Research on lane change prediction model based on GBDT

Dong Li and Changxi Ma

Physica A: Statistical Mechanics and its Applications, 2022, vol. 608, issue P1

Abstract: Vehicle Change is an extremely important part of the vehicle driving process, which has great impact on traffic safety. Especially in the automatic driving vehicle, it is possible to accurately predict the development of driving safety and the development of automatic driving vehicles. This paper uses NGSIM natural number driving data set, screening the driving data of the small car, and then the lane-changing vehicle data and non-lane-changing vehicle data are tagged, and finally using the random forest model and gradient lift tree model in integrated learning to NGSIM data concentration The data of small and medium-sized vehicles in the US-101 section will be trained. Finally, the training accuracy and test accuracy of the two models are compared, and the gradient boost tree model exhibits a good effect on the prediction result of the vehicle variability, and can be used for automatic driving of the trunk prediction and judgment.

Keywords: NGSIM dataset; Lane-change; Ensemble learning; Random forest; Gradient boosting (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008482

DOI: 10.1016/j.physa.2022.128290

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