Abstract 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 and aggregations as most effective, with 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 or as optimal choices, whilst -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.

DETOUR is a test case selector of road tests for self-driving cars, that participated to the “ICST Tool Competition 2025 - Self-Driving Car Testing Track”. DETOUR first transforms road tests, from the provided Cartesian representation, to a curvature representation based on the Frenet frame. Then, DETOUR follows a two-step process. In the first step, tests are clustered according to their similarity; this step considers both tests that have been previously executed (for which it is known whether they pass or fail) and tests that have not been executed. Then, in the second step, the tool selects, from the obtained clusters, the non-executed tests that are closer to executed tests that are known to be failing.

PALM is a generator of scenarios for UAV testing, that participated in the ICST Tool Competition 2025 - CPS-UAV Test Case Generation Track. PALM adopts Monte Carlo Tree Search (MCTS) to search for different placements of obstacles of different sizes in the mission environment. By increasing the tree depth, a new obstacle is added to the environment; instead, by adding a new node in the current tree level, the tool optimises the placement and the dimension of the last added obstacle.

Validating Autonomous Driving Systems (ADSs) is essential to ensure that the ADS meets the necessary requirements to be widely accepted. Simulation-based testing is one of the main validation approaches, in which the ADS is run in a simulated environment over different scenarios. In this context, search-based testing (SBT) is used to generate scenarios that possibly expose particular failures of the ADS under test. Most SBT approaches search for behaviors of other traffic participants, but usually fix the road map of the scenario in advance. Recently, the SBT community started investigating the search for road structures, which is particularly useful when testing specific components of the ADS, such as the lane-keeping component. However, roads can be represented in multiple ways and the impact of using a particular representation on the effectiveness of SBT is unclear. To fill this gap, this paper investigates the usage of six road representations for SBT of ADSs. As a representative SBT approach, we test the lane-keeping component of an ADS in the BeamNG.tech simulator, aiming to generate roads in which the autonomous vehicle drives off the lane. We study the effectiveness of each road representation in terms of triggered failures and also diversity of the generated roads.