1932

Abstract

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-control-060117-105157
2018-05-28
2024-06-25
Loading full text...

Full text loading...

/deliver/fulltext/control/1/1/annurev-control-060117-105157.html?itemId=/content/journals/10.1146/annurev-control-060117-105157&mimeType=html&fmt=ahah

Literature Cited

  1. 1. Fed. Highw. Adm. 2015. U.S. driving increases for sixth straight year, new federal data show Press Release, Fed. Highw. Adm., US Dep. Transp Washington, DC: https://www.fhwa.dot.gov/pressroom/fhwa1711.cfm
    [Google Scholar]
  2. 2. Assoc. Safe Intl. Road Travel (ASIRT). 2017. Home page. http://www.asirt.org
  3. 3. Natl. Saf. Counc. (NSC). 2015. NSC motor vehicle fatality estimates 2012–2015 Rep., Stat. Dep., NSC, Itasca, IL. http://www.nsc.org/NewsDocuments/2016/mv-fatality-report-1215.pdf
    [Google Scholar]
  4. 4. Natl. Highw. Traffic Saf. Adm. (NHTSA). 2015. Traffic safety facts 2015 Rep., NHTSA, US Dep. Transp Washington, DC: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812384
    [Google Scholar]
  5. 5. SAE Intl. 2016. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles Stand. J3016, SAE Intl Warrendale, PA:
    [Google Scholar]
  6. 6.  Russell HEB, Harbott LK, Nisky I, Pan S, Okamura AM, Gerdes JC 2016. Motor learning affects car-to-driver handover in automated vehicles. Sci. Robot. 1:eaah5682
    [Google Scholar]
  7. 7.  Maurer M, Gerdes JC, Lenz B, Winner H 2016. Autonomous Driving: Technical, Legal and Social Aspects Berlin: Springer
    [Google Scholar]
  8. 8.  Buehler M, Iagnemma K, Singh S 2007. The 2005 DARPA Grand Challenge: The Great Robot Race Berlin: Springer
    [Google Scholar]
  9. 9.  Buehler M, Iagnemma K, Singh S 2009. The DARPA Urban Challenge: Autonomous Vehicles in City Traffic Berlin: Springer
    [Google Scholar]
  10. 10.  Urmson C, Anhalt J, Bagnell D, Baker C, Bittner R et al. 2008. Autonomous driving in urban environments: Boss and the Urban Challenge. J. Field Robot. 25:425–66
    [Google Scholar]
  11. 11.  Leonard J, How J, Teller S, Berger M, Campbell S et al. 2008. A perception-driven autonomous urban vehicle. J. Field Robot. 25:727–74
    [Google Scholar]
  12. 12.  Furgale P, Schwesinger U, Rufli M, Derendarz W, Grimmett H et al. 2013. Toward automated driving in cities using close-to-market sensors: an overview of the V-Charge Project. 2013 IEEE Intelligent Vehicles Symposium (IV)809–16 New York: IEEE
    [Google Scholar]
  13. 13.  Ulbrich S, Reschka A, Rieken J, Ernst S, Bagschik G et al. 2017. Towards a functional system architecture for automated vehicles. arXiv:1703.08557
  14. 14.  De Luca A, Oriolo G, Samson C 1998. Feedback control of a nonholonomic car-like robot. Robot Motion Planning and Control JP Laumond 171–253 Berlin: Springer
    [Google Scholar]
  15. 15.  Gillespie TD 1997. Vehicle Dynamics Warrendale, PA: Soc. Automot. Eng.
    [Google Scholar]
  16. 16.  Pacejka H 2012. Tire and Vehicle Dynamics Oxford, UK: Elsevier, 3rd ed..
    [Google Scholar]
  17. 17.  Rajamani R 2012. Vehicle Dynamics and Control New York: Springer, 2nd ed..
    [Google Scholar]
  18. 18.  Hoffmann GM, Tomlin CJ, Montemerlo M, Thrun S 2007. Autonomous automobile trajectory tracking for off-road driving: controller design, experimental validation and racing. 2007 American Control Conference2296–301 New York: IEEE
    [Google Scholar]
  19. 19.  Falcone P, Borrelli F, Asgari J, Tseng HE, Hrovat D 2007. Predictive active steering control for autonomous vehicle systems. IEEE Trans. Control Syst. Technol. 15:566–80
    [Google Scholar]
  20. 20.  Kapania NR, Gerdes JC 2015. Design of a feedback-feedforward steering controller for accurate path tracking and stability at the limits of handling. Vehicle Syst. Dyn. 53:1687–704
    [Google Scholar]
  21. 21.  Nelles O 2001. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models Berlin: Springer
    [Google Scholar]
  22. 22.  Seegmiller N, Rogers-Marcovitz F, Miller G, Kelly A 2013. Vehicle model identification by integrated prediction error minimization. Int. J. Robot. Res. 32:912–31
    [Google Scholar]
  23. 23.  Anderson SJ, Karumanchi SB, Iagnemma K, Walker JM 2013. The intelligent copilot: a constraint-based approach to shared-adaptive control of ground vehicles. IEEE Intell. Transp. Syst. Mag. 5:45–54
    [Google Scholar]
  24. 24.  Abbink DA, Mulder M, Boer ER 2011. Haptic shared control: smoothly shifting control authority?. Cogn. Technol. Work 14:19–28
    [Google Scholar]
  25. 25.  Alonso-Mora J, Gohl P, Watson S, Siegwart R, Beardsley P 2014. Shared control of autonomous vehicles based on velocity space optimization. 2014 IEEE International Conference on Robotics and Automation (ICRA)1639–45 New York: IEEE
    [Google Scholar]
  26. 26.  Shia VA, Gao Y, Vasudevan R, Campbell KD, Lin T et al. 2014. Semiautonomous vehicular control using driver modeling. IEEE Trans. Intell. Transp. Syst. 15:2696–709
    [Google Scholar]
  27. 27.  Erlien SM, Fujita S, Gerdes JC 2016. Shared steering control using safe envelopes for obstacle avoidance and vehicle stability. IEEE Trans. Intell. Transp. Syst. 17:441–51
    [Google Scholar]
  28. 28.  Schwarting W, Alonso-Mora J, Paull L, Karaman S, Rus D 2017. Parallel autonomy in automated vehicles: safe motion generation with minimal intervention. 2017 IEEE International Conference on Robotics and Automation (ICRA)1928–35 New York: IEEE
    [Google Scholar]
  29. 28a.  Schwarting W, Alonso-Mora J, Paull L, Karaman S, Rus D 2018. Safe nonlinear trajectory generation for parallel autonomy with a dynamic vehicle model. IEEE Trans. Intell. Transp. Syst. https://doi.org/10.1109/TITS.2017.2771351
    [Crossref] [Google Scholar]
  30. 29.  Katrakazas C, Quddus M, Chen WH, Deka L 2015. Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp. Res. C 60:416–42
    [Google Scholar]
  31. 30.  Paden B, Cap M, Yong SZ, Yershov D, Frazzoli E 2016. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1:33–55
    [Google Scholar]
  32. 31.  Ferguson D, Howard TM, Likhachev M 2008. Motion planning in urban environments. J. Field Robot. 25:939–60
    [Google Scholar]
  33. 32.  Pivtoraiko M, Knepper RA, Kelly A 2009. Differentially constrained mobile robot motion planning in state lattices. J. Field Robot. 26:308–33
    [Google Scholar]
  34. 33.  Werling M, Kammel S, Ziegler J, Gröll L 2012. Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. Int. J. Robot. Res. 31:346–59
    [Google Scholar]
  35. 34.  LaValle SM, Kuffner JJ 2001. Randomized kinodynamic planning. Int. J. Robot. Res. 20:378–400
    [Google Scholar]
  36. 35.  Karaman S, Frazzoli E 2011. Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30:846–94
    [Google Scholar]
  37. 36.  Liniger A, Domahidi A, Morari M 2014. Optimization-based autonomous racing of 1:43 scale RC cars. Opt. Control Appl. Methods 36:628–47
    [Google Scholar]
  38. 37.  Andersen H, Schwarting W, Naser F, Eng YH, Ang MH Jr. et al. 2017. Trajectory optimization for autonomous overtaking with visibility maximization. 2017 IEEE International Conference on Intelligent Transportation Systems (ITSC) New York: IEEE. In press
    [Google Scholar]
  39. 38.  Kuwata Y, Teo J, Fiore G, Karaman S, Frazzoli E, How JP 2009. Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17:1105–18
    [Google Scholar]
  40. 39.  Tumova J, Hall GC, Karaman S, Frazzoli E, Rus D 2013. Least-violating control strategy synthesis with safety rules. HSCC '13: Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control1–10 New York: ACM
    [Google Scholar]
  41. 40.  Vasile CI, Tumova J, Karaman S, Belta C, Rus D 2017. Minimum-violation scLTL motion planning for mobility-on-demand. 2017 IEEE International Conference on Robotics and Automation (ICRA)1481–88 New York: IEEE
    [Google Scholar]
  42. 41.  Janai J, Güney F, Behl A, Geiger A 2017. Computer vision for autonomous vehicles: problems, datasets and state-of-the-art. arXiv:1704.05519
    [Google Scholar]
  43. 42.  Geiger A, Lenz P, Stiller C, Urtasun R 2013. Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32:1231–37
    [Google Scholar]
  44. 43.  Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M et al. 2016. The Cityscapes dataset for semantic urban scene understanding. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)3213–23 New York: IEEE
    [Google Scholar]
  45. 44.  Lowe DG 1999. Object recognition from local scale-invariant features. Seventh IEEE International Conference on Computer Vision (ICCV) 21150–57 New York: IEEE
    [Google Scholar]
  46. 45.  Lowe DG 2004. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60:91–110
    [Google Scholar]
  47. 46.  Leutenegger S, Chli M, Siegwart RY 2011. BRISK: Binary Robust Invariant Scalable Keypoints. 2011 IEEE International Conference on Computer Vision (ICCV)2548–55 New York: IEEE
    [Google Scholar]
  48. 47.  Bay H, Ess A, Tuytelaars T, Gool LV 2008. Speeded-Up Robust Features (SURF). Comput. Vis. Image Understand. 110:346–59
    [Google Scholar]
  49. 48.  Bay H, Tuytelaars T, Van Gool L 2006. SURF: Speeded Up Robust Features. Computer Vision – ECCV 2006 A Leonardis, H Bischof, A Pinz 404–17 Berlin: Springer
    [Google Scholar]
  50. 49.  Rublee E, Rabaud V, Konolige K, Bradski G 2011. ORB: an efficient alternative to SIFT or SURF. 2011 IEEE International Conference on Computer Vision (ICCV)2564–71 New York: IEEE
    [Google Scholar]
  51. 50.  Mur-Artal R, Tardós JD 2017. ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33:1255–62
    [Google Scholar]
  52. 51.  Zhang J, Singh S 2015. Visual-LIDAR odometry and mapping: low-drift, robust, and fast. 2015 IEEE International Conference on Robotics and Automation (ICRA)2174–81 New York: IEEE
    [Google Scholar]
  53. 52.  Forster C, Zhang Z, Gassner M, Werlberger M, Scaramuzza D 2017. SVO: semidirect visual odometry for monocular and multicamera systems. IEEE Trans. Robot. 33:249–65
    [Google Scholar]
  54. 53.  Engel J, Stckler J, Cremers D 2015. Large-scale direct SLAM with stereo cameras. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)1935–42 New York: IEEE
    [Google Scholar]
  55. 54.  Bar Hillel A, Lerner R, Levi D, Raz G 2014. Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25:727–45
    [Google Scholar]
  56. 55.  Russakovsky O, Deng J, Su H, Krause J, Satheesh S et al. 2015. ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115:211–52
    [Google Scholar]
  57. 56.  Ren S, He K, Girshick R, Sun J 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39:1137–49
    [Google Scholar]
  58. 57.  He K, Zhang X, Ren S, Sun J 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)770–78 New York: IEEE
    [Google Scholar]
  59. 58.  Zhao H, Shi J, Qi X, Wang X, Jia J 2017. Pyramid scene parsing network. arXiv:1612.01105
    [Google Scholar]
  60. 59.  Paszke A, Chaurasia A, Kim S, Culurciello E 2016. ENet: a deep neural network architecture for real-time semantic segmentation. arXiv:1606.02147
    [Google Scholar]
