Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …

In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

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

Aiming to improve the navigation accuracy during global navigation satellite system (GNSS)
outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding …

The ability to predict pedestrian behaviour is crucial for road safety, traffic management
systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous …

As autonomous vehicles become more common on the roads, their advancement draws on
safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper …

The prediction of pedestrian behavior is essential for automated driving in urban traffic and
has attracted increasing attention in the vehicle industry. This task is challenging because …

In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

The problem of intent understanding between highly and fully automated vehicles and
vulnerable road users (VRUs) such as pedestrians in urban traffic environment has got …

There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road
Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other …