Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

Current autonomous driving systems are composed of a perception system and a decision
system. Both of them are divided into multiple subsystems built up with lots of human …

Conventional cameras employed in autonomous vehicle (AV) systems support many
perception tasks, but are challenged by low-light or high dynamic range scenes, adverse …

In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …

With the recent advancements in artificial intelligence, there is a growing expectation that
fully autonomous driving vehicles will soon become a reality, leading to significant societal …

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 …

Automation is becoming a large component of many industries in the 21st century, in areas
ranging from manufacturing, communications and transportation. Automation has offered …

One of the most exciting technology breakthroughs in the last few years has been the rise of
deep learning. State-of-the-art deep learning models are being widely deployed in …

We present a novel compact deep multi-task learning model to handle various autonomous
driving perception tasks in one forward pass. The model performs multiple views of semantic …

This paper presents a shallow end-to-end vision-based deep learning approach for
autonomous vehicle driving in traffic scenarios. The primary objectives include lane keeping …