Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems

IEEE Transactions on Intelligent Transportation Systems

Volumn 16, Issue 1, 2015, Pages 244-263

  Da Lio, Mauro ^a   Biral, Francesco ^a   Bertolazzi, Enrico ^a   Galvani, Marco ^a   Bosetti, Paolo ^a   Windridge, David ^b   Saroldi, Andrea ^c   Tango, Fabio ^c  

^a UNIVERSITY OF TRENTO   (Italy)

^b UNIVERSITY OF SURREY   (United Kingdom)

^c CENTRO RICERCHE FIAT   (Italy)

Author keywords

Advanced driver assistance systems (ADAS); Artificial cognitive systems; Emulation theory of cognition; Intelligent vehicles; Man machine systems; Optimal control (OC)

Indexed keywords

ADVANCED VEHICLE CONTROL SYSTEMS; APPLICATION PROGRAMS; AUTOMOBILE DRIVERS; COGNITIVE SYSTEMS; HUMAN ROBOT INTERACTION; INTELLIGENT SYSTEMS; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; INTERACTIVE COMPUTER SYSTEMS; MACHINE DESIGN; MAN MACHINE SYSTEMS; ROBOTS; TRANSPORTATION; VEHICLES;

APPLICATION FIELDS; CO-OPERATIVE SYSTEMS; COGNITIVE ARCHITECTURES; ENABLING TECHNOLOGIES; INTELLIGENT TRANSPORTATION SYSTEMS; OPTIMAL CONTROLS; THEORY OF COGNITIONS; VEHICLES AND TRANSPORTATION SYSTEMS;

ADVANCED DRIVER ASSISTANCE SYSTEMS;

EID: 84922432028     PISSN: 15249050     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITS.2014.2330199     Document Type: Article

References (130)

