Comparison of Data-Driven Human-Like Driving Models Under Different Behavior Model Frameworks

被引：1
|
作者：
Huang L. [1 ,2 ,3 ]
Huang Z. [1 ]
Wu Z. [1 ]
You F. [1 ]
Cui Z. [1 ]
Zeng Y. [1 ]
Zhong H. [1 ]
机构：
[1] School of Civil Engineering and Transportation, South China University of Technology, Guangzhou
[2] Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing
[3] State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou
来源：
Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science) | 2022年 / 50卷 / 10期
关键词：
Car-following; Data-driven; Human-like driving; Integrated driving; Lane-changing; Long short-term memory neural network;
D O I：
10.12141/j.issn.1000-565X.210783
中图分类号：
学科分类号：
摘要：
The traditional driving behavior model framework divides the driving behaviors into car-following and lane-changing, which are modeled separately. While the integrated driving behavior model framework believes that car-following and lane-changing are inseparable, so all driving behaviors are modeled as a whole. Based on these two behavioral model frameworks, this paper analyzed the performance of the data-driven human-like driving models. Firstly, it established integrated driving behavior model framework and car-following lane-changing combined model framework and then determined the input and output of the models according to the influencing factors in driving. Secondly, two combinations of car-following, lane-changing and intention recognition modules were proposed: discriminative combination and probability combination. Subsequently, the processing of the original data were carried out to build integrated driving behavior, car-following, lane-changing, and intention recognition datasets, which were used to train and calibrate the corresponding modules. Finally, the study compared the performance of the two combination models with the integrated driving behavior model in various aspects, including model accuracy, safety, robustness and migration. The results show that, when the model input and output, the parameter calibration process and the dataset are the same, the accuracy of the human-like driving model based on long short-term memory neural network (LSTM) is better than the model based on FNN. The mean square error of the model based on LSTM can reach 0.227 m2, and the mean square error of the model based on FNN is 0.470 m2. Within the LSTM-based model, the model using the car-following lane-changing combined model framework has better robustness and transferability than the model using the integrated driving behavior model framework. For the car-following lane-changing combined model, the mean square error of ±10% noise robustness can reach 1.383 m2, and the mean square error of transferability can reach 0.462 m2. For the integrated driving behavior model, the mean square error of ±10% noise robustness is 2.314 m2, and the mean square error of transferability is 0.484 m2. © 2022, Editorial Department, Journal of South China University of Technology. All right reserved.
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页码：1 / 10
页数：9
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