Please wait a minute...
Frontiers of Information Technology & Electronic Engineering

ISSN 2095-9184

Frontiers of Information Technology & Electronic Engineering  2017, Vol. 18 Issue (2): 153-179   https://doi.org/10.1631/FITEE.1700053
  本期目录
混合-增强智能:协作与认知
南宁 郑1,2(),子熠 刘1,2,鹏举 任1,2,永强 马1,2,仕韬 陈1,2,思雨 余1,2,建儒 薛1,2,霸东 陈1,2,飞跃 王3
1. 西安交通大学人工智能与机器人研究所
2. 视觉信息处理及应用国家工程实验室
3. 复杂系统管理与控制国家重点实验室
 全文: PDF(3943 KB)  
Abstract

人工智能追求的长期目标是使机器能像人一样学习和思考。由于人类面临的许多问题具有不确定性、脆弱性和开放性,任何智能程度的机器都无法完全取代人类,这就需要将人的作用或人的认知模型引入到人工智能系统中,形成混合-增强智能的形态,这种形态是人工智能或机器智能的可行的、重要的成长模式。混合-增强智能可以分为两类基本形式:一类是人在回路的人机协同混合增强智能,另一类是将认知模型嵌入机器学习系统中,形成基于认知计算的混合智能。本文讨论人机协同的混合-增强智能的基本框架,以及基于认知计算的混合-增强智能的基本要素:直觉推理与因果模型、记忆和知识演化;特别论述了直觉推理在复杂问题求解中的作用和基本原理,以及基于记忆与推理的视觉场景理解的认知学习网络;阐述了竞争-对抗式认知学习方法,并讨论了其在自动驾驶方面的应用;最后给出混合-增强智能在相关领域的典型应用。

Key words人-机协同    混合增强智能    认知计算    直觉推理    因果模型    认知映射    视觉场景理解    自主驾驶汽车
收稿日期: 2017-01-16      出版日期: 2017-03-20
Corresponding Author(s): 南宁 郑   
 引用本文:   
. [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153-179.
南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王. 混合-增强智能:协作与认知. Front. Inform. Technol. Electron. Eng, 2017, 18(2): 153-179.
 链接本文:  
https://academic.hep.com.cn/fitee/CN/10.1631/FITEE.1700053
https://academic.hep.com.cn/fitee/CN/Y2017/V18/I2/153
1 Ando , R.K., 2007. Biocreative II gene mention tagging system at IBM Watson. Proc. 2nd BioCreative Challenge Evaluation Workshop, p.101–103.
2 Ando , R.K., Dredze , M., Zhang , T., 2005. Trec 2005 genomics track experiments at IBM Watson. 14th Text REtrieval Conf., p.1–10.
3 Atif , Y., Mathew , S.S., 2015. Building a smart campus to support ubiquitous learning. J. Amb. Intell. Human. Comput., 6(2):1–16.
4 Ball , M.O., Chen , C.Y., Hoffman , R., , 2001. Collaborative decision making in air traffic management: current and future research directions.In: Bianco, L., Dell’Olmo, P., Odoni, A.R. (Eds.), New Concepts and Methods in Air Traffic Management. Springer Berlin Heidelberg, Berlin, Germany, p.17–30.
5 Barnes , M.J., Chen , J.Y.C., Jentsch , F., , 2013. An overview of humans and autonomy for military environments: safety, types of autonomy, agents, and user interfaces. Proc. 10th Int. Conf. on Engineering Psychology and Cognitive Ergonomics: Applications and Services, p.243–252.
6 Boman , I.L., Bartfai , A., 2015. The first step in using a robot in brain injury rehabilitation: patients’ and health-care professionals’ perspective.Disab. Rehab. Assist. Technol., 10(5):365–370.
7 Bradley , A.P., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms.Patt. Recogn., 30(7):1145–1159.
8 Browne , C.B., Powley , E., Whitehouse , D., , 2012. A survey of Monte Carlo tree search methods.IEEE Trans. Comput. Intell. AI Games, 4(1):1–43.
