# Approaches to Analysis and Design of AI-Based Autonomous Vehicles

arXiv: https://arxiv.org/abs/2509.12169 · HTML: https://mlsanity.com/paper/2509.12169/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 7 of 7)

- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [PAMPC: Perception-Aware Model Predictive Control for Quadrotors](https://mlsanity.com/paper/1804.04811/) (2018)
- [A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety](https://mlsanity.com/paper/1904.02697/) (2019)
- [Testing Rare Downstream Safety Violations via Upstream Adaptive Sampling of Perception Error Models](https://mlsanity.com/paper/2209.09674/) (2022)
- [Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC](https://mlsanity.com/paper/2001.02307/) (2020)
- [ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2109.02529/) (2021)
- [PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles](https://mlsanity.com/paper/2302.11919/) (2023)

## Abstract (arXiv)

Artificial intelligence (AI) models are becoming key components in an autonomous vehicle (AV), especially in handling complicated perception tasks. However, closing the loop through AI-based feedback may pose significant risks on reliability of autonomous driving due to very limited understanding about the mechanism of AI-driven perception processes. To overcome it, this paper aims to develop tools for modeling, analysis, and synthesis for a class of AI-based AV; in particular, their closed-loop properties, e.g., stability, robustness, and performance, are rigorously studied in the statistical sense. First, we provide a novel modeling means for the AI-driven perception processes by looking at their error characteristics. Specifically, three fundamental AI-induced perception uncertainties are recognized and modeled by Markov chains, Gaussian processes, and bounded disturbances, respectively. By means of that, the closed-loop stochastic stability (SS) is established in the sense of mean square, and then, an SS control synthesis method is presented within the framework of linear matrix inequalities (LMIs). Besides the SS properties, the robustness and performance of AI-based AVs are discussed in terms of a stochastic guaranteed cost, and criteria are given to test the robustness level of an AV when in the presence of AI-induced uncertainties. Furthermore, the stochastic optimal guaranteed cost control is investigated, and an efficient design procedure is developed innovatively based on LMI techniques and convex optimization. Finally, to illustrate the effectiveness, the developed results are applied to an example of car following control, along with extensive simulation.