Abstract Nowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all
fields of technology, and Autonomous Vehicles (AV) research is one more of them. This …

In this paper, we propose a deep Q-network (DQN) method to develop an autonomous
vehicle control system to achieve trajectory design and collision avoidance with regard to …

To solve the problem of trajectory tracking lateral control in autonomous driving technology,
a model predictive control (MPC) controller trajectory tracking lateral control method …

Decision-making in autonomous driving is an emerging technology that has rapid progress
over the last decade. In single-lane scenarios, autonomous vehicles should simultaneously …

In this paper, we propose a new framework for collision-free intelligent vehicle navigation,
aiming to successfully avoid obstacles using deep reinforcement learning. The navigation …

Longitudinal control of autonomous vehicles (AVs) has long been a prominent subject and
challenge. A hierarchical longitudinal control system that integrates deep deterministic …

This paper presents a Deep Reinforcement Learning (DRL) framework adapted and trained
for Autonomous Vehicles (AVs) purposes. To do that, we propose a novel software …

The autonomous walking of an underground load-haul-dump (LHD) machine is a current
research hotspot. The route of an underground LHD machine is generally definite, and most …

This project's goal was to assess both the challenges of implementing the Deep Q-Learning
algorithm to create an autonomous car in the CARLA simulator, and the driving performance …