Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Pengfei Liu,Wei Fan

doi:10.7307/ptt.v33i5.3779

Abstract

Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

Highlights

The technology of connected and autonomous vehicles has developed rapidly in recent years
Features y x v xl sp vl al yl Mean absolute error (MAE) of the XGBoost model are 3.9953 and 2.6950, respectively, which are similar to the errors of the gradient boosting decision tree (GBDT) (i.e., 3.9647 and 2.7146) and smaller than the Intelligent Driver Model (IDM) (i.e., 6.2748 and 4.7164)
By comparing the prediction results, we can conclude that the XGBoost model is more reliable for vehicle trajectory prediction than the IDM

Summary

Introduction

The technology of connected and autonomous vehicles has developed rapidly in recent years. It is believed that CAVs could lead to a significant improvement of roadway safety and mobility. One important reason is that CAVs can receive information on their leading vehicles through multiple sensors and V2V technology. CAVs are able to predict their future behaviour . The Intelligent Driver Model (IDM) is a traditional method to predict the acceleration rate for the object vehicle based on the current status of the object vehicle and its leading vehicle. The IDM is a widely used car-following model which utilises an intelligent braking strategy to transit vehicle behaviour between acceleration and deceleration and creates a crash-free roadway dynamics [1]. We propose the XGBoost model, a relatively new machine learning method, to predict the acceleration rate of the object vehicle

Methods

Results

Conclusion