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Decisions in Highly Automated Vehicles for Passing Urban Intersections with Support Vector Machines
Technical Paper
2018-01-0037
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Intersections and road junctions are with higher traffic accidents due to the wrong decisions of human drivers. In this paper, we consider an artificial intelligence method to mimic decisions of human drivers for highly automated vehicles at passing an urban intersection. We applied the Principal Component Analysis and uniformly scaling for the Support Vector Machine learning is applied to model time series features. The effect due to misspecification by ignoring time series issue is investigated through the comparison of predicted action accurate rate is investigated by a simulation study on the software PreScan.
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Hsu, T. and Wang, W., "Decisions in Highly Automated Vehicles for Passing Urban Intersections with Support Vector Machines," SAE Technical Paper 2018-01-0037, 2018, https://doi.org/10.4271/2018-01-0037.Data Sets - Support Documents
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References
- Statistisches Bundesamt 2013
- U.S. Department Of Transportation, National Highway Traffic Safety Administration 2010
- U.S. Department of Transportation, National Highway Traffic Safety Administration 2016
- Wu , Z. 2015
- Christianini , N. and Shawe-Taylor , J. Support Vector Machines and Other Kernel-Based Methods Cambridge Cambridge University Press 2000
- Tran , Q. and Firl , J. Modelling of Traffic Situations at Urban Intersections with Probabilistic Non-parametric Regression IEEE Intelligent Vehicles Symposium 2013 334 339
- Tran , Q. and Firl , J. Online Maneuver Recognition and Multimodal Trajectory Prediction for Intersection Assistance Using Non-parametric Regression IEEE Intelligent Vehicles Symposium 2014 918 923
- Galcera , E. , Cunningham , A.G. , Eustice , R.M. , and Olson , E. Multipolicy Decision-Making for Autonomous Driving Via Changepoint-Based Behavior Prediction Proceedings of the Robotic: Science & Systems Conference 2015
- Shirazi , M.S. , and Morris , B.T. Looking at Intersections: A Survey of Intersection Monitoring, Behavior and Safety Analysis of Recent Studies IEEE Intelligent Vehicles Symposium 2017 4 24
- Byun , H. and Lee , S.W. A Survey of Pattern Recognition Applications of Support Vector Machines International Journal of Pattern Recognition and Artificial Intelligence 17 3 459 486 2003
- Christianini , N. and Shawe-Taylor , J. Support Vector Machines and Other Kernel-Based Methods Cambridge University Press 2000
- Fu , A.W. , Keogh , E. , Lau , Y.H. , and Ratanamahatana , C.A. Scaling and Time Warping in Time Series Querying The 31st International Conference on Very Large Data Bases 2005
- Pearson , K. On Lines and Planes of Closest Fit to Systems of Points in Space Philosophical Magazine 2 559 572 1991
- Jolliffe , I.T. Principal Component Analysis Second Springer 2002
- Kondor , R. and Jebara , T. A Kernel between Sets of Vectors Proceedings of the 20th International Conference on Mechanical Learning 2003 361 368
- “www.tasssafe.com/en/products/prescan” 2017