Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios

TL;DR

This paper introduces an adaptive evaluation method for connected and automated vehicles in complex overtaking scenarios, using sparse control variates to efficiently reduce variance and accelerate testing by approximately 30 times.

Contribution

The paper proposes a novel adaptive testing approach utilizing sparse control variates to handle high-dimensional scenarios in CAV evaluation, improving efficiency and accuracy.

Findings

Accelerates evaluation process by about 30 times.

Effectively reduces estimation variance in high-dimensional scenarios.

Validates approach in complex overtaking scenarios.

Abstract

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge. Many approaches have been proposed to adaptively generate testing scenarios during the testing process. However, most existing approaches cannot be applied to complex scenarios, where the variables needed to define such scenarios are high dimensional. Towards filling this gap, the adaptive testing with sparse control variates method is proposed in this paper. Instead of adaptively generating testing scenarios, our approach evaluates CAVs' performances by adaptively utilizing the testing results. Specifically, each testing result is adjusted using multiple linear regression techniques based on control variates. As the regression coefficients can be adaptively optimized for the CAV under test, using the adjusted results can reduce the estimation variance, compared with using the testing results directly. To overcome the high dimensionality challenge, sparse control variates are utilized only for the critical variables of testing scenarios. To validate the proposed method, the high-dimensional overtaking scenarios are investigated, and the results demonstrate that our approach can further accelerate the evaluation process by about 30 times.