1. R. Bishop, "Intelligent vehicle R&D: A review and contrast of programs worldwide and emerging trends," Ann. Telecommun., vol. 60, no. 3/4, pp. 228-263, Apr. 2005.
2. J. Piao and M. McDonald, "Advanced driver assistance systems from autonomous to cooperative approach," Transp. Rev., vol. 28, no. 5, pp. 659-684, 2008.
3. T. Inagaki, "Smart collaboration between humans and machines based on mutual understanding," Annu. Rev. Control, vol. 32, no. 2, pp. 253-261, Dec. 2008.
4. F. O. Flemisch et al., "The H-metaphor as a guideline for vehicle automation and interaction," NASA Langley Res. Center, Hampton, VA, USA, NASA/TM-2003-212672, Dec. 2003.
5. J. Dixon, "The design of future things (Norman D.A.; 2007) [Book Reviews]," IEEE Technol. Soc. Mag., vol. 29, no. 4, pp. 14-15, 2010.
6. A. Heide and K. Henning, "The 'cognitive car': A roadmap for research issues in the automotive sector," Annu. Rev. Control, vol. 30, no. 2, pp. 197-203, 2006.
7. L. Li, D. Wen, N.-N. Zheng, and L.-C. Shen, "Cognitive cars: A new frontier for ADAS research," IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 395-407, Mar. 2012.
8. HAVEit-Highly Automated Vehicles for Intelligent Transport, [Accessed: 03-Sep-2013]. [Online]. Available: http://www.haveit-eu.org/
9. F.-Y. Wang, "Agent-based control strategies for smart and safe vehicles," in Proc. IEEE ICVES, 2005, pp. 331-332.
10. F.-Y. Wang, "Parallel control and management for intelligent transportation systems: Concepts, architectures, applications," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 630-638, Sep. 2010.
11. M. Plöchl and J. Edelmann, "Driver models in automobile dynamics application," Veh. Syst. Dyn., vol. 45, no. 7/8, pp. 699-741, 2007.
12. C. C. Macadam, "Understanding and modeling the human driver," Veh. Syst. Dyn., vol. 40, no. 1-3, pp. 101-134, Jan. 2003.
13. J. A. Fodor, The Language of Thought, vol. 4. Cambridge, MA, USA: Harvard Univ. Press, 1975, ch. 3, p. 214.
14. A. Newell and H. A. Simon, "Computer science as empirical inquiry: Symbols and search," Commun. ACM, vol. 19, no. 3, pp. 113-126, Mar. 1976.
15. Z. W. Pylyshyn, Computation and Cognition: Toward a Foundation for Cognitive Science, vol. 88. Cambridge, MA, USA: MIT Press, 1984, ch. 1, pp. xxiii, 292.
16. K. J. W. Craik, The Nature of Explanation, vol. 42. Cambridge, MA, USA: Cambridge Univ. Press, 1943, ch. 3, p. 134.
17. R. Brooks, "A robust layered control system for a mobile robot," IEEE J. Robot. Autom., vol. RA-2, no. 1, pp. 14-23, Mar. 1986.
18. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Cambridge, MA, USA: MIT Press, 1986, ch. 1, pp. 318-362.
19. F. J. Varela, E. Thompson, and E. Rosch, The Embodied Mind: Cognitive Science and Human Experience, vol. 1992. Cambridge, MA, USA: MIT Press, 1991, p. 308.
20. E. Thelen and L. B. Smith, A Dynamic Systems Approach to the Development of Cognition and Action, vol. 512. Cambridge, MA, USA: MIT Press, 1994, p. 376.
21. T. Van Gelder, "What might cognition be, if not computation?" J. Philos., vol. 92, no. 7, pp. 345-381, Jul. 1995.
22. L. Steels and R. Brooks, Eds., The Artificial Life Route To Artificial Intelligence: Building Embodied, Situated Agents. Hillsdale, NJ, USA: Lawrence E. Psychology Press, 1995, p. 304.
23. R. F. Port and T. Van Gelder, Mind as Motion. Cambridge, MA, USA: MIT Press, 1995, pp. 195-226.
24. I. Harvey, "Untimed and misrepresented: Connectionism and the computer metaphor," AISB Q., vol. 96, pp. 20-27, 1996.
25. A. Clark, Being There: Putting Brain Body and World Together Again. Cambridge, MA, USA: MIT Press, 1997, p. 269.
