access icon free Autonomous road vehicles: recent issues and expectations

The automotive industry is currently facing an automated driving revolution. This technology is tightly linked to societal and economic challenges: minimisation of traffic accidents, fuel consumption, traffic congestion, parking demand, and providing mobility for an ageing population, as well as to customer needs toward more personalised services. This study aims at the analysis and presentation of the current state of the art and prospects of automated vehicles (AVs) from various perspectives. This study concentrates on revision of the critical technologies, estimation of the impact on social aspects, identification of legal issues, consideration of factors in commercial success through user acceptance, and foresight carried out by other researchers. The primary material was prepared by review and analysis of research papers, standards, regulations, roadmaps, and projects in the field of AVs technology and its implementation worldwide. A SWOT analysis was performed, and it was found that for rapid AV spread, technological solutions need to be made taking into account law and regulation; user acceptance and human–robot interaction need to be solved together as part of one system.

Inspec keywords: road vehicles; automobile industry; road traffic; mobile robots; law; position control; human-robot interaction

Other keywords: account law; review; fuel consumption; AVs technology; personalised services; automotive industry; social aspects; economic challenges; traffic accidents; ageing population; recent issues; societal; autonomous road vehicles; providing mobility; SWOT analysis; implementation worldwide; rapid AV; customer needs; legal issues; primary material; automated driving revolution; user acceptance; commercial success; minimisation; critical technologies; technological solutions; traffic congestion; automated vehicles

Subjects: Automobile industry; Mobile robots; Spatial variables control

References

    1. 1)
      • 38. Levinson, J., Askeland, J., Becker, J., et al: ‘Towards fully autonomous driving: systems and algorithms’. IEEE Intelligent Vehicles Symp. (IV), Baden-Baden, Germany, June 2011, pp. 163168. doi:10.1109/IVS.2011.5940562.
    2. 2)
      • 23. Lizbetin, J., Bartuska, L.: ‘The influence of human factor on congestion formation on urban roads’, Procedia Eng., 2017, 187, pp. 206211. doi:10.1016/j.proeng.2017.04.366.
    3. 3)
      • 24. Lioris, J., Pedarsani, R., Tascikaraoglu, F. Y., et al: ‘Platoons of connected vehicles can double throughput in urban roads’, Transp. Res. C, Emerg. Technol., 2017, 77, pp. 292305. doi:10.1016/j.trc.2017.01.023.
    4. 4)
      • 33. Yang, Y., Wei, Z., Zhang, Y., et al: ‘V2x security: a case study of anonymous authentication’, Pervasive Mob. Comput., 2017, 41, pp. 259269. doi:10.1016/j.pmcj.2017.03.009.
    5. 5)
      • 46. Benenson, R., Omran, M., Hosang, J., et al: ‘Ten years of pedestrian detection, what have we learned?’, in Agapato, L., Bronstein, M., Rother, C. (Eds.) ‘Computer Vision – ECCV 2014 Workshops (ECCV 2014)’. Lecture Notes in Computer Science, Springer, Cham, Switzerland, March 2015, (LNCS, 8926) doi:10.1007/978-3-319-16181-5_47.
    6. 6)
      • 26. Igliński, H., Babiak, M.: ‘Analysis of the potential of autonomous vehicles in reducing the emissions of greenhouse gases in road transport’, Procedia Eng., 2017, 192, pp. 353358. doi:10.1016/j.proeng.2017.06.061.
    7. 7)
      • 83. Dijke, J., Schijndel, M., Nashashibi, F., et al: ‘Certification of automated transport systems’, Procedia – Soc. Behav. Sci., 2012, 48, pp. 34613470. doi:10.1016/j.sbspro.2012.06.1310.
    8. 8)
      • 48. Gurghian, A., Koduri, T., Bailur, S.V., et al: ‘DeepLanes: end-to-end lane position estimation using deep neural networks’. IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA, July 2016, pp. 3845. doi:10.1109/CVPRW.2016.12.
    9. 9)
      • 16. Miller, S.A., Heard, B.R.: ‘The environmental impact of autonomous vehicles depends on adoption patterns’, Environ. Sci. Technol., 2016, 50, (12), pp. 61196121. doi:10.1021/acs.est.6b02490.
    10. 10)
      • 55. Menze, M., Geiger, A.: ‘Object scene flow for autonomous vehicles’. 2015 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015, pp. 30613070. doi:10.1109/CVPR.2015.7298925.
