This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …

The ability to accurately predict and simulate human driving behavior is critical for the
development of intelligent transportation systems. Traditional modeling methods have …

The inclusion of in-vehicle sensors and increased intention and state recognition
capabilities enable implicit in-vehicle interaction. Starting from a systematic literature review …

Self-driving technology companies and the research community are accelerating the pace of
use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs) …

Self-driving vehicles need to continuously analyse the driving scene, understand the
behavior of other road users and predict their future trajectories in order to plan a safe …

For an efficient integration of autonomous vehicles on roads, human-like reasoning and
decision making in complex traffic situations are needed. One of the key factors to achieve …

In intelligent driving, situational assessment (SA) is an important technology, which helps to
improve the cognitive ability of intelligent vehicles in the environment. Uncertainty analysis is …

Scene understanding and future motion prediction of surrounding vehicles are crucial to
achieve safe and reliable decision-making and motion planning for autonomous driving in a …

Human driving depends on latent states, such as aggression and intent, that cannot be
directly observed. In this work, we propose a method for learning driver models that can …

Accurate and robust recognition and prediction of traffic situation plays an important role in
autonomous driving, which is a prerequisite for risk assessment and effective decision …