“Flow reconstruction for data-driven traffic animation” by Wilkie, Sewall and Lin
Conference:
Type(s):
Title:
- Flow reconstruction for data-driven traffic animation
Session/Category Title: Data-Driven Animation
Presenter(s)/Author(s):
Moderator(s):
Abstract:
‘Virtualized traffic’ reconstructs and displays continuous traffic flows from discrete spatio-temporal traffic sensor data or procedurally generated control input to enhance a sense of immersion in a dynamic virtual environment. In this paper, we introduce a fast technique to reconstruct traffic flows from in-road sensor measurements or procedurally generated data for interactive 3D visual applications. Our algorithm estimates the full state of the traffic flow from sparse sensor measurements (or procedural input) using a statistical inference method and a continuum traffic model. This estimated state then drives an agent-based traffic simulator to produce a 3D animation of vehicle traffic that statistically matches the original traffic conditions. Unlike existing traffic simulation and animation techniques, our method produces a full 3D rendering of individual vehicles as part of continuous traffic flows given discrete spatio-temporal sensor measurements. Instead of using a color map to indicate traffic conditions, users could visualize and fly over the reconstructed traffic in real time over a large digital cityscape.
References:
1. Aw, A., and Rascle, M. 2000. Resurrection of “second order” models of traffic flow. SIAM journal on applied mathematics, 916–938. Google ScholarDigital Library
2. Cremer, J., Kearney, J., and Willemsen, P. 1997. Directable behavior models for virtual driving scenarios. Trans. Soc. Comput. Simul. Int. 14, 2, 87–96. Google ScholarDigital Library
3. Cremer, M. 1991. Flow variables: estimation. Concise encyclopedia of traffic and transportation systems, 143–148.Google Scholar
4. Donikian, S., Moreau, G., and Thomas, G. 1999. Multimodal driving simulation in realistic urban environments. Progress in System and Robot Analysis and Control Design (LNCIS) 243, 321–332.Google Scholar
5. Evensen, G. 2003. The ensemble kalman filter: Theoretical formulation and practical implementation. Ocean dynamics 53, 4, 343–367.Google Scholar
6. Hegyi, A., Mihaylova, L., Boel, R., and Lendek, Z. 2007. Parallelized particle filtering for freeway traffic state tracking. In Proc. European Control Conference.Google Scholar
7. Helbing, D. 2001. Traffic and related self-driven many-particle systems. Reviews of modern physics 73, 4, 1067.Google Scholar
8. Houtekamer, P., and Mitchell, H. 2001. A sequential ensemble kalman filter for atmospheric data assimilation. Monthly Weather Review 129, 2, 123–137.Google ScholarCross Ref
9. Jacquet, D., Canudas De Wit, C., and Koenig, D. 2005. Traffic control and monitoring with a macroscopic model in the presence of strong congestion waves. In Proc. Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC’05. 44th IEEE Conference on, IEEE, 2164–2169.Google Scholar
10. Jacquet, D., Krstic, M., and de Wit, C. 2006. Optimal control of scalar one-dimensional conservation laws. In Proc. American Control Conference, 2006, IEEE, 6–pp.Google ScholarCross Ref
11. Jia, Z., Chen, C., Coifman, B., and Varaiya, P. 2001. The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors. In Proc. IEEE Intelligent Transportation Systems, IEEE, 536–541.Google Scholar
12. Laramee, R. S., Hauser, H., Doleisch, H., Vrolijk, B., Post, F. H., and Weiskopf, D. 2004. The state of the art in flow visualization: Dense and texture-based techniques. Computer Graphics Forum 23, 2, 203–221.Google ScholarCross Ref
13. Lebacque, J.-P., Mammar, S., and Haj-Salem, H. 2007. The aw-rascle and zhang’s model: Vacuum problems, existence and regularity of the solutions of the riemann problem. Transportation Research Part B, 41, 710–721.Google ScholarCross Ref
14. Lighthill, M., and Whitham, G. 1955. On kinematic waves. ii. a theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 229, 1178, 317–345.Google Scholar
