Neural-network based modeling for stop&go behavior in real traffic flow

The first step towards an autonomous vehicle is adaptive cruise control (ACC) and stop&go maneuver systems since these kinds of systems adapt the speed of a vehicle to that of the preceding one (ACC) and get the vehicle to stop if the lead vehicle stops. There have been attempts to model stop&am...

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Bibliographic Details
Main Authors: Ghaffari, A., Khodayari, A., Panahi, A., Alimardani, F.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Biological neural networks
Control systems
Data models
Intelligent Automation
Mathematical model
modeling
neural network
Stop&go maneuver
Vehicles
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Description
Summary:The first step towards an autonomous vehicle is adaptive cruise control (ACC) and stop&go maneuver systems since these kinds of systems adapt the speed of a vehicle to that of the preceding one (ACC) and get the vehicle to stop if the lead vehicle stops. There have been attempts to model stop&go waves via microscopic and macroscopic traffic models. But modeling the maneuver itself is presented only in a few studies. The purpose of this study is to design two neural network models for stop&go maneuver. These models are designed based on the real traffic data and model the velocity and longitudinal distance (spacing) with the front vehicle for the vehicle which performs a stop&go maneuver. Using the field data, the performance of the presented models is validated and compared with the real traffic datasets. The results show very close compatibility between the model outputs and maneuvers in real traffic flow.
ISSN:1541-1672
1941-1294
DOI:10.1109/IS.2012.6335245