    11. 11)
      • 86. Debernard, S., Chauvin, C., Pokam, R., et al: ‘Designing human-machine interface for autonomous vehicles’, IFAC PapersOnLine, 2016, 49, (19), pp. 609614. doi:10.1016/j.ifacol.2016.10.629.
    12. 12)
      • 79. Mounce, R., Nelson, J.D.: ‘On the potential for one-way electric vehicle car-sharing in future mobility systems’, Transp. Res. A, 2019, 120, pp. 1730. doi:10.1016/j.tra.2018.12.003.
    13. 13)
      • 5. Dickmanns, E.D., Behringer, R., Dickmanns, D., et al: ‘The seeing passenger car ‘VaMoRs-P’. Intelligent Vehicles Symp., Paris, France, October 1994, pp. 6873. doi:10.1109/IVS.1994.639472.
    14. 14)
      • 73. Hongbo, G., Guotao, X., Hongzhe, L., et al: ‘Lateral control of autonomous vehicles based on learning driver behavior via cloud model’, J. China Univ. Posts Telecommun., 2017, 24, (2), pp. 1017. doi:10.1016/S1005-8885(17)60194-8.
    15. 15)
      • 91. McDonald, S.S., Rodier, C.: ‘Envisioning automated vehicles within the built environment: 2020, 2035, and 2050’, Road Veh. Autom., 2015, 2, pp. 225233. doi: 10.1145/3003715.3005456.
    16. 16)
      • 19. Levin, M.W., Boyles, S.D.: ‘Effects of autonomous vehicle ownership on trip, mode, and route choice transportation’, Res. Rec. J. Transp. Res. Board, 2015, 2493, pp. 2938. doi:10.3141/2493-04.
    17. 17)
      • 20. Zhang, W., Guhathakurta, S., Fang, J., et al: ‘Exploring the impact of shared autonomous vehicles on urban parking demand: an agent-based simulation approach’, Sustain. Cities Soc., 2015, 19, pp. 3445. doi:10.1016/j.scs.2015.07.006.
    18. 18)
      • 52. L'Heureux, A., Grolinger, K., Capretz, M.A.M.: ‘Machine learning with big data: challenges and approaches’, IEEE Access, 2017, 5, pp. 77767797. doi:10.1109/ACCESS.2017.2696365.
    19. 19)
      • 45. Wu, B.F., Chiang, H.H., Lee, T.T., et al: ‘The embedded driving-assistance system on Taiwan iTS-1’. 2008 IEEE Int. Conf. Systems, Man and Cybernetics, Singapore, October 2008, pp. 33823387. doi:10.1109/ICSMC.2008.4811820.
    20. 20)
      • 35. Gruyer, D., Magnier, V., Hamdi, K., et al: ‘Perception, information processing and modeling: critical stages for autonomous driving applications’, Annu. Rev. Control, 2017, 44, pp. 323341. doi:10.1016/j.arcontrol.2017.09.012.
    21. 21)
      • 63. 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’, Transp. Res. C, 2015, 60, pp. 416442. doi:10.1016/j.trc.2015.09.011.
    22. 22)
      • 61. Johnson-Robertson, M., Barto, C., Mehta, R., et al: ‘Driving in the matrix: can virtual worlds replace human-generated annotations for real world talks?’. IEEE Int. Conf. Robotics and Automation (ICRA), Singapore, May–June 2017, pp. 746753. doi:10.1109/ICRA.2017.7989092.
    23. 23)
      • 62. Pek, C., Zahn, P., Althoff, M.: ‘Verifying the safety of lane change maneuvers of self-driving vehicles based on formalized traffic rules’. 2017 IEEE Intelligent Vehicles Symp. (IV), Redondo Beach, CA, USA, June 2017, pp. 14771483. doi:10.1109/IVS.2017.7995918.
    24. 24)
      • 92. The European Road Transport Research Advisory Council (ERTRAC): ‘Automated driving roadmap’. 2019, p. 55.
    25. 25)
      • 30. Faezipour, M., Nourani, M., Saeed, A., et al: ‘Progress and challenges in intelligent vehicle area networks’, Commun. ACM, 2012, 55, (2), pp. 90100. doi:10.1145/2076450.2076470.
    26. 26)
      • 15. Fagnant, D.J., Kockelman, K.: ‘Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations’, Transp. Res. A: Policy Pract., 2015, 77, pp. 167181. doi:10.1016/j.tra.2015.04.003.
