In this study, we explore the problem of adaptive vehicle trajectory control for different risk
levels. Firstly, we introduce a sliding window-based car-following scenario extraction …

Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …

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 …

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 …

The largest portion of urban congestion is caused by'phantom'traffic jams, causing
significant delay travel time, fuel waste, and air pollution. It frequently occurs in high-density …

We present a novel adaptive cruise control (ACC) system namely SAINT-ACC:{S} afety-{A}
ware {Int} elligent {ACC} system (SAINT-ACC) that is designed to achieve simultaneous …

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 …

A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …

Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs' ability of environment …

As an indispensable branch of machine learning (ML), reinforcement learning (RL) plays a
prominent role in the decision-making process of autonomous driving (AD), which enables …