26. T. Van Gelder, "The dynamical hypothesis in cognitive science," Behav. Brain Sci., vol. 21, no. 5, pp. 615-628, Oct. 1998, discussion 629-665.
27. R. Pfeifer and C. Scheier, Understanding Intelligence, vol. 288. Cambridge, MA, USA: MIT Press, 1999, ch. 5466, p. 697.
28. J. A. Seitz, "The bodily basis of thought," New Ideas Psychol., vol. 18, no. 1, pp. 23-40, Apr. 2000.
29. R. Beer, "Dynamical approaches to cognitive science," Trends Cogn. Sci., vol. 4, no. 3, pp. 91-99, 2000.
30. R. Pfeifer and J. Bongard, How the Body Shapes the Way we Think. Cambridge, MA, USA: MIT Press, 2007, p. 394.
31. J. Gibson, "The theory of affordances," in The Ecological Approach To Visual Perception. Hillsdale, NJ, USA: Lawrence Erlbaum Associates, 1986, pp. 127-143.
32. R. A. Brooks, "Intelligence without representation," Artif. Intell., vol. 47, no. 1-3, pp. 139-159, Jan. 1991.
33. L. W. Barsalou, "Grounded cognition," Annu. Rev. Psychol., vol. 59, pp. 617-645, Jan. 2008.
34. A. Clark and J. Toribio, "Doing without representing?" Synthese, vol. 101, no. 3, pp. 401-431, Dec. 2006.
35. W. Bechtel, "Representations and cognitive explanations: Assessing the dynamicist's challenge in cognitive science," Cogn. Sci., vol. 22, no. 3, pp. 295-318, Jul. 1998.
36. F. Keijzer, "Representation in dynamical and embodied cognition," Cogn. Syst. Res., vol. 3, no. 3, pp. 275-288, Sep. 2002.
37. A. Markman and E. Dietrich, "Extending the classical view of representation," Trends Cogn. Sci., vol. 4, no. 12, pp. 470-475, Dec. 2000.
38. W. M. Ramsey, Representation Reconsidered, vol. 24. Cambridge, U.K.: Cambridge Univ. Press, 2007, ch. 1, pp. 135-139.
39. M. Mirolli, "Representations in Dynamical Embodied Agents Re-Analyzing a Minimally Cognitive Model Agent," Cogn. Sci., vol. 36, no. 5, pp. 870-895, Jul. 2012.
40. R. Grush, "The emulation theory of representation: Motor control, imagery, perception," Behav. Brain Sci., vol. 27, no. 3, pp. 377-396, Jun. 2004, discussion 396-442.
41. D. M. Wolpert, R. C. Miall, and M. Kawato, "Internal models in the cerebellum," Trends Cogn. Sci., vol. 2, no. 9, pp. 338-47, Sep. 1998.
42. G. Hesslow, "Conscious thought as simulation of behaviour and perception," Trends Cogn. Sci., vol. 6, no. 6, pp. 242-247, Jun. 2002.
43. J. Decety and J. Grèzes, "The power of simulation: Imagining one's own and other's behavior," Brain Res., vol. 1079, no. 1, pp. 4-14, Mar. 2006.
44. J. Munzert, B. Lorey, and K. Zentgraf, "Cognitive motor processes: The role of motor imagery in the study of motor representations," Brain Res. Rev., vol. 60, no. 2, pp. 306-326, May 2009.
45. G. Hesslow, "The current status of the simulation theory of cognition," Brain Res., vol. 1428, pp. 71-9, Jan. 2012.
46. M. Jeannerod and V. Frak, "Mental imaging of motor activity in humans," Curr. Opin. Neurobiol., vol. 9, no. 6, pp. 735-739, Dec. 1999.
47. J. Decety, "The neurophysiological basis of motor imagery," Behav. Brain Res., vol. 77, no. 1/2, pp. 45-52, May 1996.
48. M. Jeannerod, "The representing brain: Neural correlates of motor intention and imagery," Behav. Brain Sci., vol. 17, no. 2, pp. 187-202, Jun. 1994.
49. M. Lotze and U. Halsband, "Motor imagery," J. Physiol. Paris, vol. 99, no. 4-6, pp. 386-395, 2006.
50. M. Jeannerod, "Neural simulation of action: A unifying mechanism for motor cognition," Neuro Image, vol. 14, pt. 2, no. 1, pp. S103-S109, Jul. 2001.
51. S. Hurley, "The Shared Circuits Model (SCM): How control, mirroring, simulation can enable imitation, deliberation, mindreading," Behav. Brain Sci., vol. 31, no. 1, pp. 1-22, Feb. 2008, discussion 22-58.
