The state-of-the-art decision and planning approaches for autonomous vehicles have
moved away from manually designed systems, instead focusing on the utilisation of large …

Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Freespace detection is an essential component of visual perception for self-driving cars. The
recent efforts made in data-fusion convolutional neural networks (CNNs) have significantly …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Visual navigation (vNavigation) is a key and fundamental technology for artificial agents'
interaction with the environment to achieve advanced behaviors. Visual navigation for …

Autonomous car racing is a challenging task in the robotic control area. Traditional modular
methods require accurate mapping, localization and planning, which makes them …

Vehicle automatic driving technology can effectively improve the safety performance of
vehicle driving. This research is aimed at the needs of vehicle automatic driving. Combined …

Autonomous driving in urban environments requires intelligent systems that are able to deal
with complex and unpredictable scenarios. Traditional modular approaches focus on …

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …