The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents an application of NNs to autonomous vehicles. The method is a result of linear quadratic regulator (LQR) used to design the controller for autonomous vehicles in the steady state. The NN structure is designed with the reference of the driver model. Two single neurons perform as the path filter where the previewed path is weighted. The vehicle model with nonlinear dynamics represents...
In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the...
Toll system is improving at the same time of expressway greatly developing . Intelligent toll system attracts more and more attentions and gets very wide application. With the development of computer, communication and network technology, toll system also has intelligent and network management. Toll system collects unremittingly a lot of toll flow date and other traffic information.We can predict...
With a large number of traffic parameters data, it is an important issue that how to set up an efficient model of classification and prediction to identify the congestion state as soon as possible. The article provided a model of predicting traffic congestion based on the learn vector quantization neural network by making use of traffic parameters such as speed, volume and occupancy which were detected...
The work presented in this paper concerns the detection of drowsy driving based on time series measurements of driving behavior. Artificial neural networks, trained using particle swarm optimization, have been used to combine several indicators of drowsy driving based on a data set originating from a large study carried out in the driving simulator at the Swedish National Road and Transportation Institute...
The paper presents an original approach for visual identification of road direction of an autonomous vehicle using a neural network classifier called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of neural modules. We present the experimental results obtained by computer simulation of our model. The path to be identified has been quantized in 5 output directions...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.