    27. 27)
      • 36. Shim, I., Choi, J., Shin, S., et al: ‘An autonomous driving system for unknown environments using a unified map’, IEEE Trans. Intell. Transp. Syst., 2013, 16, (4), pp. 19992013. doi:10.1109/TITS.2015.2389237.
    28. 28)
      • 32. Manso, M., Guerra, B., Amditis, A., et al: ‘The application of telematics and smart devices in emergencies: use cases in next generation emergency services’. IEEE 1st Int. Conf. Internet-of-Things Design and Implementation (IoTDI), Berlin, Germany, May 2016, pp. 289292. doi:10.1109/IoTDI.2015.21.
    29. 29)
      • 3. Tsugawa, S., Yatabe, T., Hirose, T., et al: ‘An automobile with artificial intelligence’. Proc. Sixth Int. Joint Conf. Artificial Intelligence (IJCAI), Tokyo, Japan, 1979, vol. 2, pp. 893895.
    30. 30)
      • 17. Klynveld Peat Marwick Goerdeler (KPMG): ‘KPMG's global automotive executive survey’. 2012.
    31. 31)
      • 47. Behringer, R., Maurer, R.B.M.: ‘Results on visual road recognition for road vehicle guidance’. Proc. Conf. Intelligent Vehicles, Tokyo, Japan, September 1996, pp. 415420. doi:10.1109/IVS.1996.566416.
    32. 32)
      • 54. Brostow, G. J., Shotton, J., Fauqueur, J.: ‘Semantic object classes in video: a high-definition ground truth database’, Pattern Recognit. Lett., 2009, 30, (2), pp. 8897. doi:10.1016/j.patrec.2008.04.005.
    33. 33)
      • 69. Litman, T.: ‘Autonomous vehicle implementation predictions. Implications for transport planning’ (Victoria Transport Policy Institute, Canada, 2018), p. 34. Retrieved from https://vtpi.org/avip.pdf, accessed 15 October 2019.
    34. 34)
      • 71. Pascoe, G., Maddern, W., Newman, P.: ‘Direct visual localisation and calibration for road vehicles in changing city environments’. Proc. 2015 IEEE Int. Conf. Computer Vision Workshop (ICCVW), Satiago, Chile, December 2015, pp. 98105. doi:10.1109/ICCVW.2015.23.
    35. 35)
      • 18. Milakis, D., Arem, B., Wee, B.: ‘Policy and society related implications of automated driving: a review of literature and directions for future research’, J. Intell. Transp. Syst. Technol. Plann. Oper., 2017, 21, (4), pp. 324348. doi:10.1080/15472450.2017.1291351.
    36. 36)
      • 89. Naujoks, F., Höfling, S., Puruckera, Ch, et al: ‘From partial and high automation to manual driving: relationship between non-driving related tasks, drowsiness and take-over performance’, Accid. Anal. Prev., 2018, 121, pp. 2842. doi:10.1016/j.aap.2018.08.018.
    37. 37)
      • 49. Xia, W., Li, H., Li, B.: ‘A control strategy of autonomous vehicles based on deep reinforcement learning’. Ninth Int. Symp. Computational Intelligence and Design, Hangzhou, People's Republic of China, December 2016, pp. 198201. doi:10.1109/ISCID.2016.2054.
    38. 38)
      • 2. Society of Automotive Engineers (SAE) International: ‘Surface vehicle recommended practices: taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016)’. 2018, pp. 135.
    39. 39)
      • 81. Buning, M.C., Bruin, R.: ‘Autonomous intelligent cars: proof that the EPSRC principles are future-proof’, Connect. Sci., 2017, 29, (3), pp. 189199. doi:10.1080/09540091.2017.1310181.
    40. 40)
      • 90. Choi, J.K., Ji, Y.G.: ‘Investigating the importance of trust on adopting an autonomous vehicle’, Int. J. Human-Comput. Interact., 2015, 31, (10), pp. 692702. doi:10.1080/10447318.2015.1070549.
    41. 41)
      • 21. Hawkins, J., Habib, K. N.: ‘Integrated models of land use and transportation for the autonomous vehicle revolution’, Transp. Rev., 2019, 39, (1), pp. 6683. doi:10.1080/01441647.2018.1449033.
    42. 42)
      • 29. Sousa, S., Santos, A., Costa, A., et al: ‘A new approach on communications architectures for intelligent transportation systems’, Procedia Comput. Sci., 2017, 110, pp. 320327. doi:10.1016/j.procs.2017.06.101.
    43. 43)
      • 40. Lee, U., Yoon, S., Shim, H., et al: ‘Local path planning in a complex environment for self-driving car’. Fourth Annual IEEE Int. Conf. Cyber Technology in Automation, Control and Intelligent Systems, Hong Kong, June 2014, pp. 445450. doi:10.1109/CYBER.2014.6917505.
    44. 44)
      • 8. Thrun, S., Montemerlo, M., Dahlkamp, H., et al: ‘Stanley: the robot that won the DARPA grand challenge’, J. Field Robot., 2006, 23, (9), pp. 661692. doi:10.1002/rob.20147.
    45. 45)
      • 82. Collingwood, L.: ‘Privacy implications and liability issues of autonomous vehicles’, Inf. Commun. Technol. Law, 2017, 26, (1), pp. 3245. doi:10.1080/13600834.2017.1269871.
    46. 46)
      • 75. Amer, N.H., Zamzuri, H., Hudha, K., et al: ‘Modelling and control strategies in path tracking control for autonomous ground vehicles: a review of state of the art and challenges’, J. Intell. Robot. Syst., 2017, 86, (2), pp. 225254. doi:10.1007/s10846-016-0442-0.
    47. 47)
      • 4. Meissner, H.G., Dickmanns, E.D.: ‘Control of an unstable plant by computer vision’, in Huang, T.S. (ed.) ‘Image sequence processing and dynamic scene analysis’ (Springer, Germany, 1983), pp. 532548. doi:10.1007/978-3-642-81935-3_29.
    48. 48)
      • 44. Redmon, J., Farhadi, A.: ‘Better, faster, stronger’. 2016. Retrieved from http://arxiv.org/abs/1612.08242, accessed 15 October 2019.
    49. 49)
      • 60. Wymann, B., Dimitrakakis, C., Sumner, A., et al: ‘TORCS: the open racing car simulator’. 2015. Retrieved from http://www.cse.chalmers.se/~chrdimi/papers/torcs.pdf, accessed 15 October 2019.
    50. 50)
      • 72. Gordon, T.J., Lidberg, M.: ‘Automated driving and autonomous functions on-road vehicles’, Veh. Syst. Dyn., 2015, 53, (7), pp. 958994. doi:10.1080/00423114.2015.1037774.
    51. 51)
      • 37. Wang, L., Zhang, Y., Wang, J.: ‘Map-based localization method for autonomous vehicles using 3D-LIDAR’, IFAC-PapersOnLine, 2017, 50, (1), pp. 276281. doi:10.1016/j.ifacol.2017.08.046.
    52. 52)
      • 9. Meyer, G., Deix, S.: ‘Research and innovation for automated driving in Germany and Europe: road vehicle automation’. Lecture notes in mobility, 2014, (LNCS), pp. 7181. doi:10.1007/978-3-319-05990-7_7.
    53. 53)
      • 27. Jiménez, F., Naranjo, J.E., Anaya, J.J., et al: ‘Advanced driver assistance system for road environments to improve safety and efficiency’, Transp. Res. Procedia, 2016, 14, pp. 22452254. doi:10.1016/j.trpro.2016.05.240.
    54. 54)
      • 70. Chebly, A., Talj, R., Charara, A.: ‘Coupled longitudinal and lateral control for an autonomous vehicle dynamics modeled using a robotics formalism’, IFAC PapersOnLine, 2017, 50, (1), pp. 1252612532. doi:10.1016/j.ifacol.2017.08.2190.
    55. 55)
      • 65. Madas, D., Nosratinia, M., Keshavarz, M., et al: ‘On path planning methods for automotive collision avoidance’. 2013 IEEE Intelligent Vehicles Symp. (IV), Gold Coast, Australia, June 2013, pp. 931937. doi:10.1109/IVS.2013.6629586.
    56. 56)
      • 10. Kalra, N., Paddock, S.M.: ‘Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability?’, Transp. Res. A, 2016, 94, pp. 182193, doi:10.7249/RR1478.
    57. 57)
      • 39. Göhring, D., Wang, M., Schnürmacher, M., et al: ‘Radar/lidar sensor fusion for car-following on highways’. Fifth Int. Conf. Automation, Robotics and Applications, Wellington, New Zealand, December 2011, pp. 407412. doi:10.1109/ICARA.2011.6144918.
    58. 58)
      • 12. Zhenyu, L., Lin, P., Konglin, Z., et al: ‘Design and evaluation of V2X communication system for vehicle and pedestrian safety’, J. China Univ. Posts Telecommun., 2015, 22, (6), pp. 1826. doi:10.1016/S1005-8885(15)60689-6.
    59. 59)
      • 28. Arslan, S., Saritas, M.: ‘The effects of OFDM design parameters on the V2X communication performance: a survey’, Veh. Commun., 2017, 7, pp. 16. doi:10.1016/j.vehcom.2017.01.004.
    60. 60)
      • 25. Zabat, M., Stabile, N., Frascaroli, S., et al: ‘The aerodynamic performance of platoons: a final report’. California PATH Research Report UCB-ITS-PRR-95-35’, 1995, p. 172.
    61. 61)
      • 53. Cordts, M., Omran, M., Ramos, S., et al: ‘The cityscapes dataset for semantic urban scene understanding’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 2016, pp. 32133223. doi:10.1109/CVPR.2016.350.
    62. 62)
      • 85. McCall, R., McGee, F., Mirnig, A., et al: ‘A taxonomy of autonomous vehicle handover situations’, Transp. Res. A, 2019, 124, pp. 507522. doi:10.1016/j.tra.2018.05.005.
    63. 63)
      • 43. Behere, S., Törngren, M.: ‘A functional reference architecture for autonomous driving’, Inf. Softw. Technol., 2016, 73, pp. 136150. doi:10.1016/j.infsof.2015.12.008.
    64. 64)
      • 66. Qureshi, A.H., Ayaz, Y.: ‘Intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments’, Robot. Auton. Syst., 2015, 68, pp. 111. doi:10.1016/j.robot.2015.02.007.
    65. 65)
      • 11. World Health Organization (WHO): ‘Global status report on-road safety’. 2015. Retrieved from http://www.who.int/violence_injury_prevention/road_safety_status/2015/GSRRS2015_Summary_EN_final2.pdf?ua=1, accessed 15 October 2019.
    66. 66)
      • 1. National Highway Traffic Safety Administration (NHTSA): ‘Preliminary statement of policy concerning automated vehicles’. 2013, pp. 114.
    67. 67)
      • 56. Maddern, W., Pascoe, G., Linegar, C., et al: ‘1 year, 1000 km: the Oxford RobotCar dataset’, Int. J. Robot. Res., 2016, 36, (1), pp. 315. doi:10.1177/0278364916679498.
    68. 68)
      • 78. United Nations (UN): ‘Convention on-road traffic’ (Vienna, 1968). Retrieved 25 January 2019.
    69. 69)
      • 87. Meyer, G., Dokic, J., Müller, B.: ‘Elements of a European roadmap on smart systems for automated driving’, Road Veh. Autom., 2015, 2, pp. 153159. doi:10.1007/978-3-319-19078-5_13.
    70. 70)
      • 42. Kim, J.H., Seo, S.-H., Hai, N.-T., et al: ‘Gateway framework for In-vehicle networks based on CAN, FlexRay, and Ethernet’, IEEE Trans. Veh. Technol., 2015, 64, (10), pp. 44724486. doi:10.1109/TVT.2014.2371470.
    71. 71)
      • 41. Taranovich, S.: ‘Autonomous automotive sensors: how processor algorithms get their inputs’. 2016. Available at www.edn.com/Pdf/ViewPdf?contentItemId=4442319.
    72. 72)
      • 31. Krasniqi, X., Hajrizi, E.: ‘Use of IoT technology to drive the automotive industry from connected to full autonomous vehicles’, IFAC-PapersOnLine, 2016, 49, (29), pp. 269274. doi:10.1016/j.ifacol.2016.11.078.
    73. 73)
      • 84. J. D. Power and Associates: ‘U.S. Automotive emerging technology study’. 2012.
    74. 74)
      • 67. Furda, A., Vlacic, L. ‘Multiple criteria-based real-time decision making by autonomous city vehicles’, IFAC Proc., 2010, 43, (16), pp. 97102. doi:10.3182/20100906-3-IT-2019.00019.
    75. 75)
      • 34. Pastor, C.M., Gonzalez, R.R., Gil, J.G.: ‘A data fusion system of GNSS data and on-vehicle sensors data for improving car positioning precision in urban environments’, Expert Syst. Appl., 2017, 80, pp. 2838. doi:10.1016/j.eswa.2017.03.018.
    76. 76)
      • 68. Bojarski, M., Testa, D., Dworakowski, D.: ‘End to end learning for self-driving cars’. The Computing Research Repository (CoRR). 2016. Retrieved from http://arxiv.org/abs/1604.07316, accessed 15 October 2019.