  61. 60.  Zhao H, Qi X, Shen X, Shi J, Jia J 2017. ICNet for real-time semantic segmentation on high-resolution images. arXiv:1704.08545
    [Google Scholar]
  62. 61.  Ros G, Sellart L, Materzynska J, Vazquez D, Lopez AM 2016. The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)3234–43 New York: IEEE
    [Google Scholar]
  63. 62.  Johnson-Roberson M, Barto C, Mehta R, Sridhar SN, Rosaen K, Vasudevan R 2017. Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?. 2017 IEEE International Conference on Robotics and Automation (ICRA)746–53 New York: IEEE
    [Google Scholar]
  64. 63.  Richter SR, Vineet V, Roth S, Koltun V 2016. Playing for data: ground truth from computer games. Computer Vision – ECCV 2016 B Leibe, J Matas, N Sebe, M Welling 102–18 Cham, Switz.: Springer
    [Google Scholar]
  65. 64.  Herranz L, Jiang S, Li X 2016. Scene recognition with CNNs: objects, scales and dataset bias. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)571–79 New York: IEEE
    [Google Scholar]
  66. 65.  Gal Y, Ghahramani Z 2016. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. ICML '16: 33rd International Conference on Machine Learning MF Balcan, KQ Weinberger 1050–59 New York: PMLR
    [Google Scholar]
  67. 66.  McAllister R, Gal Y, Kendall A, van der Wilk M, Shah A et al. 2017. Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17)4745–53 Calif.: IJCAI
    [Google Scholar]
  68. 67.  Chen C, Seff A, Kornhauser A, Xiao J 2015. DeepDriving: learning affordance for direct perception in autonomous driving. 2015 IEEE International Conference on Computer Vision (ICCV)2722–30 New York: IEEE
    [Google Scholar]
  69. 68.  Caltagirone L, Bellone M, Svensson L, Wahde M 2017. LIDAR-based driving path generation using fully convolutional neural networks. arXiv:1703.08987
    [Google Scholar]
  70. 69.  Barnes D, Maddern W, Posner I 2017. Find your own way: weakly-supervised segmentation of path proposals for urban autonomy. 2017 IEEE International Conference on Robotics and Automation (ICRA)203–10 New York: IEEE
    [Google Scholar]
  71. 70.  Pomerleau DA 1989. ALVINN: an autonomous land vehicle in a neural network. Advances in Neural Information Processing Systems 1 DS Touretzky 305–13 San Francisco: Morgan Kaufmann
    [Google Scholar]
  72. 71.  Muller U, Ben J, Cosatto E, Flepp B, Cun YL 2006. Off-road obstacle avoidance through end-to-end learning. Advances in Neural Information Processing Systems 18 Y Weiss, PB Schölkopf, JC Platt 739–46 Cambridge, MA: MIT Press
    [Google Scholar]
  73. 72.  Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B et al. 2016. End to end learning for self-driving cars. arXiv:1604.07316
    [Google Scholar]
  74. 73.  Bojarski M, Yeres P, Choromanska A, Choromanski K, Firner B et al. 2017. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv:1704.07911
    [Google Scholar]
  75. 74.  Gurghian A, Koduri T, Bailur SV, Carey KJ, Murali VN 2016. DeepLanes: end-to-end lane position estimation using deep neural networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)38–45 New York: IEEE
    [Google Scholar]
  76. 75.  Xu H, Gao Y, Yu F, Darrell T 2016. End-to-end learning of driving models from large-scale video datasets. arXiv:1612.01079
    [Google Scholar]
  77. 76.  Zhang J, Cho K 2016. Query-efficient imitation learning for end-to-end autonomous driving. arXiv:1605.06450
    [Google Scholar]
  78. 77.  Ross S, Gordon GJ, Bagnell D 2011. A reduction of imitation learning and structured prediction to no-regret online learning. Proceedings of the 14th International Conference on Artificial Intelligence and Statistics627–35 New York: PMLR
    [Google Scholar]
  79. 78.  Pfeiffer M, Schaeuble M, Nieto J, Siegwart R, Cadena C 2017. From perception to decision: a data-driven approach to end-to-end motion planning for autonomous ground robots. 2017 IEEE International Conference on Robotics and Automation (ICRA)1527–33 New York: IEEE
