In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …

Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of
service of the transportation network. With increasing access to larger datasets of higher …

High-performance traffic flow prediction model designing, a core technology of Intelligent
Transportation System, is a long-standing but still challenging task for industrial and …

Abundant real-time vehicle trajectory information provides an important guarantee for the
driving safety and drivers' decision-making in the intelligent transportation system (ITS) …

Traffic state estimation (TSE) bifurcates into two main categories, model-driven and data-
driven (eg, machine learning, ML) approaches, while each suffers from either deficient …

Abstract Autonomous Vehicles (AVs) have made remarkable developments and are
anticipated to replace human drivers. In transitioning from human-driven vehicles to fully …

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic
conditions on the basis of road networks and traffic conditions in the past. The problem is …

This paper presents an integrated framework for physics-informed joint traffic state and
queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources …

Driver behavior models have been used as input to self-coaching, accident prevention
studies, and developing driver-assisting systems. In recent years, driver behavior …

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 …