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Data-driven situation awareness algorithm for vehicle lane change

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conference contribution
posted on 05.10.2016 by Dewei Yi, Jinya Su, Cunjia Liu, Wen-Hua Chen
A good level of situation awareness is critical for vehicle lane change decision making. In this paper, a Data-Driven Situation Awareness (DDSA) algorithm is proposed for vehicle environment perception and projection using machine learning algorithms in conjunction with the concept of multiple models. Firstly, unsupervised learning (i.e., Fuzzy C-Mean Clustering (FCM)) is drawn to categorize the drivers’ states into different clusters using three key features (i.e., velocity, relative velocity and distance) extracted from Intelligent Driver Model (IDM). Statistical analysis is conducted on each cluster to derive the acceleration distribution, resulting in different driving models. Secondly, supervised learning classification technique (i.e., Fuzzy k-NN) is applied to obtain the model/cluster of a given driving scenario. Using the derived model with the associated acceleration distribution, Kalman filter/prediction is applied to obtain vehicle states and their projection. The publicly available NGSIM dataset is used to validate the proposed DDSA algorithm. Experimental results show that the proposed DDSA algorithm obtains better filtering and projection accuracy in comparison with the Kalman filter without clustering.

Funding

This work is jointly supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

19th International Conference on Intelligent Transportation Systems

Citation

YI, D., 2016. Data-driven situation awareness algorithm for vehicle lane change. Proceedings of 2016 19th International Conference on Intelligent Transportation Systems (ITSC 2016), Rio, 1st-4th November 2016, pp. 998-1003.

Publisher

IEEE

Version

AM (Accepted Manuscript)

Acceptance date

16/08/2016

Publication date

2016

Notes

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781509018895

ISSN

2153-0017

Language

en

Location

Rio

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