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

arXiv: https://arxiv.org/abs/1912.05434 · HTML: https://mlsanity.com/paper/1912.05434/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [Intersection focused Situation Coverage-based Verification and Validation Framework for Autonomous Vehicles Implemented in CARLA](https://mlsanity.com/paper/2112.14706/) (2021)
- [Complete Agent-driven Model-based System Testing for Autonomous Systems](https://mlsanity.com/paper/2110.12586/) (2021)
- [DIRA: Dynamic Domain Incremental Regularised Adaptation](https://mlsanity.com/paper/2205.00147/) (2022)
- [Assessing behaviour coverage in a multi-agent system simulation for autonomous vehicle testing](https://mlsanity.com/paper/2512.23445/) (2025)

## Abstract (arXiv)

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.