9 Campbell , M., Hoane , A.J.Jr., Hsu , F.H., 2002. Deep Blue.Artif. Intell., 134(1-2):57–83.
10 Chen , D., Yuan , Z., Hua , G., , 2016. Multi-timescale collaborative tracking.IEEE Trans. Patt. Anal. Mach. Intell., 39(1):141–155.
11 Chen , Y., Argentinis , J.D.E., Weber , G., 2016. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research.Clin. Therap., 38(4):688–701.
12 Cimbala , S.J., 2012. Artificial Intelligence and National Security. Lexington Books, Lanham, USA.
13 Denton , E.L., Chintala , S., Fergus , R., , 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. Proc. 28th Int. Conf. on Neural Information Processing Systems, p.1486–1494.
14 de Rocquigny , E., Nicolas , D., Stefano , T., 2008. Uncertainty in Industrial Practice: a Guide to Quantitative Uncertainty Management. John Wiley & Sons, Hoboken, USA.
15 Dias , M.G., Harris , P., 1988. The effect of make-believe play on deductive reasoning.Br. J. Devel. Psychol., 6(3):207–221.
16 Dounias , G., 2003. Hybrid computational intelligence in medicine. Proc. Workshop on Intelligent and Adaptive Systems in Medicine.
17 Eakin , H., Luers , A.L., 2006. Assessing the vulnerability of social-environmental systems.Ann. Rev. Environ. Resourc., 31:1–477.
18 Ferreira , F.J., Crispim , V.R., Silva , A.X., 2010. Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques.Appl. Rad. Isot., 68(6):1012–1017.
19 Fire , A., Zhu , S.C., 2016. Learning perceptual causality from video.ACM Trans. Intell. Syst. Technol., 7(2):1–22.
20 Fischbein , H., 2002. Intuition in Science and Mathematics: an Educational Approach. Springer Science & Business Media, Berlin, Germany.
21 Fjellheim , R., Bratvold , R.R., Herbert , M.C., 2008. CODIO- collaborative decisionmaking in integrated operations. Intelligent Energy Conf. and Exhibition, p.1–7.
22 Fogel , D.B., 1995. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Wiley-IEEE Press.
23 Freyd , J.J., 1983. Representing the dynamics of a static form.Memory Cogn., 11(4):342–346.
24 Funahashi , K.I., Nakamura , Y., 1993. Approximation of dynamic systems by continuous-time recurrent neural networks.Neur. Netw., 6(6):801–806.
25 Gil , Y., Greaves , M., Hendler , J., , 2014. Amplify scientific discovery with artificial intelligence.Science, 346(6206):171–172.
26 Gilbert , G.R., Beebe , M.K., 2010. United States Department of Defense Research in Robotic Unmanned Systems for Combat Casualty Care. Report No. RTO-MP-HFM-182, Fort Detrick, Frederick, USA.
27 Goodfellow , I.J., Shlens , J., Szegedy , C., 2014a. Explaining and harnessing adversarial examples. ePrint Archive, arXiv:1412.6572.
28 Goodfellow , I.J., Pougetabadie , J., Mirza , M., , 2014b. Generative adversarial nets. Advances in Neural Information Processing Systems, p.2672–2680.
29 Graves , A., Mohamed , A.R., Hinton , G., 2013. Speech recognition with deep recurrent neural networks. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.6645–6649.
30 Graves , A., Wayne , G., Danihelka , I., 2014. Neural turing machines. ePrint Archive, arXiv:1410.5401.
31 Graves , A., Wayne , G., Reynolds , M., , 2016. Hybrid computing using a neural network with dynamic external memory.Nature, 538(7626):471–476.
32 Griffiths , T.L., Chater , N., Kemp , C., , 2010. Probabilistic models of cognition: exploring representations and inductive biases.Trends Cogn. Sci., 14(8):357–364.
33 Guilford , J.P., 1967. The Nature of Human Intelligence. McGraw-Hill, New York, USA.
34 Hagan , M.T., Demuth , H.B., Beale , M.H., , 2002. Neural Network Design. PWS Publishing Co., Boston, USA.
35 Hilovska , K., Koncz , P., 2012. Application of artificial intelligence and data mining techniques to financial markets.ACTA VSFS, 6:62–76.
