Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …

Autonomous vehicles (AVs) have promised to drastically improve the convenience of driving
by releasing the burden of drivers and reducing traffic accidents with more precise control …

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …

The rapid development of artificial intelligence, especially deep learning technology, has
advanced autonomous driving systems (ADSs) by providing precise control decisions to …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …

This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

Autonomous driving is the future of the automotive industry across the globe. Many
challenges must be resolved in designing and developing successful Autonomous Driving …