Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

Self-driving cars are a hot research topic in science and technology, which has a great
influence on social and economic development. Deep learning is one of the current key …

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

With the rapid development of autonomous driving technologies, a vast array of autonomous
driving algorithms and platforms have emerged. These algorithms and platforms are usually …

Estimating out-of-distribution (OOD) uncertainty is a major challenge for safely deploying
machine learning models in the open-world environment. Improved methods for OOD …

Vehicle and pedestrian detection is one of the critical tasks in autonomous driving. Since
heterogeneous techniques have been proposed, the selection of a detection system with an …

Simultaneous localization and mapping (SLAM) is a fundamental technique block in the
indoor-navigation system for most autonomous vehicles and robots. SLAM aims at building …

Lane detection in driving scenes is an important module for autonomous vehicles and
advanced driver assistance systems. In recent years, many sophisticated lane detection …

Compared to other biometrics, gait is difficult to conceal and has the advantage of being
unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to …

A self-driving car is a hot research topic in the field of the intelligent transportation system,
which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is …