J P, Lattarulo R, Nashashibi F. Dynamic trajectory generation using continuous-curvature algorithms for door to door assistance vehicles. In: Intelligent Vehicles Symposium Proceedings. Dearborn, MI: IEEE, 2014. 510–515

    Google Scholar 

  221. Dolgov D, Thrun S, Montemerlo M, et al. Path planning for autonomous vehicles in unknown semi-structured environments. Int J Robotics Res, 2010, 29: 485–501

    Google Scholar 

  222. Gu T, Dolan J M. On-road motion planning for autonomous vehicles. In: International Conference on Intelligent Robotics and Applications. Montreal, Quebec: Springer, 2012. 588–597

    Google Scholar 

  223. Ziegler J, Bender P, Schreiber M, et al. Making bertha drive-an autonomous journey on a historic route. IEEE Intell Transport Syst Mag, 2014, 6: 8–20

    Google Scholar 

  224. Cremean L B, Foote T B, Gillula J H, et al. Alice: An informationrich autonomous vehicle for high-speed desert navigation. In: Buehler M, Iagnemma K, Singh S, Eds. The 2005 DARPA Grand Challenge. Springer Tracts in Advanced Robotics, Vol 36. Berlin, Heidelberg: Springer, 2007. 777–810

    Google Scholar 

  225. Kogan D, Murray R M. Optimization-based navigation for the DARPA Grand Challenge. In: Conference on Decision & Control. San Diego, CA: IEEE, 2006. 1–6

    Google Scholar 

  226. Bohren J, Foote T, Keller J, et al. Little ben: The ben franklin racing team’s entry in the 2007 DARPA urban challenge. In: Buehler M, Iagnemma K, Singh S, Eds. The DARPA Urban Challenge. Springer Tracts in Advanced Robotics, Vol 56. Berlin, Heidelberg: Springer, 2008. 231–255

    Google Scholar 

  227. Chen Y L, Sundareswaran V, Anderson C, et al. TerraMax™: Team Oshkosh urban robot. J Field Robotics, 2008, 25: 841–860

    Google Scholar 

  228. Kammel S, Ziegler J, Pitzer B, et al. Team AnnieWAY’s autonomous system for the 2007 DARPA Urban Challenge. In: Buehler M, Iagnemma K, Singh S, Eds. The DARPA Urban Challenge. Springer Tracts in Advanced Robotics, Vol 56. Berlin, Heidelberg: Springer, 2010. 359–391

    Google Scholar 

  229. Ferguson D, Howard T M, Likhachev M. Motion planning in urban environments. J Field Robotics, 2010, 25: 939–960

    Google Scholar 

  230. Mcnaughton M, Urmson C, Dolan J M, et al. Motion planning for autonomous driving with a conformal spatiotemporal lattice. In: IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011. 4889–4895

    Google Scholar 

  231. Lavalle S. Rapidly-exploring random trees: A new tool for path planning. Research Report. Ames, IA: Computer Science Department, Iowa State University, 1998

    Google Scholar 

  232. Kuwata Y, Fiore G A, Teo J, et al. Motion planning for urban driving using RRT. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice: IEEE, 2008. 1681–1686

    Google Scholar 

  233. Thurston D L. A formal method for subjective design evaluation with multiple attributes. Res Eng Des, 1991, 3: 105–122

    Google Scholar 

  234. Emerson E A. Temporal and modal logic. In: Handbook of Theoretical Computer Science (vol. B). Cambridge, MA: MIT Press, 1990. 995–1072

    Google Scholar 

  235. Kloetzer M, Belta C. A fully automated framework for control of linear systems from temporal logic specifications. IEEE Trans Automat Contr, 2008, 53: 287–297

    MathSciNet  MATH  Google Scholar 

  236. Artale A, Kontchakov R, Wolter F, et al. Temporal description logic for ontology-based data access. In: International Joint Conference on Artificial Intelligence. Beijing, China, 2013. 711–717

    Google Scholar 

  237. Liu J, Ozay N. Abstraction, discretization, and robustness in temporal logic control of dynamical systems. In: International Conference on Hybrid Systems: Computation and Control. Berlin, 2014. 293–302

    Google Scholar 

  238. Wongpiromsarn T. Formal methods for design and verification of embedded control systems: Application to an autonomous vehicle. Dissertation of Doctrol Degree. Caltrch: California Institute of Technology. 2010

    Google Scholar 

  239. Sadigh D, Kim E S, Coogan S, et al. A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications. In: 53rd IEEE Conference on Decision and Control. Los Angeles, CA: IEEE, 2014. 1091–1096

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering, Collaborative Innovation Center of Intelligent New Energy Vehicle, Tsinghua University, Beijing, 100084, China

    DianGe Yang, Kun Jiang, ChunLei Yu, Zhong Cao, ShiChao Xie, ZhongYang Xiao, XinYu Jiao, SiJia Wang & Kai Zhang

  2. University of Michigan Transportation Research Institute, Ann Arbor, MI, 48109, USA

    Ding Zhao

Authors
  1. DianGe Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ding Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ChunLei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. ShiChao Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. ZhongYang Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. XinYu Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. SiJia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to DianGe Yang, Kun Jiang or Ding Zhao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, D., Jiang, K., Zhao, D. et al. Intelligent and connected vehicles: Current status and future perspectives. Sci. China Technol. Sci. 61, 1446–1471 (2018). https://doi.org/10.1007/s11431-017-9338-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-017-9338-1

Keywords

Search

Navigation