# An Application of Scenario Exploration to Find New Scenarios for the Development and Testing of Automated Driving Systems in Urban Scenarios

arXiv: https://arxiv.org/abs/2205.08202 · HTML: https://mlsanity.com/paper/2205.08202/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 3 of 3)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Scenarios for Development, Test and Validation of Automated Vehicles](https://mlsanity.com/paper/1801.08598/) (2018)
- [A taxonomy for quality in simulation-based development and testing of automated driving systems](https://mlsanity.com/paper/2102.06588/) (2021)

## Built on by (most cited first, 1 of 1)

- [Bayesian Optimization applied for accelerated Virtual Validation of the Autonomous Driving Function](https://mlsanity.com/paper/2507.22769/) (2025)

## Abstract (arXiv)

Verification and validation are major challenges for developing automated driving systems. A concept that gets more and more recognized for testing in automated driving is scenario-based testing. However, it introduces the problem of what scenarios are relevant for testing and which are not. This work aims to find relevant, interesting, or critical parameter sets within logical scenarios by utilizing Bayes optimization and Gaussian processes. The parameter optimization is done by comparing and evaluating six different metrics in two urban intersection scenarios. Finally, a list of ideas this work leads to and should be investigated further is presented.