15. 2011. MIT Intelligent Transportation Systems. http://mit.edu/its/mitsimlab.html.Google Scholar
16. 2013. Next Generation SIMulation. http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.Google Scholar
17. Papageorgiou, M., Blosseville, J., and Hadj-Salem, H. 1990. Modelling and real-time control of traffic flow on the southern part of boulevard périphérique in paris: Part i: Modelling. Transportation Research Part A: General 24, 5, 345–359.Google ScholarCross Ref
18. Pausch, R., Crea, T., and Conway, M. 1992. A literature survey for virtual environments – military flight simulator visual systems and simulator sickness. Presence: Teleoperators and Virtual Environments 1, 3, 344–363. Google ScholarDigital Library
19. Rice, J., and Van Zwet, E. 2004. A simple and effective method for predicting travel times on freeways. Intelligent Transportation Systems, IEEE Transactions on 5, 3, 200–207. Google ScholarDigital Library
20. Richards, P. 1956. Shock waves on the highway. Operations research, 42–51.Google Scholar
21. Sau, J., El Faouzi, N., Aissa, A., and de Mouzon, O. 2007. Particle filter-based real-time estimation and prediction of traffic conditions. Recent advances in stochastic modeling and data analysis: Chania, Greece, 29 May-1 June 2007, 406.Google Scholar
22. Sewall, J., Wilkie, D., Merrell, P., and Lin, M. 2010. Continuum traffic simulation. In Computer Graphics Forum, vol. 29, Wiley Online Library, 439–448.Google Scholar
23. Sewall, J., van den Berg, J., Lin, M., and Manocha, D. 2011. Virtualized traffic: Reconstructing traffic flows from discrete spatiotemporal data. Visualization and Computer Graphics, IEEE Transactions on 17, 1, 26–37. Google ScholarDigital Library
24. Sewall, J., Wilkie, D., and Lin, M. C. 2011. Interactive hybrid simulation of large-scale traffic. ACM Transaction on Graphics (Proceedings of SIGGRAPH Asia) 30, 6 (December). Google ScholarDigital Library
25. Sewall, J. 2011. Efficient, Scalable Traffic and Compressible Fluid Simulations Using Hyperbolic Models. PhD thesis, University of North Carolina at Chapel Hill. Google ScholarDigital Library
26. Thomas, G., and Donikian, S. 2000. Modelling virtual cities dedicated to behavioural animation. Computer Graphics Forum 19, 3.Google ScholarCross Ref
27. Treiber, M., Hennecke, A., and Helbing, D. 2000. Congested traffic states in empirical observations and microscopic simulations. Physical Review E 62, 2, 1805–1824.Google ScholarCross Ref
28. van den Berg, J., Sewall, J., Lin, M., and Manocha, D. 2009. Virtualized traffic: Reconstructing traffic flows from discrete spatio-temporal data. In Proc. IEEE Virtual Reality Conference., IEEE, 183–190. Google ScholarDigital Library
29. Wang, Y., and Papageorgiou, M. 2005. Real-time freeway traffic state estimation based on extended kalman filter: a general approach. Transportation Research Part B: Methodological 39, 2, 141–167.Google ScholarCross Ref
30. Wang, H., Kearney, J., Cremer, J., and Willemsen, P. 2005. Steering behaviors for autonomous vehicles in virtual environments. In Proc. IEEE Virtual Reality Conf., 155–162. Google ScholarDigital Library
31. Wilkie, D., van den Berg, J., Lin, M., and Manocha, D. 2011. Self-Aware traffic route planning. Proc. of AAAI 2011.Google Scholar
32. Wilkie, D., Sewall, J., and Lin, M. 2012. Transforming GIS data into functional road models for large-scale traffic simulation. IEEE Trans. on Visualization and Computer Graphics 18, 6. Google ScholarDigital Library
33. Work, D., Tossavainen, O., Blandin, S., Bayen, A., Iwuchukwu, T., and Tracton, K. 2008. An ensemble kalman filtering approach to highway traffic estimation using gps enabled mobile devices. In Proc. IEEE Decision and Control, IEEE, 5062–5068.Google Scholar
34. Work, D., Blandin, S., Tossavainen, O., Piccoli, B., and Bayen, A. 2010. A traffic model for velocity data assimilation. Applied Mathematics Research eXpress.Google Scholar
35. Zhang, H. 2002. A non-equilibrium traffic model devoid of gaslike behavior. Transportation Research Part B: Methodological 36, 3, 275–290.Google ScholarCross Ref
