Forecasting the Passage Time of the Queue of Highly Automated Vehicles Based on Neural Networks in the Services of Cooperative Intelligent Transport Systems

Sergey Aliukov,Vitalii Varkentin,Aleksandr Marusin,Sultan Zhankaziev,Alexey Marusin,Vladimir Shepelev,Aleksandr Gritsenko

doi:10.3390/math10020282

Sergey Aliukov, Vitalii Varkentin + Show 5 more

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https://doi.org/10.3390/math10020282

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Abstract

This study addresses the problem of non-stop passage by vehicles at intersections based on special processing of data from a road camera or video detector. The basic task in this article is formulated as a forecast for the release time of a controlled intersection by non-group vehicles, taking into account their classification and determining their number in the queue. To solve the problem posed, the YOLOv3 neural network and the modified SORT object tracker were used. The work uses a heuristic region-based algorithm in classifying and measuring the parameters of the queue of vehicles. On the basis of fuzzy logic methods, a model for predicting the passage time of a queue of vehicles at controlled intersections was developed and refined. The elaborated technique allows one to reduce the forced number of stops at controlled intersections of connected vehicles by choosing the optimal speed mode. The transmission of information on the predicted delay time at a controlled intersection is locally possible due to the V2X communication of the road controller equipment, and in the horizontally scaled mode due to the interaction of HAV—the Digital Road Model.

Highlights

There is a continuous and uncontrolled increase in the number of cars in many major cities around the world
The simulation was carried out at various scenarios of vehicle traffic conditions on the intersection lanes; the results show that the use of a fuzzy system allows one to unload the intersection over a minimum time under different traffic conditions and uneven speed of entering the intersection
This paper describes a predictive model allowing one to calculate the speed at which the probability of passing controlled intersections without stopping increases by using the transport section data

Summary

Introduction

There is a continuous and uncontrolled increase in the number of cars in many major cities around the world This phenomenon gives rise to many issues in the management of the transport system, such as traffic jams, environmental degradation, and the growth of traffic accidents [1,2,3,4,5,6,7]. The combination of V2V and V2I, known as the Vehicle to Everything (V2X) link, provides an estimate of traffic density [15,16,17,18,19,20,21,22,23] This ensures calculation of the optimal speed of the vehicle to minimize the engine idling when stopping at intersections [24,25,26,27,28,29,30]. Lack of timely and accurate data from drivers on the operation mode of traffic lights and the presence of a transport queue at the intersection do not allow them to choose a speed that ensures non-stop travel

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Conclusion