… inverse reinforcement learning (IRL) framework [11] to automatically learn the cost function
from human driving … Inverse Reinforcement Learning Although we have obtained the …

… induced by the sub-optimality of different driving maneuvers. In terms of application domain,
… also utilize inverse reinforcement learning to learn the reward functions from real driving …

… (P-ACC) system that can learn the driver’s car-following preferences from … Inverse
Reinforcement Learning (IRL). Once activated in real-time, the P-ACC system first classifies the …

… Thanks to the recent advances in deep learning and inverse reinforcement learning (IRL), …
of accurate behavior modeling in autonomous driving and analyze the key approaches and …

… autonomous agents should be human like and therefore understandable by surrounding
human drivers… task of lane change in autonomous driving and demonstrate its superior perfor…

… driving environment, which is not a scalable approach. In this study, we extend a deep inverse
reinforcement … rating multiple contexts for autonomous driving in a dynamic environment. …

… for problems with continuous states, eg, autonomous driving. Even worse, it is necessary to
call … Therefore, this study proposes an inverse reinforcement learning method based on pre-…

… -driving vehicle. We train our trajectory scoring model on a 500+ hour real-world dataset of
expert driving … the Las Vegas Strip and demonstrated fully autonomous driving in heavy traffic, …

… Inverse reinforcement learning employed in this paper can solve this challenge by learning
… for human-like autonomous driving based on deep inverse reinforcement learning (DIRL). …

… With the help of inverse reinforcement learning, we could recover the unknown reward …
In this paper, we use the IRL approach for modeling driver behavior in autonomous driving …