An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification

TL;DR

This paper presents an agency-directed multi-agent system approach for generating effective simulation tests to verify autonomous vehicles, significantly outperforming pseudo-random methods in effectiveness and realism.

Contribution

It introduces a novel agency-driven test generation method using multi-agent systems that enhances test effectiveness and realism in autonomous vehicle simulation verification.

Findings

Agency-directed testing doubles the effectiveness of test generation.

Generated tests are more realistic and robust.

Method outperforms pseudo-random test generation in efficiency and effectiveness.

Abstract

Simulation-based verification is beneficial for assessing otherwise dangerous or costly on-road testing of autonomous vehicles (AV). This paper addresses the challenge of efficiently generating effective tests for simulation-based AV verification using software testing agents. The multi-agent system (MAS) programming paradigm offers rational agency, causality and strategic planning between multiple agents. We exploit these aspects for test generation, focusing in particular on the generation of tests that trigger the precondition of an assertion. On the example of a key assertion we show that, by encoding a variety of different behaviours respondent to the agent's perceptions of the test environment, the agency-directed approach generates twice as many effective tests than pseudo-random test generation, while being both efficient and robust. Moreover, agents can be encoded to behave naturally without compromising the effectiveness of test generation. Our results suggest that generating tests using agency-directed testing significantly improves upon random and simultaneously provides more realistic driving scenarios.