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 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/22
Y1 - 2016/12/22
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 - http://www.scopus.com/inward/record.url?scp=85010053585&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2016.7795677
DO - 10.1109/ITSC.2016.7795677
M3 - Conference article published in proceeding or book
AN - SCOPUS:85010053585
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 998
EP - 1003
BT - 2016 IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Intelligent Transportation Systems, ITSC 2016
Y2 - 1 November 2016 through 4 November 2016
ER -