Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
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Advanced Driver Assistance Systems (ADAS) should correctly infer the intentions of the driver from what is implied by the incoming data available to it. Gaze behaviour has been found to be an indicator of information gathering, and therefore could be used to derive information about the driver's next planned objective in order to identify intended manoeuvres without relying solely on car data. Previous work has shown that significantly distinct gaze patterns precede each of the driving manoeuvres analysed indicating that eye movement data might be used as input to ADAS supplementing sensors, such as CAN-Bus, laser, or radar in order to recognise intended driving manoeuvres. Drivers' gaze behaviour was measured prior to and during the execution of different driving manoeuvres performed in a dynamic driving simulator. The efficacy of Artificial Neural Network models in learning to predict the occurrence of certain driving manoeuvres using both car and gaze data was investigated, which could successfully be demonstrated with real traffic data [1]. Issues considered included the amount of data prior to the manoeuvre to use, the relative difficulty of predicting different manoeuvres, and the accuracy of the models at different pre-manoeuvre times.