Autonomous vehicles require safe motion planning in uncertain environments, which are
largely caused by surrounding vehicles. In this paper, a driving environment uncertainty …

Over the last few years, we have witnessed tremendous progress on many subtasks of
autonomous driving including perception, motion forecasting, and motion planning …

A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The
filter utilizes recurrent neural networks to learn the vehicle's geometrical and kinematic …

Over the last decades, deep neural networks (DNNs) have penetrated all fields of science
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …

Automatic vehicle following and cruising under all road geometry characteristics (curvature,
slope, and superelevation) and traffic conditions without putting the vehicle occupants or …

Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle,
which are limited by the line of sight and occlusions caused by any other elements on the …

Trajectory planning is a critical function in an autonomous vehicle, which is about generating
a local spatio-temporal curve with safety, traverse efficiency, and comfort factors considered …

Accurate and reliable localization is of great importance for autonomous vehicles (AV).
Mainstream localization approaches in autonomous vehicles (AV) are limited by the …

Safe and efficient decision-making and path planning is a challenging problem for
autonomous driving in highway because of numerous dynamic vehicles around the ego …

Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on
accurate and continuous vehicle location information. The traditional localization method …