Context The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large set of diverse road scenarios. Literature suggests that if a vehicle is driven along a set of geometrically diverse roads—measured using various diversity measures (DMs)—it will react in a wide range of behaviours, thereby increasing the chances of observing failures, or strengthening the confidence in its safety, if no failures are observed. However, this assumption has never been tested before, nor have road DMs been assessed for their properties. Objective Our goal was to perform an exploratory study on 53 currently used and new, potentially promising road DMs. Specifically, our research questions looked into the road DMs themselves, to analyse their properties (e.g. monotonicity, computation efficiency), and to test correlation between DMs. Furthermore, we investigated the use of road DMs to determine whether the assumption that diverse test suites of roads expose diverse driving behaviour holds. Method Our empirical analysis relies on a state-of-the-art, open-source ADS testing infrastructure and uses a data set containing over 97,000 individual road geometries and matching simulation data that were collected using two driving agents. By considering test suites of various sizes and measuring their roads’ geometric diversity, we studied road DM properties, the correlation between road DMs, and the correlation between road DMs and the observed behaviour. Results Our findings reveal a strong correlation between road diversity and behavioural diversity, confirming that geometrically diverse test suites systematically exercise diverse driving behaviours. We identified <Emphasis FontCategory="SansSerif">Dist. Entropy</Emphasis> and <Emphasis FontCategory="SansSerif">Summing</Emphasis> aggregations as most effective, with <Emphasis FontCategory="SansSerif">Feature Map</Emphasis> achieving the strongest correlation of 0.95 while requiring minimal computation time. The analysed measures maintain robust correlation with behavioural diversity across test suites containing roads of varying lengths, eliminating the need for length normalisation. Conclusions These results empirically validate the fundamental assumption underlying diversity-driven ADS testing: road geometry diversity serves as a reliable proxy for behavioural diversity. For practitioners, we recommend <Emphasis FontCategory="SansSerif">Feature Map</Emphasis> or <Emphasis FontCategory="SansSerif">Dist. Entropy</Emphasis> as optimal choices, whilst <Emphasis FontCategory="SansSerif">Averaging</Emphasis>-based measures should be avoided entirely. The near-identical correlation patterns observed across architecturally different driving agents indicate that our findings generalise beyond specific ADS implementations, providing a solid foundation for diversity-driven test generation and selection.

Does road diversity really matter in testing automated driving systems?

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2026年7月25日

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Stefan Klikovits,
Vincenzo Riccio,
Ezequiel Castellano,
Ahmet Cetinkaya,
Alessio Gambi,
Paolo Arcaini

摘要

Context

The use of automated driving systems (ADSs) in the real world requires rigorous testing to ensure safety. To increase trust, ADSs should be tested on a large set of diverse road scenarios. Literature suggests that if a vehicle is driven along a set of geometrically diverse roads—measured using various diversity measures (DMs)—it will react in a wide range of behaviours, thereby increasing the chances of observing failures, or strengthening the confidence in its safety, if no failures are observed. However, this assumption has never been tested before, nor have road DMs been assessed for their properties.

Objective

Our goal was to perform an exploratory study on 53 currently used and new, potentially promising road DMs. Specifically, our research questions looked into the road DMs themselves, to analyse their properties (e.g. monotonicity, computation efficiency), and to test correlation between DMs. Furthermore, we investigated the use of road DMs to determine whether the assumption that diverse test suites of roads expose diverse driving behaviour holds.

Method

Our empirical analysis relies on a state-of-the-art, open-source ADS testing infrastructure and uses a data set containing over 97,000 individual road geometries and matching simulation data that were collected using two driving agents. By considering test suites of various sizes and measuring their roads’ geometric diversity, we studied road DM properties, the correlation between road DMs, and the correlation between road DMs and the observed behaviour.

Results

Our findings reveal a strong correlation between road diversity and behavioural diversity, confirming that geometrically diverse test suites systematically exercise diverse driving behaviours. We identified Dist. Entropy and Summing aggregations as most effective, with Feature Map achieving the strongest correlation of 0.95 while requiring minimal computation time. The analysed measures maintain robust correlation with behavioural diversity across test suites containing roads of varying lengths, eliminating the need for length normalisation.

Conclusions

These results empirically validate the fundamental assumption underlying diversity-driven ADS testing: road geometry diversity serves as a reliable proxy for behavioural diversity. For practitioners, we recommend Feature Map or Dist. Entropy as optimal choices, whilst Averaging-based measures should be avoided entirely. The near-identical correlation patterns observed across architecturally different driving agents indicate that our findings generalise beyond specific ADS implementations, providing a solid foundation for diversity-driven test generation and selection.