36 Hiskens , I.A., Davy , R.J., 2001. Exploring the power flow solution space boundary.IEEE Trans. Power Syst., 16(3):389–395.
37 Hoffman , R., 1998. Integer Programming Models for Ground-Holding in Air Traffic Flow Management. PhD Thesis, University of Maryland, College Park, USA.
38 Holland , J.H., 1992. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press.
39 Honey , C.J., Thivierge , J.P., Sporns , O., 2010. Can structure predict function in the human brain?NeuroImage, 52(3):766–776.
40 Hu , P., Zhou , S., Ding , W.Z., , 2010. The comprehensive measurement model of the member importance in social networks. Int. Conf. on Management and Service Science, p.1–4.
41 Hu , P., Wen , C.L., Pan , D., 2013. The mutual relationship among external network, internal resource, and competitiveness of enterprises.Sci. Res. Manag., V(4):90–98 (in Chinese).
42 Hughes , D., Camp , C., O’Hara , J., , 2016. Health resource use following robot-assisted surgery versus open and conventional laparoscopic techniques in oncology: analysis of English secondary care data for radical prostatectomy and partial nephrectomy.BJU Int., 117(6):940–947.
43 Im , D.Y., Ryoo , Y.J., Kim , D.Y., , 2009. Unmanned driving of intelligent robotic vehicle. ISIS Proc. 10th Symp. on Advanced Intelligent Systems, p.213–216.
44 Ioffe , A.D., 1979. Necessary and sufficient conditions for a local minimum. 3: second order conditions and augmented duality.SIAM J. Contr. Opt., 17(2):266–288.
45 Janis , I.L., Mann , L., 1977. Decision Making: a Psychological Analysis of Conflict, Choice, and Commitment. Free Press, New York, USA.
46 Jennings , N.R., 2000. On agent-based software engineering artificial intelligence.Artif. Intell., 117(2):277–296.
47 Johnson , M., Bradshaw , J.M., Feltovich , P.J., , 2014. Coactive design: designing support for interdependence in joint activity. Electr. Eng. Math. Comput. Sci., 3(1):43–49.
48 Johnson , S., Slaughter , V., Carey , S., 1998. Whose gaze will infants follow? The elicitation of gaze-following in 12-month-olds.Devel. Sci., 1(2):233–238.
49 Jordan , M.I., 2016. On computational thinking, inferential thinking and data science. Proc. 28th ACM Symp. on Parallelism in Algorithms and Architectures, p.47.
50 Kourtzi , Z., Kanwisher , N., 2000. Activation in human MT/MST by static images with implied motion.J. Cogn. Neurosci., 12(1):48–55.
51 Lake , B.M., Salakhutdinov , R., Tenenbaum , J.B., 2015. Human-level concept learning through probabilistic program induction.Science, 350(6266):1332–1338.
52 Lake , B.M., Ullman , T.D., Tenenbaum , J.B., , 2016. Building machines that learn and think like people.Behav. Brain Sci., 22:1–101.
53 Ledford , H., 2015. How to solve the world’s biggest problems.Nature, 525:308–311.
54 Lewis , D.D., 1998. Naive (Bayes) at forty: the independence assumption in information retrieval. European Conf. on Machine Learning, p.4–15.
55 Lillicrap , T.P., Hunt , J.J., Pritzel , A., , 2016. Continuous control with deep reinforcement learning. ePrint Archive, arXiv:1509.02971.
56 Lippmann , R.P., 1987. An introduction to computing with neural nets.IEEE ASSP Mag., 4(2):4–22.
57 Liyanage , J.P., 2012. Hybrid Intelligence Through Business Socialization and Networking: Managing Complexities in the Digital Era. IGI Global, Hershey, USA.
58 Marchiori , D., Warglien , M., 2008. Predicting human interactive learning by regret-driven neural networks.Science, 319(5866):1111–1113.
