Graph attention network for Car‐Following Model under game between desired and real state

Abstract

The Car‐following model plays a major role in the study of traffic flow theories and traffic simulation. Car‐following behaviours can be regarded as a game process between the desired driving state and the real traffic state. By defining the expected car‐following state, this paper expresses the game process with a graph attention neural network, and developed the GATCF car‐following model. Different from other models that apply time series information as input, GATCF only needs instantaneous information as feature input. Two simulation cases, trajectory sequence prediction and multi‐vehicle simulation, are performed with the I‐80 data in the NGSIM project. Compared with other car‐following models such as Gipps model, Optimal Velocity model, and Intelligent Driver model, the GATCF model developed in this paper shows higher accuracy and stability.

Anisotropy safety potential field model under intelligent and connected vehicle environment and its application in car-following modeling

“…In order to swiftly create the autonomous driving judgments, Masmoudi et al (2021) initially employed YOLOv3 identification detection and then applied Qlearning and Deep Q-learning. To increase the accuracy and stability of the car-following model, Xing and Liu (2022) abstracted the vehicle following process as graph information and utilized graph neural networks to describe the game process between the intended driving status of the vehicle and the actual traffic state. Some academics have also developed simulation of the car-following model to map the following vehicle's acceleration from its speed, the vehicle headway, and the relative speed in a human-like manner in order to achieve homogeneity based on deep reinforcement learning (Zhu et al, 2018).…”