Synthesis of Different Autonomous Vehicles Test Approaches

TL;DR

This paper proposes a co-Kriging model to synthesize results from various autonomous vehicle testing approaches, aiming to improve safety assessment accuracy while reducing costs and risks.

Contribution

It introduces a novel statistical method to combine data from different testing methods, enhancing evaluation reliability and safety insights for autonomous vehicles.

Findings

The co-Kriging model effectively integrates diverse test data.

The approach improves safety assessment accuracy.

It offers a cost-effective alternative to on-road testing.

Abstract

Currently, the most prevalent way to evaluate an autonomous vehicle is to directly test it on the public road. However, because of recent accidents caused by autonomous vehicles, it becomes controversial about whether on-road tests should be the best approach. Alternatively, people use test tracks or simulation to assess the safety of autonomous vehicles. These approaches are time-efficient and less costly, however, their credibility varies. In this paper, we propose to use a co-Kriging model to synthesize the results from different evaluation approaches, which allows us to fully utilize the information and provides an accurate, affordable, and safe way to assess a design of an autonomous vehicle.