This comprehensive review focuses on the Autonomous Driving System (ADS), which aims
to reduce human errors that are the reason for about 95% of car accidents. The ADS …

In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

Deep Learning (DL) systems have received exponential growth in popularity and have
become ubiquitous in our everyday life. Such systems are built on top of popular DL …

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation,
which is struggling to address the problem of challenging scenarios and speed. Inspired by …

Bridge inspection and monitoring are usually used to evaluate the status and integrity of
bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods …

Modern methods mainly regard lane detection as a problem of pixel-wise segmentation,
which is struggling to address the problems of efficiency and challenging scenarios like …

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …

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 …

Lane detection is one of the most important tasks in self-driving. Due to various complex
scenarios (eg, severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals …

Convolutional neural networks (CNNs) are usually built by stacking convolutional operations
layer-by-layer. Although CNN has shown strong capability to extract semantics from raw …