Comparative evaluation of fuzzy inference system, support vector machine and multilayer feed-forward neural network in making discretionary lane changing decisions

Title:

Comparative evaluation of fuzzy inference system, support vector machine and multilayer feed-forward neural network in making discretionary lane changing decisions

Creator:

Balal, Esmaeil and Cheu, Ruey Long

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7e0b8697-fdae-44a6-86fb-8434688f4b4f
uuid:7e0b8697-fdae-44a6-86fb-8434688f4b4f
doi:10.14311/NNW.2018.28.021

Subject:

fuzzy inference system, multilayer feed-forward neural network, support vector machine, lane change, and gap acceptance

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper compares Fuzzy Inference System (FIS), Support Vector Machine (SVM) and MultiLayer Feed-forward neural network (MLF) in modeling a driver's decision when making a discretionary lane changing move on a freeway. The FIS model has been developed and published in an earlier work by the authors, whereas the SVM and MLF models are newly developed in this research. The FIS, SVM and MLF models use the same four inputs: the gap between the subject vehicle and the leading vehicle in the original lane, the gap between the subject vehicle and the leading vehicle in the destination lane, the gap between the subject vehicle and the trailing vehicle in the destination lane, and the distance between the preceding and trailing vehicles in the destination lane. The models give a binary decision of "no, stay in the same lane" or "yes, move to the destination lane now". These models were trained and then tested with the Next Generation SIMulation (NGSIM) vehicle trajectory data. The results have shown that the FIS has the highest accuracies in making correct lane changing decisions. It recommends "yes, move to the destination lane now" with 82.2% accuracy, and "no, stay in the same lane" with 99.5% accuracy. The SVM model also outperformed the traditional gap acceptance model which was used as the benchmark. However, the MLF model was not as accurate as the gap acceptance model.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:4

Harvested from:

CDK

Metadata only:

false