Title :
A Study of Driver Behavior Inference Model at Time of Lane Change using Bayesian Networks
Author :
Tezuka, Shigeki ; Soma, Hitoshi ; Tanifuji, Katsuya
Author_Institution :
Niigata Univ., Niigata
Abstract :
Recent years have brought hope that driving support systems tailored to the characteristics of each driver can be developed. To accomplish this, a driver model must be constructed that considers the driver´s psychological function when inferring driver behavior. This paper thus proposes a method to infer driver behavior by capturing time-series steering angle data at the time of lane change. The proposed method uses a static type conditional Gaussian model on Bayesian networks. By using this method, if the driver behavior of the subject and learned data nearness of features (norms) are below a certain level, it is possible to infer driver behavior with nearly 100% probability. Moreover, compared to the HMM models, this method reduces the rate of incorrect inference inclusion.
Keywords :
Bayes methods; Gaussian processes; driver information systems; hidden Markov models; inference mechanisms; time series; Bayesian networks; Gaussian model; HMM models; driver behavior; driving support systems; inference inclusion; inference model; probability; psychological function; time-series steering angle data; Bayesian methods; Context modeling; Electronic mail; Hidden Markov models; Intelligent systems; Psychology; Safety; Timing; Traffic control; Vehicle driving;
Conference_Titel :
Industrial Technology, 2006. ICIT 2006. IEEE International Conference on
Conference_Location :
Mumbai
Print_ISBN :
1-4244-0726-5
Electronic_ISBN :
1-4244-0726-5
DOI :
10.1109/ICIT.2006.372650
