Car-following Behavior Decision-making Deep Learning Model For Traffic Transferability And Safety Control

Car-following behavior is a common and important behavior in the driving process of autonomous vehicles.The research on the car-following behavior of vehicles has always been one of the hot spots in the field of traffic flow.Currently,the most widely used carfollowing model is theoretically driven.Based on the observed car-following behavior,the designer puts forward theoretical assumptions in line with driving experience and establishes a car-following model.However,some scholars pointed out that the theorydriven model cannot accurately describe the complex characteristics of car-following behavior.Thanks to the development of intelligent transportation in recent years,machine learning and big data have provided research conditions for the development of datadriven car-following models.Such models have broad application prospects.At present,the research of data-driven models is mainly focused on the condition that the carfollowing scene has not changed,and this type of model has certain hidden dangers of rear-end collision,this article focuses on the research and analysis of the above problems.Through the changes of traffic scenes,the research shows that the safety and efficiency of the models differ greatly in different scenes,and combined with the Gipps model to solve the problem of rear-end collision of the model.and the main tasks completed are as follows:(1)Data acquisition and preprocessing.The data in this article use the UAV to shoot traffic flow video to obtain the vehicle following trajectory.This method has two parts of system error,one is the jitter of the UAV,and the other is the vehicle tracking error of the video processing software.The method to reduce the error,and carry out the trajectory cleaning.(2)Verify the migration performance of the model in different scenarios.Train each data set to obtain its own model,and then simulate the model on different data sets to verify the adaptability of the model to different scene data.In addition,mixed training of different data sets and model weighting is used to balance the performance of the model in different data sets.(3)Research on the method to solve the collision problem of the LSTM model.Because the Gipps model has the characteristics of collision avoidance,this paper combines the Gipps model with the LSTM model to propose a safety-controlled Gipps-LSTM model,which can effectively avoid part of the trajectory collision problem in the simulation and can stably simulate the traffic flow.