2 research outputs found
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
Evaluation and validation of complicated control systems are crucial to
guarantee usability and safety. Usually, failure happens in some very rarely
encountered situations, but once triggered, the consequence is disastrous.
Accelerated Evaluation is a methodology that efficiently tests those
rarely-occurring yet critical failures via smartly-sampled test cases. The
distribution used in sampling is pivotal to the performance of the method, but
building a suitable distribution requires case-by-case analysis. This paper
proposes a versatile approach for constructing sampling distribution using
kernel method. The approach uses statistical learning tools to approximate the
critical event sets and constructs distributions based on the unique properties
of Gaussian distributions. We applied the method to evaluate the automated
vehicles. Numerical experiments show proposed approach can robustly identify
the rare failures and significantly reduce the evaluation time
Synthesis of Different Autonomous Vehicles Test Approaches
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