TY - GEN
T1 - Driving intention inference based on dynamic bayesian networks
AU - Li, Fang
AU - Wang, Wuhong
AU - Feng, Guangdong
AU - Guo, Weiwei
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2014.
PY - 2014
Y1 - 2014
N2 - Driving intention inference can anticipate the driving risk in advance, drivers have enough time to respond and avoid accident. There are several models for identifying driving intention in recent years. However, these methods infer driving intention without considering the impact of past driver behavior on current station, and only take a few basic factors into account, such as speed, accelerate, etc., which reduce the inference accuracy to some extent. To attack this, a fourstep framework for driving intention inference is proposed. The main contribution includes driving behavior factors selecting analysis which can choose the main impacting factors, and improving the existing inferring model based on pattern recognition method. The improved method can consider the impact of past driver behavior on current station with add Auto-regression (AR). Experiments show that our framework can provide a good result for driving intention, including lane changing and braking intention inference. Moreover, compared to the tradition model, the improved model improves the correct recognition rate.
AB - Driving intention inference can anticipate the driving risk in advance, drivers have enough time to respond and avoid accident. There are several models for identifying driving intention in recent years. However, these methods infer driving intention without considering the impact of past driver behavior on current station, and only take a few basic factors into account, such as speed, accelerate, etc., which reduce the inference accuracy to some extent. To attack this, a fourstep framework for driving intention inference is proposed. The main contribution includes driving behavior factors selecting analysis which can choose the main impacting factors, and improving the existing inferring model based on pattern recognition method. The improved method can consider the impact of past driver behavior on current station with add Auto-regression (AR). Experiments show that our framework can provide a good result for driving intention, including lane changing and braking intention inference. Moreover, compared to the tradition model, the improved model improves the correct recognition rate.
KW - AR-HMM
KW - Driving intention
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=84921835379&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-54927-4_106
DO - 10.1007/978-3-642-54927-4_106
M3 - Conference contribution
AN - SCOPUS:84921835379
T3 - Advances in Intelligent Systems and Computing
SP - 1109
EP - 1119
BT - Practical Applications of Intelligent Systems - Proceedings of the 8th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2013
A2 - Wen, Zhenkun
A2 - Li, Tianrui
PB - Springer Verlag
T2 - 8th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2013
Y2 - 20 November 2013 through 23 November 2013
ER -