To characterize the dynamic interaction properties of heterogeneous traffic flow in the
complex human–vehicle–road environment and to enhance the safety and efficiency of …

In the foreseeable future, a complex heterogeneous traffic environment will emerge as
Connected and Autonomous Vehicles (CAVs) coexist with Human-driven Vehicles (HDVs) …

Car-following models have been extensively studied using either theory-driven models or
data-driven models. On the other hand, theory-driven models have low accuracy, whereas …

Recent works on data-driven car-following modeling have shown that a Graph
Convolutional Network (GCN)-based car-following model (CFM) can outperform other data …

With the technical support of an intelligent networking environment, autonomous driving
technology is facing a new stage of development, and the decision-making behavior of …

Car-following behavior is influenced by multiple factors. Utilizing the potential field theory,
previously determined as a comprehensive representation of these influencing factors, this …

Aiming at the characteristics of connected and autonomous vehicle (CAV) which makes
autonomous decision by perceiving the surrounding environment, a safe potential field …

Intra-driver heterogeneity is defined as transition of driver's behavior between usual and
unusual, which is an intrinsic feature of drivers while yet to be extensively explored. This …

The data–driven car–following model can realistically depict driving behavior, and is widely
used in simulating the longitudinal movements of vehicles. However, it has some …

The emergence of connected and autonomous vehicles (CAV) is of great significance to the
development of transportation systems. This paper proposes a multiple-factors aware car …