Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic
transportation environments. In this paper, a reinforcement learning approach with value …

The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …

This paper studies the decision making problem of autonomous vehicles in traffic. We model
the interaction between an autonomous vehicle and the environment as a stochastic Markov …

In this paper, we develop an intelligent overtaking decision-making method for highway
autonomous driving. The key idea is to use reinforcement learning algorithms to learn an …

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 …

In this paper, the problem of decision making for autonomous vehicles changing lanes is
addressed by formulating multiple games in normal form for pairs of agents. This formulation …

This paper addresses the trajectory planning problem for autonomous vehicles in traffic. We
build a stochastic Markov decision process (MDP) model to represent the behaviors of the …

This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …

In this paper, we focus on computing a consistent traffic signal configuration at each junction
that optimizes multiple performance indices, ie, multi-objective traffic signal control. The multi …

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 …