52. D. M. Wolpert, K. Doya, and M. Kawato, "A unifying computational framework for motor control and social interaction," Philos. Trans. R. Soc. Lond. B, Biol. Sci., vol. 358, no. 1431, pp. 593-602, Mar. 2003.
53. D. M. Wolpert and M. Kawato, "Multiple paired forward and inverse models for motor control," Neural Netw., vol. 11, no. 7/8, pp. 1317-1329, Oct. 1998.
54. D. M. Wolpert and M. S. Landy, "Motor control is decision-making," Curr. Opin. Neurobiol., vol. 22, no. 6, pp. 996-1003, Dec. 2012.
55. Y. Demiris, "Prediction of intent in robotics and multi-agent systems," Cogn. Process., vol. 8, no. 3, pp. 151-158, Sep. 2007.
56. Y. Demiris and A. Billard, "Special issue on robot learning by observation, demonstration and imitation," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. 3, pp. 254-255, Apr. 2007.
57. Y. Demiris and M. Johnson, "Distributed, predictive perception of actions: A biologically inspired robotics architecture for imitation and learning," Connect. Sci., vol. 15, no. 4, pp. 231-243, Dec. 2003.
58. Y. Demiris and B. Khadhouri, "Hierarchical attentive multiple models for execution and recognition of actions," Robot. Auton. Syst., vol. 54, no. 5, pp. 361-369, May 2006.
59. Y. Demiris and A. Meltzoff, "The Robot in the Crib: A Developmental Analysis of Imitation Skills in Infants and Robots," Infant Child Dev., vol. 17, no. 1, pp. 43-53, Jan. 2008.
60. M. Haruno, D. M. Wolpert, and M. Kawato, "Mosaic model for sensorimotor learning and control," Neural Comput., vol. 13, no. 10, pp. 2201-2220, Oct. 2001.
61. A. Meltzoff, "Understanding the intentions of others: Re-enactment of intended acts by 18-month-old children [References]," Dev. Psychol., vol. 31, no. 5, pp. 838-850, 1995.
62. A. N. Meltzoff, "The 'like me' framework for recognizing and becoming an intentional agent," Acta Psychol. (Amst.), vol. 124, no. 1, pp. 26-43, Jan. 2007.
63. A. Amditis et al., "A holistic approach to the integration of safety applications: The INSAFES subproject within the European framework programme 6 integrating project PReVENT," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 554-566, Sep. 2010.
64. E. Bertolazzi, F. Biral, M. Da Lio, A. Saroldi, and F. Tango, "Supporting drivers in keeping safe speed and safe distance: The SASPENCE subproject within the European framework programme 6 integrating project PReVENT," IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 525-538, Sep. 2010.
65. Dynamic Interactive Perception-action LEarning in Cognitive Systems-DIPLECS, [Accessed: 04-Sep-2013]. [Online]. Available: http://www.diplecs.eu/
66. E. Hollnagel and D. D. Woods, "Cognitive systems engineering: New wine in new bottles," Int. J. Human-Comput. Stud., vol. 51, no. 2, pp. 339-356, Aug. 1999.
67. E. Hollnagel, "Cognition as control: A pragmatic approach to the modelling of joint cognitive systems," unpublished. Available: http://www.ida.liu.se/eriho/images/IEEE-SMC-Cognition-as-control.pdf [accessed Feb 21 2014]
68. E. Hollnagel and D. D. Woods, Joint Cognitive Systems. New York, NY, USA: Taylor & Francis, 2005, pp. 113-133.
69. G. H. Granlund, "Organization of architectures for cognitive vision systems," in Proc. Cogn. Vis. Syst., 2006, vol. 3948, pp. 37-55.
70. M. Felsberg, J. Wiklund, and G. Granlund, "Exploratory learning structures in artificial cognitive systems," Image Vis. Comput., vol. 27, no. 11, pp. 1671-1687, Oct. 2009.
71. M. Felsberg, A. Shaukat, and D. Windridge, "Online Learning in Perception-Action Systems," presented at the 11th Eur. Conf. Comput. Vis., Crete, USA, 2010. [Online]. Available: http://liu.diva-portal.org/smash/get/diva2:342948/FULLTEXT01.pdf
72. L. Ellis, M. Felsberg, and R. Bowden, "Affordance mining: Forming perception through action," in Proc. 10th Asian Conf. Comput. Vis., vol. 6495, Lecture Notes in Computer Science, 2011, pp. 525-538.
73. A. Shaukat, D. Windridge, E. Hollnagel, L. Macchi, and J. Kittler, "Induction of the human perception-action hierarchy employed in junction-navigation scenarios," presented at the [poster n. 26] 4th Int. Conf. CogSys, Zurich, Switzerland, 2010. [Online]. Available: http://www.cogsys2010.ethz.ch/doc/cogsys2010-proceedings/cogsys2010-0026.pdf
74. N. Pugeault and R. Bowden, "Learning pre-attentive driving behaviour from holistic visual features," in Proc. ECCV, 2010, vol. 6316, pp. 154-167.
75. A. Shaukat, D. Windridge, E. Hollnagel, L. Macchi, and J. Kittler, "Adaptive, Perception-Action-based Cognitive Modelling of Human Driving Behaviour using Control, Gaze and Signal inputs," presented at the Brain Inspired Cogn. Syst., Madrid, Spain, 2010. [Online]. Available: https://www.academia.edu/544727/Adaptive-Perception-Action-based-Cognitive-Modelling-of-Human-Driving-Behaviour-using-Control-Gaze-and-Signal-inputs
76. D. Windridge, M. Felsberg, and A. Shaukat, "A framework for hierarchical perception-action learning utilizing fuzzy reasoning," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 43, no. 1, pp. 155-169, Feb. 2012.
77. D. Windridge, A. Shaukat, and E. Hollnagel, "Characterizing driver intention via hierarchical perception-action modeling," IEEE Trans. Hum.-Mach. Syst., vol. 43, no. 1, pp. 17-31, Jan. 2013.
78. D. M. Wolpert, J. Diedrichsen, and J. R. Flanagan, "Principles of sensorimotor learning," Nat. Rev. Neurosci., vol. 12, no. 12, pp. 739-51, Dec. 2011.
79. P. M. Bays and D. M. Wolpert, "Computational principles of sensorimotor control that minimize uncertainty and variability," J. Physiol., vol. 578, pt. 2, pp. 387-96, Jan. 2007.
80. Y. Demiris and B. Khadhouri, "Content-based control of goal-directed attention during human action perception," Interact. Stud., vol. 9, no. 2, pp. 353-376, 2008.
81. T. Carlson and Y. Demiris, "Collaborative control for a robotic wheelchair: Evaluation of performance, attention, workload," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 3, pp. 876-888, Jan. 2012.
82. E. Todorov, "Optimality principles in sensorimotor control," Nat. Neurosci., vol. 7, no. 9, pp. 907-15, Sep. 2004.
83. D. Liu and E. Todorov, "Evidence for the flexible sensorimotor strategies predicted by optimal feedback control," J. Neurosci. Off. J. Soc. Neurosci., vol. 27, no. 35, pp. 9354-68, Aug. 2007.
84. E. Todorov and M. I. Jordan, "Optimal feedback control as a theory of motor coordination," Nat. Neurosci., vol. 5, no. 11, pp. 1226-1235, Nov. 2002.
85. A. J. Nagengast, D. A. Braun, and D. M. Wolpert, "Optimal control predicts human performance on objects with internal degrees of freedom," PLoS Comput. Biol., vol. 5, no. 6, p. e1000419, Jun. 2009.
86. C. M. Harris, "Biomimetics of human movement: Functional or aesthetic?" Bioinspir. Biomim., vol. 4, no. 3, p. 033001, Sep. 2009.
87. C. M. Harris, "Signal-dependent noise determines motor planning," Nature, vol. 394, no. 6695, pp. 780-784, Aug. 1998.
88. P. Viviani and T. Flash, "Minimum-jerk, two-thirds power law, isochrony: Converging approaches to movement planning," J. Exp. Psychol. Hum. Percept. Perform., vol. 21, no. 1, pp. 32-53, Mar. 1995.
89. H. Hicheur, S. Vieilledent, M. J. E. Richardson, T. Flash, and A. Berthoz, "Velocity and curvature in human locomotion along complex curved paths: A comparison with hand movements," Exp. Brain Res., vol. 162, no. 2, pp. 145-54, Apr. 2005.
90. T. Flash and B. Hochner, "Motor primitives in vertebrates and invertebrates," Curr. Opin. Neurobiol., vol. 15, no. 6, pp. 660-6, Dec. 2005.
