With the aim to improve road safety services in critical situations, vehicle trajectory and future
location prediction are important tasks. An infinite set of possible future trajectories can exit …

In recent years, the role of vehicular networks has become increasingly important for the
future of Intelligent Transportation Systems, as they are useful for providing safety …

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

We develop a Kalman filter for predicting traffic flow at urban arterials based on data
obtained from connected vehicles. The proposed algorithm is computationally efficient and …

Vehicular ad-hoc network (VANET) is an essential component of the intelligent
transportation system, that facilitates the road transportation by giving a prior alert on traffic …

Current or historic traffic states are essential input to advanced traveler information, dynamic
traffic management, and model predictive control systems. As traffic states are usually not …

Depending on driver intention and current motion state of vehicle, an infinite set of possible
future trajectories exists. In this paper we present a stochastic filter which is able to select a …

Recent researches have shown the potential benefits of using Lagrangian coordinates in
modeling mobile sensor data such as GPS, Bluetooth, Wi-Fi, and cellphone probe data …

Vehicle state estimation and path prediction, which usually involve Kalman filter and motion
model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative …

The fast and frequent movement of vehicles creates many challenges in vehicular networks,
such as handling regular topological changes. Predicting a vehicle's future location by …