Intelligent vehicles have increased their capabilities for highly and, even fully, automated
driving under controlled environments. Scene information is received using onboard …

Currently autonomous or self-driving vehicles are at the heart of academia and industry
research because of its multi-faceted advantages that includes improved safety, reduced …

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 …

The emerging development of connected and automated vehicles imposes a significant
challenge on current vehicle control and transportation systems. This paper proposes a …

This paper focuses on the real-time trajectory planning problem for autonomous vehicles
driving in realistic urban environments. To solve the complex navigation problem, we adopt …

In this paper, a trajectory optimization algorithm is proposed, which formulates the lateral
vehicle guidance task along a reference curve as a constrained optimal control problem …

This study proposes a novel integrated local trajectory planning and tracking control
(ILTPTC) framework for autonomous vehicles driving along a reference path with obstacles …

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 …

Efficient trajectory planning of autonomous vehicles in complex traffic scenarios is of interest
both academically and in automotive industry. Time efficiency and safety are of key …