Omnidirectional Active Vision for Evolutionary Car Driving

We describe a set of simulations to evolve omnidirectional active vision, an artificial retina scanning over images taken via an omnidirectional camera, being applied to a car driving task. While the retina can immediately access features in any direction, it is asked to select behaviorally-relevant features so as to drive the car on the road. Neural controllers which direct both the retinal movement and the system behavior, i.e., the speed and the steering angle of the car, are tested in three different circuits and developed through artificial evolution. We show that the evolved retina moving over the omnidirectional image successfully detects the task-relevant visual features so as to drive the car on the road. Behavioral analysis illustrates its effective strategy in algorithmic, computational, and memory resources.