# Data-Driven Modeling and Verification of Perception-Based Autonomous Systems

arXiv: https://arxiv.org/abs/2312.06848 · HTML: https://mlsanity.com/paper/2312.06848/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2023.

## Built on by (most cited first, 3 of 3)

- [Scenario-based Compositional Verification of Autonomous Systems with Neural Perception](https://mlsanity.com/paper/2504.20942/) (2025)
- [Verification of Visual Controllers via Compositional Geometric Transformations](https://mlsanity.com/paper/2507.04523/) (2025)
- [Certified Training for Convolutional Perturbations](https://mlsanity.com/paper/2607.18195/) (2026)

## Abstract (arXiv)

This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems. We assume the perception model can be decomposed into a canonical model (obtained from first principles or a simulator) and a noise model that contains the measurement noise introduced by the real environment. We focus on two types of noise, benign and adversarial noise, and develop a data-driven model for each type using generative models and classifiers, respectively. We show that the trained models perform well according to a variety of evaluation metrics based on downstream tasks such as state estimation and control. Finally, we verify the safety of two systems with high-dimensional data-driven models, namely an image-based version of mountain car (a reinforcement learning benchmark) as well as the F1/10 car, which uses LiDAR measurements to navigate a racing track.