Deep reinforcement learning (DRL) has been successfully applied to end-to-end
autonomous driving, especially in simulation environments. However, common DRL …

Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …

Even though some of the driver assistant systems have been commercialized to provide
safety and convenience to the driver, they can be applied for autonomous driving in limited …

Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …