Microscopic traffic models (MTMs) are widely used for assessing the impacts of (connected)
autonomous vehicles ((C) AVs). These models utilize car‐following (CF) and lane‐changing …

In a mixed traffic environment of human and autonomous driving, it is crucial for an
autonomous vehicle to predict the lane change intentions and trajectories of vehicles that …

Car-following is a control process in which a following vehicle adjusts its acceleration to
keep a safe distance from the lead vehicle. Recently, there has been a booming of data …

To ensure safe driving in dynamic environments, autonomous vehicles should possess the
capability to accurately predict the lane change intentions of surrounding vehicles in …

Car-following (CF) behavior is a fundamental of traffic flow modeling; it can be used for the
virtual testing of connected and automated vehicles and the simulation of various types of …

Car-following is the most common driving scenario where a following vehicle follows a lead
vehicle in the same lane. One crucial factor of car-following behavior is driving style which …

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles.
The interactions not only affect their driving behaviors but also influence the traffic flow. This …

Model-based and learning-based methods are two major types of methodologies to model
car following behaviors. Model-based methods describe the car-following behaviors with …

Reinforcement learning (RL)–based car-following (CF) control strategies have attracted
significant attention in academia, emerging as a prominent research topic in recent years …

This paper proposes a novel hybrid car-following model: the physics-informed conditional
generative adversarial network (PICGAN), designed to enhance multi-step car-following …