A Distributed Architecture for Cooperative Deep Learning System in Intelligent Vehicle Systems

Abstract

The recent proposed deep learning system is a promising technology to extract essential information from vision based high dimension sensors. The current intelligent vehicle systems heavily rely on vision sensors to be aware of the environment. Even though a single vehicle may have several vision sensors installed, view and range restrictions may result in information loss, leading to incorrect autopilot system decisions. To ease this limitation, a cooperative deep learning architecture for intelligent vehicle systems has been proposed in this paper. In this architecture, the observed raw data from vehicles will be processed by a partitioned deep learning system with convolution layers deployed in geographically distributed computation units. With the proposed cooperative deep learning architecture, numerous observations of a single object from various views can be utilized to increase detection accuracy. As to be expected, almost all of the currently available intelligent vehicle systems have been designed in the context of a single entity deep learning system. This means that in order to deploy a cooperative deep learning system, which has been discussed in this paper, various aspects such as computation, communication, and storage may need to be modified. A simple but representative experiment has been deployed to demonstrate the feasibility of the proposed system.