Predicting the trajectory of neighboring vehicles is closely related to the driving safety of
intelligent vehicles and supports driving assistance. This article proposes a dual-attention …

In this article we study how we can transfer knowledge between mobility models that
represent different locations and means of transport. Specifically, we propose the use of …

Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy
consumption, and traffic efficiency can be significantly improved. An accurate vehicle …

Human mobility analytics using artificial intelligence (AI) has gained significant attention with
advancements in computational power and the availability of high-resolution spatial data …

Next-generation cellular networks will play a key role in the evolution of different vertical
industries. Low latency will be a major requirement in many related uses cases. This …

Autonomous vehicles hold the potential to significantly improve traffic efficiency and
advance the development of intelligent transportation systems. With the progression of …

Vehicular named data networking (V-NDN) is promising to improve the content delivery
efficiency in vehicular ad hoc networks (VANETs). However, the potential broadcast storm …

Predicting future trajectories of surrounding vehicles accurately benefits the decision-making
and motion-planning of autonomous vehicles (AVs). Physical-model-based prediction …

This paper applies a quantum machine learning technique to predict mobile users'
trajectories in mobile wireless networks by using an approach called quantum reservoir …

Vehicular Edge Computing (VEC) is a promising paradigm that provides cloud computing
services closer to vehicular users. In VEC, vehicles and communication infrastructures can …