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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22665

Title: Data-driven situation awareness algorithm for vehicle lane change
Authors: Yi, Dewei
Su, Jinya
Liu, Cunjia
Chen, Wen-Hua
Keywords: Clustering and classification
Filtering and prediction
Lane change
NGSIM dataset
Issue Date: 2016
Publisher: IEEE
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.
Abstract: 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.
Description: This paper is in closed access until it is published.
Sponsor: 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.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/22665
Publisher Link: http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000396
Appears in Collections:Closed Access (Aeronautical and Automotive Engineering)

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