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Traffic Modeling Considering Motion Uncertainties
Technical Paper
2017-01-2000
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Simulation has been considered as one of the key enablers on the development and testing for autonomous driving systems as in-vehicle and field testing can be very time-consuming, costly and often impossible due to safety concerns. Accurately modeling traffic, therefore, is critically important for autonomous driving simulation on threat assessment, trajectory planning, etc. Traditionally when modeling traffic, the motion of traffic vehicles is often considered to be deterministic and modeled based on its governing physics. However, the sensed or perceived motion of traffic vehicles can be full of errors or inaccuracy due to the inaccurate and/or incomplete sensing information. In addition, it is naturally true that any future trajectories are unknown. This paper proposes a novel modeling method on traffic considering its motion uncertainties, based on Gaussian process (GP). A probability distribution function is employed to represent traffic vehicles’ future trajectories, which are further classified based on Gaussian Mixture Model (GMM) into typical motion trajectories. Then the GP-based motion model is built from the typical motion trajectories. With this model, any potential trajectories of traffic vehicles can be simulated by sampling the GP conditional distribution. The experiment has been performed in a high-fidelity driving simulator with a full-motion base. The results have demonstrated that the proposed GP-based model can faithfully represent the uncertainties of traffic vehicles motion, thus, is suitable for the high-fidelity simulation of autonomous driving systems.
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Li, J., Wu, J., Sun, H., Jiang, Y. et al., "Traffic Modeling Considering Motion Uncertainties," SAE Technical Paper 2017-01-2000, 2017, https://doi.org/10.4271/2017-01-2000.Data Sets - Support Documents
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References
- Mardiati R. , Ismail N. , and Faroqi A. Review of Microscopic Model for Traffic Flow ARPN Journal of Engineering and Applied Sciences 9 2014
- Mohan R. and Ramadurai G. State-of-the art of macroscopic traffic flow modelling International Journal of Advances in Engineering Sciences and Applied Mathematics 5 158 176 2013
- Barceló J. and Casas J. Dynamic network simulation with AIMSUN Simulation approaches in transportation analysis Springer 2005 57 98
- Hidas P. Modelling vehicle interactions in microscopic simulation of merging and weaving Transportation Research Part C: Emerging Technologies 13 37 62 2005
- Liu R. , Van Vliet D. , and Watling D. Microsimulation models incorporating both demand and supply dynamics Transportation Research Part A: Policy and Practice 40 125 150 2006
- Shang L. and Lu H.-p. Urban microscopic traffic simulation system and its application Journal of System Simulation 1 055 2006
- Wu J. , Brackstone M. , and Mcdonald M. Fuzzy sets and systems for a motorway microscopic simulation model Fuzzy Sets & Systems 116 65 76 1999
- Errampalli M. , Okushima M. , and Akiyama T. Development of the microscopic traffic simulation model with the fuzzy logic technique Simulation 89 87 101 2013
- Fulgenzi C. , Tay C. , Spalanzani A. , and Laugier C. Probabilistic navigation in dynamic environment using rapidly-exploring random trees and gaussian processes 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems 2008 1056 1062
- Miyajima C. , Nishiwaki Y. , Ozawa K. , Wakita T. , Itou K. , Takeda K. et al. Driver modeling based on driving behavior and its evaluation in driver identification Proceedings of the IEEE 95 427 437 2007
- Wiest J. , Höffken M. , Kreßel U. , and Dietmayer K. Probabilistic trajectory prediction with gaussian mixture models Intelligent Vehicles Symposium (IV), 2012 IEEE 2012 141 146
- Anzai Y. Pattern recognition and machine learning Elsevier 2012
- Rasmussen C. E. Gaussian processes for machine learning 2006
- Neal R. M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification arXiv preprint physics/9701026 1997
- Chen Z. and Wang B. How priors of initial hyperparameters affect Gaussian process regression models arXiv preprint arXiv:1605.07906 2016
- http://www.panosim.com