3 accepted papers
Modeling expert driving behavior is crucial for the successful implementation of human-like autonomous driving. In this paper, we propose a new sampling-based Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework. It leverages naturalistic human driving data to train the reward mode
Achieving fully autonomous driving in urban traffic scenarios is a significant challenge that necessitates balancing safety, efficiency, and compliance with traffic regulations. In this letter, we introduce a novel Curriculum Residual Hierarchical Reinforcement Learning (CR-HRL) framework. It integr
Trajectory planning plays a crucial role in autonomous driving systems, as it is tasked to generate feasible trajectories under highly dynamic scenarios within the time constraint. This paper proposes a novel two-stage coarse-to-fine framework for efficient sampling-based trajectory planning. The pr…