With the development of state-of-art deep reinforcement learning, we can efficiently tackle
continuous control problems. But the deep reinforcement learning method for continuous …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

Reinforcement learning has steadily improved and outperform human in lots of traditional
games since the resurgence of deep neural network. However, these success is not easy to …

Recently, autonomous driving has become one of the most popular topics for smart vehicles.
However, traditional control strategies are mostly rule-based, which have poor adaptability …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …

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 …

Obstacle avoidance is an important function in self-driving vehicle control. When the vehicle
move from any arbitrary start positions to any target positions in environment, a proper path …

In performance tests, replacing humans with robotic drivers has many advantages, such as
high efficiency and high security. To realize the vehicle speed tracking control with a robotic …

The uncertainty and complexity of autonomous driving make Deep Reinforcement Learning
(DRL) appealing. DRL can optimize the expected reward by interacting with environments …

The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …