Recent years, autonomous driving has always been a hot topic, which is regarded as an
important means to shape the future of transport. However, safety is always the essential of …

To address the problem of the lack of interpretability of vehicle trajectory prediction models
based on deep learning, this paper proposes a Fusion Neural network with the Spatio …

Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can
help ATM to forecast potential dangers and effectively provide instructions for safely …

Recent years have witnessed the advent and prevalence of deep learning that have
provoked storms in ITS (Intelligent Transportation Systems). Consequently, traditional ML …

Urban road intersections are the most common complex tra c conditions, especially in
intersections without signal lights, when a right-of-way conflict occurs between vehicles, the …

The future motion prediction of vehicles in the front is widely valued for its great potential to
improve a vehicle's safety, fuel consumption, and efficiency. However, due to the uncertainty …

Sažetak The ability to predict the motion of vehicles is essential for autonomous vehicles.
Aiming at the problem that existing models cannot make full use of the external parameters …

In this paper, we design an ambient vehicle path prediction model based on deep learning.
The most important goal of the autonomous driving system is to ensure the safety of …

Safe autonomous driving assistance systems are actively being developed based on
Convolutional Neural Network (CNN). Unlike understanding the road environment through …

This paper proposed a method based on STGCN (Spatial-Temporal Graph Convolutional
Network) for predicting vehicles trajectories on highway. This method takes interaction …