59 Marr , D., 1977. Artificial intelligence—a personal view.Artif. Intell., 9(1):37–48.
60 Martin , J., 2007. The Meaning of the 21st Century: a Vital Blueprint for Ensuring Our Future. Random House.
61 McCarthy , J., Hayes , P.J., 1987. Some Philosophical Problems from the Standpoint of Artificial Intelligence. Morgan Kaufmann Publishers Inc., Burlington, USA.
62 Michalski , R.S., Carbonell , J.G., Mitchell , T.M., 1984. Machine Learning: an Artificial Intelligence Approach. Springer Science & Business Media, Berlin, Germany.
63 Mikolov , T., Karafiát , M., Burget , L., , 2010. Recurrent neural network based language model. Conf. of the Int. Speech Communication Association, p.1045–1048.
64 Minsky , M., 1961. Steps toward artificial intelligence.Proc. IRE, 49(1):8–30.
65 Mirza , M., Osindero , S., 2014. Conditional generative adversarial nets. ePrint Archive, arXiv:1411.1784.
66 Mizumoto , M., 1982. Comparison of fuzzy reasoning methods.Fuzzy Sets Syst., 8(3):253–283.
67 Mnih , V., Kavukcuoglu , K., Silver , D., , 2013. Playing Atari with deep reinforcement learning. ePrint Archive, arXiv:1312.5602.
68 Mnih , V., Kavukcuoglu , K., Silver , D., , 2015. Humanlevel control through deep reinforcement learning.Nature, 518(7540):529–533.
69 Moran , J., Desimone , R., 1985. Selective Attention Gates Visual Processing in the Extrastriate Cortex. MIT Press, Cambridge, USA.
70 Muir , B.M., 1994. Trust in automation: part I. Theoretical issues in the study of trust and human intervention in automated systems.Ergonomics, 37(11):1905–1922.
71 Nash , J.F., 1950. Equilibrium points in n-person games.PNAS, 36(1):48–49.
72 Navigli , R., Ponzetto , S.P., 2012. Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network.Artif. Intell., 193(6):217–250.
73 Newell , A., Simon , H.A., 1972. Human Problem Solving. Prentice-Hall, Englewood Cliffs, USA.
74 Nilsson , N.J., 1965. Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill, New York, USA.
75 Nissen , M.J., Bullemer , P., 1987. Attentional requirements of learning: evidence from performance measures. Cogn. Psychol., 19(1):1–32.
76 Noh , H., Hong , S., Han , B., 2015. Learning deconvolution network for semantic segmentation. IEEE Int. Conf. on Computer Vision, p.1520–1528.
77 Norman , K.A., O’Reilly , R.C., 2003. Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.Psychol. Rev., 110(4):611–646.
78 Ogura , T., Yamada , J., Yamada , S.I., , 1989. A 20 kbit associative memory lSI for artificial intelligence machines. IEEE J. Sol.-State Circ., 24(4):1014–1020.
79 O’Keefe , J., Nadel , L., 1978. The Hippocampus as a Cognitive Map. Clarendon Press, Oxford.
80 O’Leary , D.E., 2013. Artificial intelligence and big data.IEEE Intell. Syst., 28(2):96–99.
81 Pan , Y.H., 2016. Heading toward artificial intelligence 2.0.Engineering, 2(4):409–413.
82 Park , C.C., Kim , G., 2015. Expressing an image stream with a sequence of natural sentences. Advances in Neural Information Processing Systems, p.73–81.
83 Poole , D., Mackworth , A., Goebel , R., 1997. Computational Intelligence: a Logical Approach. Oxford University Press, Oxford, UK.
84 Premack , D., Premack , A.J., 1997. Infants attribute value to the goal-directed actions of self-propelled objects.J. Cogn. Neurosci., 9(6):848–856.
85 Pylyshyn , Z.W., 1984. Computation and Cognition: Toward a Foundation for Cognitive Science. The MIT Press, Cambridge, Massachusetts, USA.
86 Rachlin , H., 2012. Making IBM’s computer, Watson, human.Behav. Anal., 35(1):1–16.
87 Radford , A., Metz , L., Chintala , S., 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. ePrint Archive, arXiv:1511.06434.