91. T. Flash, Y. Meirovitch, and A. Barliya, "Models of human movement: Trajectory planning and inverse kinematics studies," Robot. Auton. Syst., vol. 61, no. 4, pp. 330-339, Apr. 2013.
92. A. Simpkins and E. Todorov, "Complex object manipulation with hierarchical optimal control," in Proc. IEEE Symp. ADPRL, 2011 pp. 338-345.
93. E. Todorov and M. I. Jordan, "A minimal intervention principle for coordinated movement," in Proc. Adv. Neural Inf. Process. Syst., 2003, vol. 15, pp. 27-34.
94. F. J. Valero-Cuevas, M. Venkadesan, and E. Todorov, "Structured variability of muscle activations supports the minimal intervention principle of motor control," J. Neurophysiol., vol. 102, no. 1, pp. 59-68, Jul. 2009.
95. R. S. Rice, "Measuring car-driver interaction with the g-g diagram," presented at the SAE International Automotive Engineering Congress, Detroit, MI, USA, 1973, SAE Tech. Paper 730018.
96. Y. Hisaoka, M. Yamamoto, and A. Okada, "Closed-loop analysis of vehicle behavior during braking in a turn," JSAE Rev., vol. 20, no. 4, pp. 537-542, Oct. 1999.
97. W. Bartlett, O. Masory, and B. Wright, "Driver abilities in closed course testing," presented at the SAEWorld Congress, Detroit, MI, USA, 2000, SAE Tech. Paper 2000-01-0179.
98. P. Bosetti, M. Da Lio, and A. Saroldi, "On the human control of vehicles: An experimental study of acceleration," Eur. Transp. Res. Rev., vol. 6, no. 2, pp. 157-170, Jun. 2013.
99. interactIVe-Accident avoidance by intervention for Intelligent Vehicles, [Accessed: 30-May-2013]. [Online]. Available: http://www.interactive-ip.eu/
100. M. Abe, Vehicle Handling Dynamics. Oxford, U.K.: Elsevier, 2009, p. 286.
101. H. Moriguchi and H. Lipson, "Learning symbolic forward models for robotic motion planning and control," in Proc. ECAL, 2011, pp. 558-564.
102. D. Nguyen-Tuong, M. Seeger, and J. Peters, "Real-time local GP model learning," in From Motor Learning to Interaction Learning in Robots, vol. 264, Studies in Computational Intelligence. Berlin, Germany: Springer-Verlag, 2010, pp. 193-207.
103. D. Nguyen-Tuong and J. Peters, "Local Gaussian process regression for real-time model-based robot control," in Proc. IEEE/RSJ IROS, 2008, pp. 380-385.
104. D. Nguyen-Tuong and J. Peters, "Model learning for robot control: A survey," Cogn. Process., vol. 12, no. 4, pp. 319-340, Nov. 2011.
105. O. Sigaud, C. Salan, and V. Padois, "On-line regression algorithms for learning mechanical models of robots: A survey," Robot. Auton. Syst., vol. 59, no. 12, pp. 1115-1129, Dec. 2011.
106. B. Bócsi, P. Hennig, L. Csató, and J. Peters, "Learning tracking control with forward models," in Proc. IEEE Int. Conf. Robot. Autom., 2012, pp. 259-264.
107. S. Kim and A. Billard, "Estimating the non-linear dynamics of free-flying objects," Robot. Auton. Syst., vol. 60, no. 9, pp. 1108-1122, Sep. 2012.
108. A. Gijsberts and G. Metta, "Real-time model learning using incremental sparse spectrum Gaussian process regression," Neural Netw., vol. 41, pp. 59-69, May 2013.
109. D. Mitrovic, S. Klanke, and S. Vijayakumar, "Adaptive optimal feedback control with learned internal dynamics models," in From Motor Learning to Interaction Learning in Robots, vol. 264. Berlin, USA: Springer-Verlag, 2010, pp. 65-84.
110. D. Mitrovic, S. Klanke, and S. Vijayakumar, "Adaptive optimal control for redundantly actuated arms," in From Animals to Animats 10, vol. 5040. Berlin, USA: Springer-Verlag, 2008, ser. Lecture Notes in Computer Science, pp. 93-102.
111. S. Vijayakumar, A. D'Souza, and S. Schaal, "Incremental online learning in high dimensions," Neural Comput., vol. 17, no. 12, pp. 2602-2634, Dec. 2005.
