A Biologically-Motivated Developmental System Towards Perceptual Awareness in Vehicle-Based Robots

Abstract

Existing learning networks and architectures are not suited to handle autonomous driving or driver assistance in complex, human-designed environments such as city driving. Developmental learning techniques for such “vehicle-based ” robots will be necessary. Motivated by neuroscience, we propose a system with a design based on the criteria of autonomous, open-ended development. The eventual goal is perceptual awareness – a conceptual and symbolic understanding of the sensed environment, that can be communicated, developed and refined using a teacher defined language. In the system proposed here, radars and a camera are integrated to localize nearby objects for further analysis. The attended areas are each transformed into sparse representation by a layer of developed natural filters analogous to V1. Taking that layer’s response, MILN (Multilayer In-place Learning Network) integrates unsupervised and supervised learning to selforganize efficient representations for recognition of the types of the objects. We trained our system with data from 10 different city and highway road environments and compare with other learning algorithms favorably. Results of the comparison show that this system is the only one tested that can fit all the specified criteria of development for a generalpurpose learning architecture. ∗ Both authors contributed equally to this paper.