# ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles

arXiv: https://arxiv.org/abs/2109.02529 · HTML: https://mlsanity.com/paper/2109.02529/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles](https://mlsanity.com/paper/2104.07830/) (2021)

## Built on by (most cited first, 6 of 6)

- [PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles](https://mlsanity.com/paper/2302.11919/) (2023)
- [Generative AI for Autonomous Driving: A Review](https://mlsanity.com/paper/2505.15863/) (2025)
- [CoPEM: Cooperative Perception Error Models for Autonomous Driving](https://mlsanity.com/paper/2211.11175/) (2022)
- [Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2305.18380/) (2023)
- [A Systematic Literature Review on Safety of the Intended Functionality for Automated Driving Systems](https://mlsanity.com/paper/2503.02498/) (2025)
- [Approaches to Analysis and Design of AI-Based Autonomous Vehicles](https://mlsanity.com/paper/2509.12169/) (2025)

## Abstract (arXiv)

In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge. Scenario-based virtual testing aims to construct specific challenges posed for the AV to overcome, albeit in virtual test environments that may not necessarily resemble the real world. This approach is aimed at identifying specific issues that arise safety concerns before an actual deployment of the AV on the road. In this paper, we describe a comprehensive test case generation approach that facilitates the design of special-purpose scenarios with meaningful parameters to form test cases, both in automated and manual ways, leveraging the strength and weaknesses of either. Furthermore, we describe how to automate the execution of test cases, and analyze the performance of the AV under these test cases.