This paper provides a literature review of some of the most important concepts, techniques,
and methodologies used within autonomous car systems. Specifically, we focus on two …

Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …

Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV).
With the help of deep learning and big data, it is possible to understand the between-vehicle …

Anomaly detection is crucial to the reliability and safety of rail transit systems. The rapid
development of Internet of Things (IoT) and cloud technologies together with recent …

Low-quality modalities contain not only a lot of noisy information but also some
discriminative features in RGB-Thermal (RGBT) tracking. However, the potentials of low …

Autonomous vehicles (AV) s have become a trending topic nowadays since they have the
potential to solve traffic problems, such as accidents and congestion. Although AV systems …

Due to the complex and dynamic character of intersection scenarios, the autonomous
driving strategy at intersections has been a difficult problem and a hot point in the research …

A long-sequence multi-step prediction method based on multivariate Gaussian hypothesis
and Gaussian process is proposed to model the uncertainty in the future ship path. This is a …

In order to improve driving safety and achieve more accurate decisions and planning,
autonomous vehicles are required to predict the future trajectories of the surrounding …

Traditional dehazing convolutional neural networks (CNNs) learn the feature maps only from
hazy images to the corresponding hazy-free ones, which usually lead to some important …