Predicting driving behavior using inverse reinforcement learning with multiple reward functions towards environmental diversity

Predicting defensive driving is a promising technology for novel advanced driver assistance systems. In recent years, modeling driving behavior in residential roads through inverse reinforcement learning (IRL) has been attracting attention in intelligent vehicle community thanks to the superiority of this approach providing long-term prediction of fine-grained driving behavior. However, it suffers from poor performance in diverse environment due to the fact that the single reward function could not handle all the environment with large diversity. Towards this issue, a novel IRL framework with multiple reward functions to deal with environmental diversity is proposed in the paper. Specifically, the model employs Dirichlet process mixtures as a flexible and powerful Bayesian model to divide the environment into clusters and learns the parameters in each cluster simultaneously. Experimental result with expert driver behavior data shows that our model with multiple reward functions provides superior performance over the IRL model with single reward function. It also suggests that the clustering of environments based on the driving behavior of professional drivers could be useful on evaluating driving environments.