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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 coulddoi:10.1109/ivs.2015.7225745 dblp:conf/ivs/ShimosakaNSK15 fatcat:wbtfd5aninh6dgl4lszmzhat24