    [Google Scholar]
  80. 79.  Chen YF, Everett M, Liu M, How JP 2017. Socially aware motion planning with deep reinforcement learning. arXiv:1703.08862
    [Google Scholar]
  81. 80.  Richter C, Roy N 2017. Safe visual navigation via deep learning and novelty detection. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 64 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  82. 81.  Wolf P, Hubschneider C, Weber M, Bauer A, Hrtl J et al. 2017. Learning how to drive in a real world simulation with deep Q-networks. 2017 IEEE Intelligent Vehicles Symposium (IV)244–50 New York: IEEE
    [Google Scholar]
  83. 82.  You Y, Pan X, Wang Z, Lu C 2017. Virtual to real reinforcement learning for autonomous driving. arXiv:1704.03952
    [Google Scholar]
  84. 83.  Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T et al. 2015. Continuous control with deep reinforcement learning. arXiv:1509.02971
    [Google Scholar]
  85. 84.  Montemerlo M, Becker J, Bhat S, Dahlkamp H, Dolgov D et al. 2008. Junior: the Stanford entry in the Urban Challenge. J. Field Robot. 25:569–97
    [Google Scholar]
  86. 85.  Trautman P, Ma J, Murray RM, Krause A 2015. Robot navigation in dense human crowds: statistical models and experimental studies of human-robot cooperation. Int. J. Robot. Res. 34:335–56
    [Google Scholar]
  87. 86.  Toit NED, Burdick JW 2012. Robot motion planning in dynamic, uncertain environments. IEEE Trans. Robot. 28:101–15
    [Google Scholar]
  88. 87.  Sadigh D, Sastry S, Seshia SA, Dragan AD 2016. Planning for autonomous cars that leverage effects on human actions. Robotics: Science and Systems XII D Hsu, N Amato, S Berman, S Jacobs, chap. 29 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  89. 88.  Kretzschmar H, Spies M, Sprunk C, Burgard W 2016. Socially compliant mobile robot navigation via inverse reinforcement learning. Int. J. Robot. Res. 35:1289–307
    [Google Scholar]
  90. 89.  Düring M, Pascheka P 2014. Cooperative decentralized decision making for conflict resolution among autonomous agents. 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings154–61 New York: IEEE
    [Google Scholar]
  91. 90.  Ulbrich S, Grossjohann S, Appelt C, Homeier K, Rieken J, Maurer M 2015. Structuring cooperative behavior planning implementations for automated driving. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC)2159–65 New York: IEEE
    [Google Scholar]
  92. 91.  Bahram M, Lawitzky A, Friedrichs J, Aeberhard M, Wollherr D 2016. A game-theoretic approach to replanning-aware interactive scene prediction and planning. IEEE Trans. Veh. Technol. 65:3981–92
    [Google Scholar]
  93. 92.  Lenz D, Kessler T, Knoll A 2016. Tactical cooperative planning for autonomous highway driving using Monte-Carlo tree search. 2016 IEEE Intelligent Vehicles Symposium (IV)447–53 New York: IEEE
    [Google Scholar]
  94. 93.  Schwarting W, Pascheka P 2014. Recursive conflict resolution for cooperative motion planning in dynamic highway traffic. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC),1039–44 New York: IEEE
    [Google Scholar]
  95. 94.  Li N, Oyler DW, Zhang M, Yildiz Y, Kolmanovsky I, Girard AR 2017. Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. IEEE Trans. Control Syst. Technol. In press. https://doi.org/10.1109/TCST.2017.2723574
    [Crossref] [Google Scholar]
  96. 95.  Wei J, Dolan JM, Litkouhi B 2013. Autonomous vehicle social behavior for highway entrance ramp management. 2013 IEEE Intelligent Vehicles Symposium (IV)201–7 New York: IEEE
    [Google Scholar]
  97. 96.  Evestedt N, Ward E, Folkesson J, Axehill D 2016. Interaction aware trajectory planning for merge scenarios in congested traffic situations. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)465–72 New York: IEEE
    [Google Scholar]
  98. 97.  Hoermann S, Stumper D, Dietmayer K 2017. Probabilistic long-term prediction for autonomous vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV)237–43 New York: IEEE
