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 …

In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Abstract Best-of-N (BoN) Average Displacement Error (ADE)/Final Displacement Error (FDE)
is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not …

Lately, Autonomous vehicles (AV) have been gaining traction globally owing to their huge
social, economic and environmental benefits. However, the rising safety apprehensions for …

Forecasting vehicles' future motion is crucial for real-world applications such as the
navigation of autonomous vehicles and feasibility of safety systems based on the Internet of …

Earlier trajectory prediction approaches focus on ways of capturing sequential structures
among pedestrians by using recurrent networks, which is known to have some limitations in …

This paper investigates vehicle trajectory prediction problems in real traffic scenarios by fully
harnessing the spatio-temporal dependencies between multiple vehicles. The existing GCN …

Pedestrian trajectory prediction in crowd scenes plays a significant role in intelligent
transportation systems. The main challenges are manifested in learning motion patterns and …