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SVM based Intention Inference and Motion Planning at Uncontrolled Intersection

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Issue Date
2019-07
Publisher
IFAC Secretariat
Citation
IFAC-PapersOnLine, Vol.52 No.8, pp.356-361
Abstract
This paper presents a support vector machine (SVM) based intention inference and motion planning algorithm for autonomous driving through uncontrolled intersection. Intention of target vehicles is inferred using SVM with intersection map to predict the future state of targets. A cross point, which has a highest collision probability, is estimated using predicted target state considering prediction uncertainty. Longitudinal acceleration is determined using model predictive control approach considering the predicted cross point. The proposed algorithm is validated via simulation and vehicle tests. The results show the accurate intention inference and human-like motion planning at uncontrolled intersection scenarios. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
ISSN
2405-8963
URI
https://hdl.handle.net/10371/187057
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Journal Papers (저널논문_기계항공공학부)
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Journal Papers (저널논문_기계항공공학부)
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