We survey research on self-driving cars published in the literature focusing on autonomous
cars developed since the DARPA challenges, which are equipped with an autonomy system …

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 …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Traffic signs are one of the most important information that guide cars to travel, and the
detection of traffic signs is an important component of autonomous driving and intelligent …

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

Existing datasets for autonomous driving (AD) often lack diversity and long-range
capabilities, focusing instead on 360* perception and temporal reasoning. To address this …

Although great progress has been made in generic object detection by advanced deep
learning techniques, detecting small objects from images is still a difficult and challenging …

Automatic detection and recognition of traffic signs plays a crucial role in management of the
traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory …

Back-door attack poses a severe threat to deep learning systems. It injects hidden malicious
behaviors to a model such that any input stamped with a special pattern can trigger such …

This paper presents a comprehensive literature review on environment perception for
intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent …