MODELLING DUAL CARRIAGEWAY LANE CHANGING USING NEURAL NETWORKS
John Graham Hunt, Glenn D. Lyons · 1993
The paper considers the application of neural networks to model lane changing on dual carriageways. The first approach uses prediction type neural networks to model the behaviour of individual drivers. Neural network input principally comprises a series of consecutive time scan traffic patterns describing the driver's environment and changes over time as the selected vehicle travels along a link. The neural network then predicts the new lane and position of the vehicle. A major disadvantage of this approach is the difficulty in obtaining the data required to train the neural network. An alternative approach concentrates specifically on lane changing and makes use of classification type neural networks. Input to the neural network still consists primarily of time scan traffic patterns. The format, however, is changed to facilitate the possibility of data acquisition using image processing. The neural network output classifies the input data by determining the new lane for the vehicle concerned. Progress to date is described. (A) For the covering abstract see IRRD 861794.