Ronald Hugh Miller

An in-vehicle virtual driving assistant using neural networks

A methodology has been developed that aids drivers by suggesting a safer following distance, through the use of sensors, and optionally, vehicle to vehicle communication. Given the restricted case where there is no option to swerve into another lane, a Matlab Simulink model varies vehicle dynamics, driver reaction delay, following distance, and initial speeds when a lead vehicle suddenly decelerates. Based upon the likelihood of collision, neural networks suggest a best following distance, and the benefits of reducing reaction delay with adaptive agents are quantified.

Predicting a vehicle or pedestrian's next move with neural networks

For a given digitized view of a driving scenario, motion clusters are formed, indicating possible moving object threats to the driver. Recurrent neural networks minimize hopping between clusters and predict a clusters next location. Frequency analysis then categorizes as significant motion, and then as either head-on/away or transverse motion.