This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …

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 …

This study aims to model two-dimensional (lateral and longitudinal) motion of an Ego
Vehicle (EV). Intelligent Driver Model (IDM) is enhanced for this purpose. All the surrounding …

Driving in a human-like manner is important for an autonomous vehicle to be a smart and
predictable traffic participant. To achieve this goal, parameters of the motion planning …

The safety and energy efficiency of autonomous vehicles in the vehicle-following driving
scenario is seriously affected by the decision-making framework. This article proposes a …

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 …

Trajectory planning has generally been framed as finding the lowest cost one from a set of
trajectory candidates, where the cost function has been hand-crafted with carefully tuned …

The driver model is the decision-making and control center of intelligent vehicle. In order to
improve the adaptability of intelligent vehicles under complex driving conditions, and …

Advanced driving assistance systems (ADAS) such as adaptive cruise control (ACC), traffic
jam assistance, and collision warning have been developed to enhance driving comfort and …

In autonomous vehicles (AVs), especially in fully AVs,“drivers” perceive vehicle operation
from a passenger's perspective. This study focuses on the perspective change of drivers …