    [Google Scholar]
  99. 98.  Dong C, Dolan JM, Litkouhi B 2017. Intention estimation for ramp merging control in autonomous driving. 2017 IEEE Intelligent Vehicles Symposium (IV)1584–89 New York: IEEE
    [Google Scholar]
  100. 99.  Hubmann C, Becker M, Althoff D, Lenz D, Stiller C 2017. Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles. 2017 IEEE Intelligent Vehicles Symposium (IV)1671–78 New York: IEEE
    [Google Scholar]
  101. 100.  Liu W, Kim SW, Pendleton S, Ang MH 2015. Situation-aware decision making for autonomous driving on urban road using online POMDP. 2015 IEEE Intelligent Vehicles Symposium (IV)1126–33 New York: IEEE
    [Google Scholar]
  102. 101.  Ulbrich S, Maurer M 2015. Towards tactical lane change behavior planning for automated vehicles. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC)989–95 New York: IEEE
    [Google Scholar]
  103. 102.  Galceran E, Cunningham AG, Eustice RM, Olson E 2017. Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: theory and experiment. Auton. Robots 41:1367–82
    [Google Scholar]
  104. 102a.  Zhou B, Schwarting W, Rus D, Alonso-Mora J 2018. Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving. 2018 IEEE International Conference on Robotics and Automation (ICRA). New York: IEEE. In press
    [Google Scholar]
  105. 103.  Brechtel S, Gindele T, Dillmann R 2014. Probabilistic decision-making under uncertainty for autonomous driving using continuous POMDPs. 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC)392–99 New York: IEEE
    [Google Scholar]
  106. 104.  Shalev-Shwartz S, Shammah S, Shashua A 2016. Safe, multi-agent, reinforcement learning for autonomous driving. arXiv:1610.03295
    [Google Scholar]
  107. 105.  Vallon C, Ercan Z, Carvalho A, Borrelli F 2017. A machine learning approach for personalized autonomous lane change initiation and control. 2017 IEEE Intelligent Vehicles Symposium (IV)1590–95 New York: IEEE
    [Google Scholar]
  108. 106.  Lenz D, Diehl F, Le MT, Knoll A 2017. Deep neural networks for Markovian interactive scene prediction in highway scenarios. 2017 IEEE Intelligent Vehicles Symposium (IV)685–92 New York: IEEE
    [Google Scholar]
  109. 107.  Lee N, Choi W, Vernaza P, Choy CB, Torr PH, Chandraker M 2017. DESIRE: distant future prediction in dynamic scenes with interacting agents. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2165–74 New York: IEEE
    [Google Scholar]
  110. 108.  Sadigh D, Sastry SS, Seshia SA, Dragan A 2016. Information gathering actions over human internal state. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)66–73 New York: IEEE
    [Google Scholar]
  111. 109.  Sadigh D, Dragan A, Sastry S, Seshia S 2017. Active preference-based learning of reward functions. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 53 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  112. 110.  Huang SH, Held D, Abbeel P, Dragan AD 2017. Enabling robots to communicate their objectives. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 59 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  113. 111.  Abbeel P, Ng AY 2004. Apprenticeship learning via inverse reinforcement learning. ICML '04: Proceedings of the Twenty-First International Conference on Machine Learning chap. 1 New York: ACM
    [Google Scholar]
  114. 112.  Abbeel P, Dolgov D, Ng AY, Thrun S 2008. Apprenticeship learning for motion planning with application to parking lot navigation. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)1083–90 New York: IEEE
    [Google Scholar]
  115. 113.  Ziebart BD, Maas AL, Bagnell JA, Dey AK 2008. Maximum entropy inverse reinforcement learning. 23rd AAAI Conference on Artificial Intelligence1433–38 Menlo Park, CA: AAAI Press
    [Google Scholar]
  116. 114.  Kuderer M, Gulati S, Burgard W 2015. Learning driving styles for autonomous vehicles from demonstration. 2015 IEEE International Conference on Robotics and Automation (ICRA)2641–46 New York: IEEE
    [Google Scholar]
  117. 115.  Pfeiffer M, Schwesinger U, Sommer H, Galceran E, Siegwart R 2016. Predicting actions to act predictably: cooperative partial motion planning with maximum entropy models. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2096–101 New York: IEEE
    [Google Scholar]
  118. 116.  Herman M, Fischer V, Gindele T, Burgard W 2015. Inverse reinforcement learning of behavioral models for online-adapting navigation strategies. 2015 IEEE International Conference on Robotics and Automation (ICRA)3215–22 New York: IEEE
    [Google Scholar]
  119. 117.  Ratliff ND, Bagnell JA, Zinkevich MA 2006. Maximum margin planning. Proceedings of the 23rd International Conference on Machine Learning729–36 New York: ACM
    [Google Scholar]
  120. 118.  Silver D, Bagnell JA, Stentz A 2010. Learning from demonstration for autonomous navigation in complex unstructured terrain. Int. J. Robot. Res. 29:1565–92
    [Google Scholar]
  121. 119.  Silver D, Bagnell JA, Stentz A 2013. Learning autonomous driving styles and maneuvers from expert demonstration. Experimental Robotics: The 13th International Symposium on Experimental Robotics J Desai, G Dudek, O Khatib, V Kumar 371–86 Heidelberg, Ger.: Springer
    [Google Scholar]
  122. 120.  Levine S, Koltun V 2012. Continuous inverse optimal control with locally optimal examples. Proceedings of the 29th International Conference on International Conference on Machine Learning475–82 Madison, WI: Omnipress
    [Google Scholar]
  123. 121.  Majumdar A, Singh S, Mandlekar A, Pavone M 2017. Risk-sensitive inverse reinforcement learning via coherent risk models. Robotics: Science and Systems XIII N Amato, S Srinivasa, N Ayanian, S Kuindersma, chap. 69 N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  124. 122.  Wulfmeier M, Ondruska P, Posner I 2015. Maximum entropy deep inverse reinforcement learning. arXiv:1507.04888
    [Google Scholar]
  125. 123.  Wulfmeier M, Rao D, Wang DZ, Ondruska P, Posner I 2017. Large-scale cost function learning for path planning using deep inverse reinforcement learning. Int. J. Robot. Res. 10:1073–87
    [Google Scholar]
  126. 124.  Kuefler A, Morton J, Wheeler T, Kochenderfer M 2017. Imitating driver behavior with generative adversarial networks. arXiv:1701.06699
    [Google Scholar]
  127. 125.  Ho J, Ermon S 2016. Generative adversarial imitation learning. Advances in Neural Information Processing Systems 29 DD Lee, M Sugiyama, UV Luxburg, I Guyon, R Garnett 4565–73 New York: Curran Assoc.
    [Google Scholar]
  128. 126.  Kalra N, Paddock S 2016. Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Rep. RR-1478-RC, Rand Corp Santa Monica, CA: http://www.rand.org/pubs/research_reports/RR1478.html
    [Google Scholar]
  129. 127.  Nilsson P, Hussien O, Balkan A, Chen Y, Ames AD et al. 2016. Correct-by-construction adaptive cruise control: two approaches. IEEE Trans. Control Syst. Technol. 24:1294–307
    [Google Scholar]
  130. 128.  Kim ES, Arcak M, Seshia SA 2015. Compositional controller synthesis for vehicular traffic networks. 2015 54th IEEE Conference on Decision and Control (CDC)6165–71 New York: IEEE
    [Google Scholar]
  131. 129.  Wongpiromsarn T 2010. Formal methods for design and verification of embedded control systems: application to an autonomous vehicle. PhD Thesis, Calif. Inst. Technol Pasadena, CA:
    [Google Scholar]
  132. 130.  Loos SM, Platzer A, Nistor L 2011. Adaptive cruise control: hybrid, distributed, and now formally verified. FM 2011: Formal Methods M Butler, W Schulte 42–56 Berlin: Springer
    [Google Scholar]
  133. 131.  Althoff M, Dolan JM 2014. Online verification of automated road vehicles using reachability analysis. IEEE Trans. Robot. 30:903–18
    [Google Scholar]
  134. 132.  Schürmann B, Heß D, Eilbrecht J, Stursberg O, Köster F, Althoff M 2017. Ensuring drivability of planned motions using formal methods. In 2017 20th IEEE Intelligent Transportation Systems Conference (ITSC) New York: IEEE. In press
    [Google Scholar]
  135. 133.  Liebenwein L, Schwarting W, Vasile CI, DeCastro J, Alonso-Mora J et al. 2018. Compositional and contract-based verification for autonomous driving on road networks. Robotics Research: The 18th International Symposium ISRR Forthcoming
    [Google Scholar]
  136. 134.  Katz G, Barrett C, Dill DL, Julian K, Kochenderfer MJ 2017. Reluplex: an efficient SMT solver for verifying deep neural networks. Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24–28, 2017, Proceedings, Part I R Majumdar, V Kunčak 97–117 Cham, Switz.: Springer
    [Google Scholar]
  137. 135.  Seshia SA, Sadigh D, Sastry SS 2016. Towards verified artificial intelligence. arXiv:1606.08514
    [Google Scholar]
  138. 136.  Pavone M, Smith S, Frazzoli E, Rus D 2012. Robotic load balancing for mobility-on-demand systems. Int. J. Robot. Res. 31:839–54
    [Google Scholar]
  139. 137.  Zhang R, Pavone M 2016. Control of robotic mobility-on-demand systems: a queueing-theoretical perspective. Int. J. Robot. Res. 35:186–203
    [Google Scholar]
  140. 138.  de Almeida Correia GH, van Arem B 2016. Solving the user optimum privately owned automated vehicles assignment problem (UO-POAVAP): a model to explore the impacts of self-driving vehicles on urban mobility. Transp. Res. B 87:64–88
    [Google Scholar]
  141. 139.  Toth P, Vigo D 2014. Vehicle Routing: Problems, Methods, and Applications Philadelphia: SIAM, 2nd ed..
    [Google Scholar]
  142. 140.  Pillac V, Gendreau M, Guéret C, Medaglia AL 2013. A review of dynamic vehicle routing problems. Eur. J. Oper. Res. 225:1–11
    [Google Scholar]
  143. 141.  Agatz NA, Erera AL, Savelsbergh MW, Wang X 2011. Dynamic ride-sharing: a simulation study in metro Atlanta. Transp. Res. B 45:1450–64
    [Google Scholar]
  144. 142.  Santi P, Resta G, Szell M, Sobolevsky S, Strogatz SH, Ratti C 2014. Quantifying the benefits of vehicle pooling with shareability networks. PNAS 111:13290–94
    [Google Scholar]
  145. 143.  Alonso-Mora J, Samaranayake S, Wallar A, Frazzoli E, Rus D 2017. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. PNAS 114:462–67
    [Google Scholar]
  146. 144.  Shaheen S, Christensen M 2014. The true future of transportation has two big barriers to entry. CityLab Apr. 25. https://www.citylab.com/transportation/2014/04/true-future-transportation-has-two-big-barriers-entry/8933
    [Google Scholar]
  147. 145. NYC OpenData. 2016. New York City yellow taxi trip data https://data.cityofnewyork.us/dataset/2016-Yellow-Taxi-Trip-Data/k67s-dv2t
    [Google Scholar]
  148. 146.  Ritzinger U, Puchinger J, Hartl RF 2016. A survey on dynamic and stochastic vehicle routing problems. Int. J. Prod. Res. 54:215–31
    [Google Scholar]
  149. 147.  Alonso-Mora J, Wallar A, Rus D 2017. Predictive routing for autonomous mobility-on-demand systems with ride-sharing. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)3583–90 New York: IEEE
    [Google Scholar]
  150. 148.  Barnard M 2016. Autonomous cars likely to increase congestion. CleanTechnica Jan. 17. http://cleantechnica.com/2016/01/17/autonomous-cars-likely-increase-congestion
    [Google Scholar]
  151. 149.  Zhang R, Rossi F, Pavone M 2017. Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms. Auton. Robots. In press
    [Google Scholar]
  152. 150.  Levin MW 2017. Congestion-aware system optimal route choice for shared autonomous vehicles. Transp. Res. C 82:229–47
    [Google Scholar]
  153. 151.  de Weerdt MM, Stein S, Gerding EH, Robu V, Jennings NR 2016. Intention-aware routing of electric vehicles. IEEE Trans. Intell. Transp. Syst. 17:1472–82
    [Google Scholar]
/content/journals/10.1146/annurev-control-060117-105157
Loading
/content/journals/10.1146/annurev-control-060117-105157
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error