Data-Driven Situation Awareness Algorithm for Vehicle Lane Change

Dewei Yi, Jinya Su*, Cunjia Liu, Wen-Hua Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

7 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
PublisherIEEE Press
Number of pages6
ISBN (Electronic)9781509018895
ISBN (Print)9781509018901
Publication statusPublished - 2016
Event19th International IEEE Conference on Intelligent Transportation Systems - Windsor Oceanico Hotel, Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Nov 2016

Publication series

ISSN (Electronic)2153-0017


Conference19th International IEEE Conference on Intelligent Transportation Systems
CityRio de Janeiro

Bibliographical note

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


  • Clustering and Classification
  • Filtering and Prediction
  • Lane Change
  • NGSIM dataset


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