Abstract Model predictive control is widely used in the design of autonomous driving
algorithms. However, its parameters are sensitive to dynamically varying driving conditions …

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 …

Autonomous driving vehicles can reduce congestion and improve safety while increasing
traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety …

Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …

Decision making is an essential component of autonomous vehicle technology and received
significant attention from academic and industry organizations. One of the promising …

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 …

We present a new simulation framework for learning control policies for autonomous
vehicles (AVs) based on real-world vehicle data and maps. The framework we propose …

Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a
significant component of autonomous vehicle system, which directly influences the …

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 …

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 …