112. F. Larsson, E. Jonsson, and M. Felsberg, "Simultaneously learning to recognize and control a low-cost robotic arm," Image Vis. Comput., vol. 27, no. 11, pp. 1729-1739, Oct. 2009.
113. S. G. Khan, G. Herrmann, F. L. Lewis, T. Pipe, and C. Melhuish, "Reinforcement learning and optimal adaptive control: An overview and implementation examples," Annu. Rev. Control, vol. 36, no. 1, pp. 42-59, Apr. 2012.
114. S. Schaal and C. G. Atkeson, "Learning control in robotics," IEEE Robot. Autom. Mag., vol. 17, no. 2, pp. 20-29, Jun. 2010.
115. A. G. Barto and R. S. Sutton, "Reinforcement learning in artificial intelligence," Adv. Psychol., vol. C-121, pp. 358-386, 1997.
116. R. S. Sutton, A. G. Barto, and R. J. Williams, "Reinforcement learning is direct adaptive optimal control," IEEE Control Syst. Mag., vol. 12, no. 2, pp. 19-22, Apr. 1992.
117. E. Bertolazzi, F. Biral, and M. Da Lio, "Symbolic-numeric efficient solution of optimal control problems for multibody systems," J. Comput. Appl. Math., vol. 185, no. 2, pp. 404-421, Jan. 2006.
118. E. Bertolazzi, F. Biral, and M. Da Lio, "Real-time motion planning for multibody systems: Real life application examples," Multibody Syst. Dyn., vol. 17, no. 2/3, pp. 119-139, Apr. 2007.
119. E. Bertolazzi, F. Biral, and M. Da Lio, "Symbolic-numeric indirect method for solving optimal control problems for large multibody systems: The time-optimal racing vehicle example," Multibody Syst. Dyn., vol. 13, no. 2, pp. 233-252, 2005.
120. F. Biral et al., "A web based 'Virtual Racing Car Championship' to teach vehicle dynamics and multidisciplinary design," in Proc. ASME IMECE, 2011, vol. 5, pp. 391-401.
121. B. Hommel, J. Müsseler, G. Aschersleben, and W. Prinz, "The Theory of Event Coding (TEC): A framework for perception and action planning," Behav. Brain Sci., vol. 24, no. 5, pp. 849-878, Oct. 2001, discussion 878-937.
122. A. M. Liberman and I. G. Mattingly, "The motor theory of speech perception revised," Cognition, vol. 21, no. 1, pp. 1-36, Oct. 1985.
123. B. Galantucci, C. A. Fowler, and M. T. Turvey, "Event coding as feature guessing: The lessons of the motor theory of speech perception," Behav. Brain Sci., vol. 24, no. 5, pp. 886-887, Oct. 2001.
124. A. Doshi and M. M. Trivedi, "Tactical driver behavior prediction and intent inference: A review," in Proc. IEEE ITSC, 2011, pp. 1892-1897.
125. F. Lethaus, M. R. K. Baumann, F. Köster, and K. Lemmer, "A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data," Neurocomputing, vol. 121, pp. 108-130, Dec. 2013.
126. G. Csibra and G. Gergely, "'Obsessed with goals': Functions and mechanisms of teleological interpretation of actions in humans," Acta Psychol. (Amst.), vol. 124, no. 1, pp. 60-78, Jan. 2007.
127. J. C. McCall, D. P. Wipf, M. M. Trivedi, and B. D. Rao, "Lane change intent analysis using robust operators and sparse Bayesian learning," IEEE Trans. Intell. Transp. Syst., vol. 8, no. 3, pp. 431-440, Sep. 2007.
128. D. Windridge and J. Kittler, "Perception-action learning as an epistemologically-consistent model for self-updating cognitive representation," Adv. Exp. Med. Biol., vol. 657, pp. 95-134, 2010.
129. M. Shevchenko, D. Windridge, and J. Kittler, "A linear-complexity reparameterisation strategy for the hierarchical bootstrapping of capabilities within perception-action architectures," Image Vis. Comput., vol. 27, no. 11, pp. 1702-1714, Oct. 2009.
130. V. Cossalter, M. Da Lio, R. Lot, and L. Fabbri, "A general method for the evaluation of vehicle manoeuvrability with special emphasis on motorcycles," Veh. Syst. Dyn., vol. 31, no. 2, pp. 113-135, 1999.