88 Rashevsky , N., 1964. Man-machine interaction in automobile driving. Prog. Biocybern., 42:188–200.
89 Rasmussen , C.E., 2000. The infinite Gaussian mixture model. Advances in Neural Information Processing Systems, p.554–560.
90 Rehder , B., Hastie , R., 2001. Causal knowledge and categories: the effects of causal beliefs on categorization, induction, and similarity.J. Exp. Psychol., 130(3):323–360.
91 Russell , S.J., Norvig , P., 1995. Artificial Intelligence: a Modern Approach. Prentice Hall, Englewood Cliffs, USA.
92 Salimans , T., Goodfellow , I., Zaremba , W., , 2016. Improved techniques for training gans. Advances in Neural Information Processing Systems, p.2226–2234.
93 Salvi , C., Bricolo , E., Kounios , J., , 2016. Insight solutions are correct more often than analytic solutions.Think. Reason., 22(4):443–460.
94 Samuel , A.L., 1988. Some studies in machine learning using the game of checkers.IBM J. Res. Dev., 44(1-2):206–226.
95 Saripalli , S., Montgomery , J.F., Sukhatme , G., 2003. Visually guided landing of an unmanned aerial vehicle.IEEE Trans. Robot. Autom., 19(3):371–380.
96 Saxe , R., Carey , S., 2006. The perception of causality in infancy.ACTA Psychol., 123(1-2):144–165.
97 Schlottmann , A., Ray , E.D., Mitchell , A., , 2006. Perceived physical and social causality in animated motions: spontaneous reports and ratings.ACTA Psychol., 123(1-2):112–143.
98 Schwartz , T., Zinnikus , I., Krieger , H.U., , 2016. Hybrid teams: flexible collaboration between humans, robots and virtual agents. German Conf. on Multiagent System Technologies, p.131–146.
99 Selfridge , O.G., 1988. Pandemonium: a paradigm for learning. National Physical Laboratory Conf., p.511–531.
100 Shader , R.I., 2016. Some reflections on IBM Watson and on women’s health.Clin. Therap., 38(1):1–2.
101 Sharp , C.S., Shakernia , O., Sastry , S.S., 2001. A vision system for landing an unmanned aerial vehicle. IEEE Int. Conf. on Robotics & Automation, p.1720–1727.
102 Shrivastava , P., 1995. Ecocentric management for a risk society.Acad. Manag. Rev., 20(1):118–137.
103 Shuaibu , B.M., Norwawi , N.M., Selamat , M.H., , 2015. Systematic review of Web application security development model.Artif. Intell. Rev., 43(2):259–276.
104 Silver , D., Huang , A., Maddison , C.J., , 2016. Mastering the game of Go with deep neural networks and tree search.Nature, 529(7587):484–489.
105 Simon , H.A., 1969. The Sciences of the Artificial. MIT Press, Cambridge, USA.
106 Son , D., Lee , J., Qiao , S., , 2014. Multifunctional wearable devices for diagnosis and therapy of movement disorders.Nat. Nanotechnol., 9(5):397–404.
107 Sternberg , R.J., 1984. Beyond IQ: a triarchic theory of human intelligence. Br. J. Educat. Stud., 7(2):269–287.
108 Sternberg , R.J., Davidson , J.E., 1983. Insight in the gifted.Educat. Psychol., 18(1):51–57.
109 Stone , P., Brooks , R., Brynjolfsson , E., , 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, USA.
110 Sun , Y., Wang , X.G., Tang , X.O., 2014. Deep learning face representation from predicting 10,000 classes. IEEE Conf. on Computer Vision and Pattern Recognition, p.1891–1898.
111 Szegedy , C., Zaremba , W., Sutskever , I., , 2013. Intriguing properties of neural networks. ePrint Archive, arXiv:1312.6199.
112 Szolovits , P., Patil , R.S., Schwartz , W.B., 1988. Artificial intelligence in medical diagnosis.Ann. Int. Med., 108(1):80–87.
113 Tenenbaum , J.B., Kemp , C., Griffiths , T.L., , 2011. How to grow a mind: statistics, structure, and abstraction. Science, 331(6022):1279–1285.
114 Thielscher , M., 1997. Ramification and causality.Artif. Intell., 89(1-2):317–364.
115 Thielscher , M., 2001. The qualification problem: a solution to the problem of anomalous models.Artif. Intell., 131(1-2):1–37.
116 Thrun , S., Burgard , W., Fox , D., 1998. A probabilistic approach to concurrent mapping and localization for mobile robots.Mach. Learn., 5(3):253–271.
117 Tolman , E.C., 1948. Cognitive maps in rats and men.Psychol. Rev., 55(4):189–208.
118 Tremoulet , P.D., Feldman , J., 2000. Perception of animacy from the motion of a single object.Perception, 29(8):943–951.
119 Tversky , A., Kahneman , D., 1983. Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment.Psychol. Rev., 90(4):293–315.
120 van den Oord , A., Kalchbrenner , N., Kavukcuoglu , K., 2016. Pixel recurrent neural networks. ePrint Archive, arXiv:1601.06759.
121 Varaiya , P., 1993. Smart car on smart roads: problems of control.IEEE Trans. Autom. Contr., 38(2):195–207.
122 Waldrop , M.M., 2015. Autonomous vehicles: no drivers required.Nature, 518(7537):20–23.
123 Walters , M.L., Koay , K.L., Syrdal , D.S., , 2013. Companion robots for elderly people: using theatre to investigate potential users’ views. IEEE Ro-Man, p.691–696.
124 Wang , F.Y., 2004. Artificial societies, computational experiments, and parallel systems: a discussion on computational theory of complex social-economic systems.Compl. Syst. Compl. Sci., 1(4):25–35.
125 Wang , F.Y., Wang , X., Li , L.X., , 2016. Steps toward parallel intelligence.IEEE/CAA J. Autom. Sin., 3(4):345–348.
126 Wang , J.J., Ma , Y.Q., Chen , S.T., , 2017. Fragmentation knowledge processing and networked artificial.Seieat. Sin. Inform., 47(1):1–22.
127 Wang , L.M., Xiong , Y.J., Wang , Z., , 2016. Temporal segment networks: towards good practices for deep action recognition.LNCS, 9912:20–36.
128 Wei , P., Zheng , N.N., Zhao , Y.B., , 2013. Concurrent action detection with structural prediction. IEEE Int. Conf. on Computer Vision, p.3136–3143.
129 Wei , P., Zhao , Y., Zheng , N., , 2016. Modeling 4D human-object interactions for joint event segmentation, recognition, and object localization.IEEE Trans. Softw. Eng.
130 Williams , R.J., Zipser , D., 1989. A learning algorithm for continually running fully recurrent neural networks.Neur. Comput., 1(2):270–280.
131 Williams , W.M., Sternberg , R.J., 1988. Group intelligence: why some groups are better than others.Intelligence, 12(4):351–377.
132 Xiao , C.Y., Dymetman , M., Gardent , C., 2016. Sequencebased structured prediction for semantic parsing. Meeting of the Association for Computational Linguistics, p.1341–1350.
133 Yau , S.S., Gupta , S.K.S., Karim , F., , 2003. Smart classroom: enhancing collaborative learning using pervasive computing technology. ASEE Annual Conf. and Exposition, p.13633–13642.
134 Yegnanarayana , B., 1994. Artificial neural networks for pattern recognition.Sadhana, 19(2):189–238.
135 Youseff , L., Butrico , M., da Silva , D., 2008. Toward a unified ontology of cloud computing. Grid Computing Environments Workshop, p.1–10.
136 Zadeh , L.A., 1996. Fuzzy logic and approximate reasoning.In: Advances in Fuzzy Systems- Applications and Theory: Volume 6. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. World Scientific Publishing, Singapore, p.238–259.
137 Zhao , Y.Y., Qin , B., Liu , T., 2010. Sentiment analysis.J. Softw., 21(8):1834–1848.
138 Zheng , N.N., Tang , S.M., Cheng , H., , 2004. Toward intelligent driver-assistance and safety warning systems.IEEE Intell. Syst., 19(2):8–11.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed