Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …

Self-driving technology companies and the research community are accelerating the pace of
use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs) …

Autonomous vehicle (AV) is expected to reshape the future transportation system, and its
decision-making is one of the most critical modules. Many current decision-making modules …

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 …

In this paper, a prediction-and cost function-based algorithm (PCB) is proposed to
implement robust freeway driving in autonomous vehicles. A prediction engine is built to …

In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust
single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle …

Today, one of the major challenges that autonomous vehicles are facing is the ability to drive
in urban environments. Such a task requires communication between autonomous vehicles …

In this paper we describe cooperation and social dilemmas in multiagent systems, with an
analogy applied to road traffic. Cooperative human drivers, based on their perception of trust …

To better regulate traffic flow and reduce the potential impacts due to uncoordinated lane
changes, we proposed a real-time optimal lane selection (OLS) algorithm by using the …

Learning from human driver's strategies for solving complex and potentially dangerous
situations including interaction with other road users has the potential to improve decision …