# Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

arXiv: https://arxiv.org/abs/1907.05364 · HTML: https://mlsanity.com/paper/1907.05364/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 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)
- [Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems](https://mlsanity.com/paper/2501.11924/) (2025)
- [Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios](https://mlsanity.com/paper/2110.02892/) (2021)
- [LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization](https://mlsanity.com/paper/2412.00517/) (2024)
- [Efficient Safety Testing of Autonomous Vehicles via Adaptive Search over Crash-Derived Scenarios](https://mlsanity.com/paper/2508.06575/) (2025)

## Abstract (arXiv)

Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.