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Volume 35 Issue 4
July  2020
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Hua Wang, Xiao-Yu He, Liu-Yang Chen, Jun-Ru Yin, Li Han, Hui Liang, Fu-Bao Zhu, Rui-Jie Zhu, Zhi-Min Gao, Ming-Liang Xu. Cognition-Driven Traffic Simulation for Unstructured Road Networks[J]. Journal of Computer Science and Technology, 2020, 35(4): 875-888. DOI: 10.1007/s11390-020-9598-y
Citation: Hua Wang, Xiao-Yu He, Liu-Yang Chen, Jun-Ru Yin, Li Han, Hui Liang, Fu-Bao Zhu, Rui-Jie Zhu, Zhi-Min Gao, Ming-Liang Xu. Cognition-Driven Traffic Simulation for Unstructured Road Networks[J]. Journal of Computer Science and Technology, 2020, 35(4): 875-888. DOI: 10.1007/s11390-020-9598-y
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Cognition-Driven Traffic Simulation for Unstructured Road Networks

  • 1 School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China;
    2 School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China;
    3 School of Software, Zhengzhou University of Light Industry, Zhengzhou 450002, China

Funds: This research is supported by the National Natural Science Foundation of China under Grant Nos. 61602425, 61572445 and 61822701, and the Doctor Fund of Zhengzhou University of Light Industry of China under Grant No. 2015BSJJ007.
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  • Author Bio:

    Hua Wang received her Ph.D. degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, in 2015. She is currently an associate professor of Zhengzhou University of Light Industry, Zhengzhou. Her main research interests include traffic animation and environment modeling.

  • Corresponding author:

    Ming-Liang Xu E-mail:iexumingliang@zzu.edu.cn

  • Received Date: March 29, 2019
  • Revised Date: March 08, 2020
  • Published Date: July 19, 2020
    Abstract
  • Dynamic changes of traffic features in unstructured road networks challenge the scene-cognitive abilities of drivers, which brings various heterogeneous traffic behaviors. Modeling traffic with these heterogeneous behaviors would have significant impact on realistic traffic simulation. Most existing traffic methods generate traffic behaviors by adjusting parameters and cannot describe those heterogeneous traffic flows in detail. In this paper, a cognition-driven trafficsimulation method inspired by the theory of cognitive psychology is introduced. We first present a visual-filtering model and a perceptual-information fusion model to describe drivers' heterogeneous cognitive processes. Then, logistic regression is used to model drivers' heuristic decision-making processes based on the above cognitive results. Lastly, we apply the high-level cognitive decision-making results to low-level traffic simulation. The experimental results show that our method can provide realistic simulations for the traffic with those heterogeneous behaviors in unstructured road networks and has nearly the same efficiency as that of existing methods.
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    • References (45)
  • [1]
    Sutherland K T, Thompson W B. Localizing in unstructured environments:Dealing with the errors. IEEE Transactions on Robotics and Automation, 1994, 10(6):740-754.
    [2]
    Harmat A, Trentini M, Sharf I. Multi-camera tracking and mapping for unmanned aerial vehicles in unstructured environments. Journal of Intelligent and Robotic Systems, 2015, 78(2):291-317.
    [3]
    Shen J, Jin X. Detailed traffic animation for urban road networks. Graphical Models, 2012, 74(5):265-282.
    [4]
    Xu M L, Xie X Z, Lv P et al. Crowd behavior simulation with emotional contagion in unexpected multihazard situations. IEEE Transactions on Systems, Man, and Cybernetics. doi: 10.1109/TSMC.2019.2899047.
    [5]
    Lu X Q, Wang Z H, Xu M L et al. A personality model for animating heterogeneous traffic behaviors. Journal of Visualization and Computer Animation, 2014, 25(3/4):363-373.
    [6]
    Garcia-Dorado I, Aliaga D G, Ukkusuri S V et al. Designing large-scale interactive traffic animations for urban modeling. Computer Graphics Forum, 2014, 33(2):411-420.
    [7]
    Li C C, Lv P, Dinesh M et al. ACSEE:Antagonistic crowd simulation model with emotional contagion and evolutionary game theory. IEEE Transactions on Affective Computing. doi: 10.1109/TAFFC.2019.2954394.
    [8]
    Chao Q, Shen J, Jin X G et al. Video-based personalized traffic learning. Graphical Models, 2013, 75(6):305-317.
    [9]
    Xu W, Zha Y W, Zhao H J et al. A vehicle model for microtraffic simulation in dynamic urban scenarios. In Proc. the 2011 IEEE International Conference on Robotics and Automation, May 2011, pp.2267-2274.
    [10]
    Xu Y S, Li Y J, Jiang L et al. The effects of situational factors and impulsiveness on drivers' intentions to violate traffic rules:Difference of driving experience. Accident Analysis & Prevention, 2014, 62:54-62.
    [11]
    Cestac J, Paran F, Delhomme P. Young drivers' sensation seeking, subjective norms, and perceived behavioral control and their roles in predicting speeding intention:How risk-taking motivations evolve with gender and driving experience. Safety Science, 2011, 49(3):424-432.
    [12]
    Kovácsová N, Rošková E, Lajunen T. Forgivingness, anger, and hostility in aggressive driving. Accident Analysis & Prevention, 2014, 62:303-308.
    [13]
    Biçaksiz P, Özkan T. Impulsivity and driver behaviors, offences and accident involvement:A systematic review. Transportation Research Part F:Traffic Psychology and Behaviour, 2016, 38:194-223.
    [14]
    Ge Y, Qu W, Jiang C et al. The effect of stress and personality on dangerous driving behavior among Chinese drivers. Accident Analysis & Prevention, 2014, 73:34-40.
    [15]
    Wang H, Xu M L, Zhu F B et al. Shadow traffic:A unified model for abnormal traffic behavior simulation. Computers & Graphics, 2018, 70(1):235-241.
    [16]
    Lu X Q, Chen W Z, Xu M L et al. AA-FVDM:An accident avoidance full velocity difference model for animating realistic street-level traffic in rural scenes. Journal of Visualization and Computer Animation, 2014, 25(1):83-97.
    [17]
    Wang H, Mao T L, Wang Z Q. Modeling interactions in continuum traffic. In Proc. the 2014 IEEE Virtual Reality Conference, March 2014, pp.123-124.
    [18]
    Xu M L, Jiang H, Jin X G et al. Crowd simulation and its applications:Recent advances. Journal of Computer Science and Technology, 2014, 29(5):799-811.
    [19]
    Wilkie D, Sewall J, Lin M C. Flow reconstruction for datadriven traffic animation. ACM Transactions on Graphics, 2013, 32(4):Article No. 89.
    [20]
    Chao Q, Deng Z G, Ren J et al. Realistic data-driven traffic flow animation using texture synthesis. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(2):1167-1178.
    [21]
    Li W, Wolinski D, Lin M C. City-scale traffic animation using statistical learning and metamodel-based optimization. ACM Transactions on Graphics, 2017, 36(6):Article No. 200.
    [22]
    Liu Y T, Wu S L, Chou K P et al. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network. In Proc. the 2016 IEEE International Conference on Fuzzy Systems, July 2016, pp.2488-2494.
    [23]
    Helbing D, Tilch B. Generalized force model of traffic dynamics. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 1998, 58(1):133-138.
    [24]
    Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 2000, 62(2):1805-1824.
    [25]
    Xu M L, Wang H, Chu S L et al. Traffic simulation and visual verification in smog. ACM Transactions on Intelligent Systems and Technology, 2019, 10(1):Article No. 3.
    [26]
    Lin W, Wong S, Li C et al. Generating believable mixedtraffic animation. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(11):3171-3183.
    [27]
    Perez O, Mukamel R, Tankus A et al. Preconscious prediction of a driver's decision using intracranial recordings. Journal of Cognitive Neuroscience, 2015, 27(8):1492-1502.
    [28]
    Berdoulat E, Vavassori D, Sastre et al. Driving anger, emotional and instrumental aggressiveness, and impulsiveness in the prediction of aggressive and transgressive driving. Accident Analysis & Prevention, 2013, 50:758-767.
    [29]
    Nasar J L, Troyer D. Pedestrian injuries due to mobile phone use in public places. Accident Analysis & Prevention, 2013, 57:91-95.
    [30]
    Møller M, Haustein S. Peer influence on speeding behavior among male drivers aged 18 and 28. Accident Analysis & Prevention, 2014, 64:92-99.
    [31]
    Fleiter J, Lennon A, Watson B. How do other people influence your driving speed? Exploring the ‘who’ and the ‘how’ of social influences on speeding from a qualitative perspective. Transportation Research Part F:Traffic Psychology and Behaviour, 2010, 13(1):49-62.
    [32]
    Hill J, Boyle N L. Driver stress as influenced by driving maneuvers and roadway conditions. Transportation Research Part F:Traffic Psychology and Behaviour, 2007, 10(3):177-186.
    [33]
    Tversky A, Kahneman D. Advances in prospect theory:Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 1992, 5(4):297-323.
    [34]
    Wood W, Rünger D. Psychology of habit. Annual Review of Psychology, 2016, 67(1):289-314.
    [35]
    Chartrand T L, Bargh J A. The chameleon effect:The perception-behavior link and social interaction. Journal of Personality and Social Psychology, 1999, 76(6):893-910.
    [36]
    Shi Z. Cognitive Science. University of Science and Technology of China Press, 2008. (in Chinese)
    [37]
    Ho C, Spence C. The Multisensory Driver:Implications for Ergonomic Car Interface Design (1st edition, Kindle edition). CRC Press, 2017.
    [38]
    Crundall D, Underwood G. Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics, 1998, 41(4):448-458.
    [39]
    Underwood G. Visual attention and the transition from novice to advanced driver. Ergonomics, 2007, 50(8):1235-1249.
    [40]
    Konstantopoulos P, Chapman P, Crundall D. Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers' eye movements in day, night and rain driving. Accident Analysis & Prevention, 2010, 42(3):827-834.
    [41]
    Yuan W. Study on car driver's dynamic visual characters test on city road[Ph.D. Thesis]. Department of Vehicle Engineering, Chang'an University, 2008. (in Chinese)
    [42]
    Bower G H, Karlin M B, Dueck A. Comprehension and memory for pictures. Memory & Cognition, 1975, 3(2):216-220.
    [43]
    Anderson J R. Cognitive Psychology and Its Implications (7th edition). Worth Publishers, 2010.
    [44]
    Harrell F E. Regression Modeling Strategies:With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Springer-Verlag, 2001.
    [45]
    Chen Y P, Wang J K, Li J et al. LiDAR-video driving dataset:Learning driving policies effectively. In Proc. the 2018 IEEE Conference on Computer Vision and Pattern Recognition, June 2018, pp. 5870-5878.
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