Intelligence Assessment of Automated Driving Systems Based on Driving Intelligence Quotient

When design, test and validate an intelligent agent, assessing its intelligence is essential. While autonomous vehicles (AVs) are deployed to a certain degree, it is still hard to assess their intelligence because it highly depends on tested scenarios but in real world tested scenarios are limited and far away from edges. Therefore, this paper attempts to propose an intelligence assessment approach for automated driving systems (ADS) based on behavior index (BI) and scenario complexity (SC). The main contributions of the scheme consist of three aspects: 1) proposing an intelligence assessment framework by following the idea of Turing test, 2) presenting a scenario bank for scenario complexity (SC) and behavior metrics for behavior index (BI), and 3) constructing a definition of driving intelligence quotient (DIQ) by the product of SC and BI. Finally, we present a lane-change scenario bank in Monte Carlo simulations to demonstrate the proposed assessment approach.