A Theoretical Foundation of Intelligence Testing and Its Application for Intelligent Vehicles

Abstract

Intelligent vehicle testing received quickly increasing attention due to the intermittent accidents of intelligent vehicle prototypes that occurred recently. In this paper, we investigate the theoretical underpinnings of such testing and establish a rigid analyzing framework for general intelligence testing problems by borrowing the ideas of Probably Approximately Correct (PAC) learning. Our focus is on the relationship between the number of sampled scenarios and the testing efficiency. We explain various existing algorithms within this new framework and clarify some misconceptions about the reasoning underpinning these methods. We show that intelligent vehicles are testable if the testing scenarios are well defined and appropriately sampled. Moreover, we propose a sampling strategy to generate new challenging scenarios to boost testing efficiency.