Modeling and Prediction of Driving Behavior

Driving assistance systems that adapt to an individual driver are essential for avoiding traffic accidents because there are individual differences in the way of driving. To realize such systems, it is necessary to take account of not only observable physical quantities, but also information inferred from observation. For example, a collision avoidance system warns a driver according to an estimated probability of a future collision. Several probabilistic inference methods have been applied to modeling and recognition of driving behavior. Sakaguchi et al. inferred a probabilistic distribution of brake onset time by a static Bayesian network from various evidence, such as weather condition, methodical driving style scores, accelerator pedal release timing, and so on [1]. Dynamic Bayesian networks, including well-known hidden Markov models, have also attracted many researchers. Forbes et al. provided a decision-making model for an autonomous vehicle of a simple simulation environment [2]. Oliver et al. used a hidden Markov model for modeling and recognizing driving maneuvers at a tactical level [3]. Pentland et al. applied a switching Kalman filter for modeling and recognizing simulated driving behavior [4]. Nevertheless, only a few studies have proposed a method for predicting future driving behavior. Sakaguchi et al. designed a predictor through a Bayesian network. However, their static model was not appropriate for a time series prediction of dynamic behavior. We propose a predictive method for driving behavior in the near future using a simple dynamic Bayesian network. The proposed method shows good performance in a stop probability prediction problem [5]. In this study, we applied the proposed method to future speed prediction. Especially, we compared two simple dynamic Bayesian networks: a hidden Markov model (HMM) and a switching linear dynamic system (SLDS).