Comparative evaluation of adaptive fuzzy inference system and adaptive neuro-fuzzy inference system for mandatory lane changing decisions on freeways

An essential activity of driving is making lane changes. Depending on the driver's motivation, lane changing events may be classified as discretionary or mandatory. Past research has shown that there is a difference in drivers' risk-taking behavior when making discretionary and mandatory lane changes. A lane changing decision model, based on Fuzzy Inference System (FIS), has been developed with promising accuracy. This research investigates if such model can be adapted to make decisions for mandatory lane changing moves and if it is necessary to develop a new model from scratch that is dedicated to mandatory lane changes. Vehicle trajectory data of mandatory lane changing events in the NGSIM database was extracted to form a training and a test data set for comparative evaluation. First, the FIS model developed in earlier research for discretionary lane changes was directly applied to the mandatory lane changing data. Then, an Adaptive FIS (AFIS) model was implemented by adjusting a critical parameter in the FIS-based discretionary lane changing model to optimize its performance for the mandatory lane changing training data set. Additionally, new models based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) were developed for mandatory lane changes using the training data set. These models were evaluated using the mandatory lane changing test data set. Based on the test results, one of the ANFIS models is recommended, as it gave a higher overall correct decision rate compared to the existing FIS, AFIS, and other ANFIS models.

Automated summary

This study investigates the adaptation of Fuzzy Inference System (FIS) model for mandatory lane changing decisions and compares its performance with Adaptive FIS (AFIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) models on a test data set. Results suggest that an ANFIS model is recommended for mandatory lane changes due to its higher overall correct decision rate.