# Coverage-based Scene Fuzzing for Virtual Autonomous Driving Testing

arXiv: https://arxiv.org/abs/2106.00873 · HTML: https://mlsanity.com/paper/2106.00873/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving](https://mlsanity.com/paper/2005.03778/) (2020)
- [Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World](https://mlsanity.com/paper/2003.07739/) (2020)
- [Waymo Public Road Safety Performance Data](https://mlsanity.com/paper/2011.00038/) (2020)
- [Evolutionary Grammar-Based Fuzzing](https://mlsanity.com/paper/2008.01150/) (2020)

## Built on by (most cited first, 4 of 4)

- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems](https://mlsanity.com/paper/2109.06404/) (2021)
- [Natural Adversaries: Fuzzing Autonomous Vehicles with Realistic Roadside Object Placements](https://mlsanity.com/paper/2409.10562/) (2024)
- [SAFLITE: Fuzzing Autonomous Systems via Large Language Models](https://mlsanity.com/paper/2412.18727/) (2024)

## Abstract (arXiv)

Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems. Testers need to handcraft the virtual driving scenes and configure various environmental settings like surrounding traffic, weather conditions, etc. Due to the huge amount of configuration possibilities, the human efforts are subject to the inefficiency in detecting flaws in industry-class autonomous driving system. This paper proposes a coverage-driven fuzzing technique to automatically generate diverse configuration parameters to form new driving scenes. Experimental results show that our fuzzing method can significantly reduce the cost in deriving new risky scenes from the initial setup designed by testers. We expect automated fuzzing will become a common practice in virtual testing for autonomous driving systems.