Analysis and evaluation of the method of determining the performance of self-driving cars at intersections in yellow light conditions using artificial intelligence

Articles in Press

Document Type : Original Article

Authors

1 Professor-School of Civil Engineering-Iran University of Science and Technology

2 Department Transportation, Faculty of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

3 School of Civil Engineering, Iran University of Science and Technology

10.22034/tri.2024.419174.3195

Abstract

The main goal of this research is to implement an artificial intelligence system based on deep learning to detect and determine the distance between traffic lights and the self-driving car and the proper performance of the car in yellow light conditions. To implement the model, the data set specific to the lights of Tehran city was used, and the model works in the framework of the Tensorflow library. To evaluate the performance of the model in different conditions, including the width of the intersection and the speed of the vehicle, the images of the training section were used, which enables the car to decide to cross the intersection or stop at the intersection using the four scenarios presented in this research. The analysis of the model results by checking the output of the model such as correctness, accuracy, recall, F1 score and speed of the models were evaluated with the results of past studies and showed that the results are correct and have higher accuracy than the existing models. Also, the best model presented in this research has an accuracy of 96% and an accuracy of 98%. Based on the traffic light data of Tehran city, this system is able to calculate the distance of the car to the traffic light with an error of less than one percent (0.8 percent), which shows the high accuracy of the model that can provide a proper performance in yellow light conditions.

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Journal of Transportation Research

Articles in Press, Accepted Manuscript
Available Online from 20 April 2024

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