This paper investigates the vision-based autonomous driving with deep learning and
reinforcement learning methods. Different from the end-to-end learning method, our method …

Given the recent advances in computer vision, image processing and control systems, self-
driving vehicles has been one of the most promising and challenging research topics …

The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …

A robust steering torque control strategy for lateral tracking functionality of autonomous
driving vehicles that perform active steering, active accelerating and active braking is …

We propose an end-to-end machine learning model that integrates multi-task (MT) learning,
convolutional neural networks (CNNs), and control algorithms to achieve efficient inference …

Recent years have seen the rapid development of autonomous driving systems, which are
typically designed in a hierarchical architecture or an end-to-end architecture. The …

Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …

Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …

Deep reinforcement learning (DRL) has been successfully applied to end-to-end
autonomous driving, especially in simulation environments. However, common DRL …

With the development of state-of-art deep reinforcement learning, we can efficiently tackle
continuous control problems. But the deep reinforcement learning method for continuous …