TY - GEN
T1 - Data-Driven Situation Awareness Algorithm for Vehicle Lane Change
AU - Yi, Dewei
AU - Su, Jinya
AU - Liu, Cunjia
AU - Chen, Wen-Hua
N1 - 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
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Clustering and Classification
KW - Filtering and Prediction
KW - Lane Change
KW - NGSIM dataset
UR - https://repository.lboro.ac.uk/articles/Data-driven_situation_awareness_algorithm_for_vehicle_lane_change/9222068
U2 - 10.1109/ITSC.2016.7795677
DO - 10.1109/ITSC.2016.7795677
M3 - Published conference contribution
SN - 9781509018901
SP - 998
EP - 1003
BT - 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
PB - IEEE Press
T2 - 19th International IEEE Conference on Intelligent Transportation Systems
Y2 - 1 November 2016 through 4 November 2016
ER -