    77. 77)
      • 22. Cohen, T., Cavoli, C.: ‘Automated vehicles: exploring possible consequences of government (non)intervention for congestion and accessibility’, Trans. Rev., 2019, 39, (1), pp. 129151. doi:10.1080/01441647.2018.1524401.
    78. 78)
      • 14. Haboucha, C.J., Ishaq, R., Shiftan, Y.: ‘User preferences regarding autonomous vehicles’, Transp. Res. C, 2017, 78, pp. 3749. doi:10.1016/j.trc.2017.01.010.
    79. 79)
      • 80. European Commission: ‘Gear 2030 presentation’. 2017. Retrieved from https://circabc.europa.eu/d/a/workspace/SpacesStore/741e4c94-5f67-4b3c-a1fb-1ff76ec66c75/GEAR%25202030-WG_%2520connected%2520and%2520automated%2520driving_12_05_05_2016%2520PT1.pdf, accessed 15 October 2019.
    80. 80)
      • 51. Temam, O.: ‘Enabling future progress in machine-learning’. 2016 Symp. VLSI Circuits Digest of Technical Papers, Honolulu, HI, USA, June 2016, pp. 13. doi: 10.1109/VLSIC.2016.7573457.
    81. 81)
      • 7. Broggi, A., Bertozzi, M., Fascioli, A., et al: ‘Automatic vehicle guidance: the experience of the ARGO autonomous vehicle’ (World Scientific Pub Co Inc, Singapore, 1999), p. 256. doi:10.1142/3986.
    82. 82)
      • 57. Blanco-Claraco, J.L., Moreno-Dueñas, F.Á., González-Jiménez, J.: ‘The Málaga urban dataset: high-rate stereo and LiDAR in a realistic urban scenario’, Int. J. Robot. Res., 2014, 33, (2), pp. 207214. doi:10.1177/0278364913507326.
    83. 83)
      • 58. Pandey, G., McBride, J.R., Eustice, R.M.: ‘Ford campus vision and lidar data set’, Int. J. Robot. Res. Arch., 2011, 30, (13), pp. 15431552. doi:10.1177/0278364911400640.
    84. 84)
      • 77. Mitkevičius, V.: ‘Autonomous vehicles – today's legal challenges for tomorrow’, Teisė, 2016, 101, pp. 126144. doi:10.15388/Teise.2016.101.10448.
    85. 85)
      • 76. Li, X., Sun, Z., Cao, D., et al: ‘Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles’, Mech. Syst. Signal Process., 2017, 87, (Part B), pp. 118137. doi:10.1016/j.ymssp.2015.10.021.
    86. 86)
      • 13. Doecke, S., Grant, A., Anderson, R.W.G.: ‘The real-world safety potential of connected vehicle technology’, Traffic Inj. Prev., 2015, 16, (1), pp. 3135. doi:10.1080/15389588.2015.1014551.
    87. 87)
      • 50. Du, S., Guo, H., Simpson, A.: ‘Self-driving car steering angle prediction based on image recognition. Stendford’. 2017, pp. 19. Retrieved from http://cs231n.stanford.edu/reports/2017/pdfs/626.pdf, accessed 15 October 2019.
    88. 88)
      • 64. Xu, W., Pan, J., Wei, J., et al: ‘Motion planning under uncertainty for on-road autonomous driving’. IEEE Int. Conf. Robotics and Automation (ICRA), Hong Kong, People's Republic of China, May–June 2014, pp. 20612067. doi:10.1109/ICRA.2014.6907209.
    89. 89)
      • 74. Kamada, S., Ichimura, T.: ‘Knowledge extracted from recurrent deep belief network for real-time deterministic control’. 2017 IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Banff, Canada, October 2017, pp. 825830. doi:10.1109/SMC.2017.8122711.
    90. 90)
      • 88. Arakawaa, T., Hibia, R., Fujishiro, T.: ‘Psychophysical assessment of a driver's mental state in autonomous vehicles’, Transp. Res. A, 2018, 124, pp. 587610. doi: 10.1016/j.tra.2018.05.003.
    91. 91)
      • 6. Thorpe, C., Hebert, M., Kanade, T., et al: ‘Vision and navigation for Carnegie Mellon Navlab’, Annu. Rev. Comput. Sci., 1987, 2, pp. 521556. doi:10.1007/978-3-642-74585-0_6.
    92. 92)
      • 59. Santana, E., Hotz, G.: ‘Learning a driving simulator’. The Computing Research Repository (CoRR), abs/1608.01230, 2016. Retrieved from http://arxiv.org/abs/1608.01230, accessed 15 October 2019.
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