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 …

Reinforcement learning has gradually demonstrated its decision-making ability in
autonomous driving. Reinforcement learning is learning how to map states to actions by …

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

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 …

Safety limits the application of traditional reinforcement learning (RL) methods to
autonomous driving. To address the challenge of safe exploration in autonomous driving …

Reinforcement learning (RL) can learn from past failures and has the potential to provide
self-improvement ability and higher-level intelligence. However, the current RL algorithms …

Mandatory lane-change scenarios are often challenging for autonomous vehicles in
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …

Reinforcement learning (RL) is a powerful data-driven control method that has been largely
explored in autonomous driving tasks. However, conventional RL approaches learn control …

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 …

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