Approaches to Analysis and Design of AI-Based Autonomous Vehicles

2025-09-15

AI Summary

AI perception uncertainty undermines the stability and robustness of autonomous driving closed-loop systems. Method: This paper proposes an integrated modeling–analysis–synthesis framework. Perception errors are innovatively modeled as three statistically distinct structures—Markov chains, Gaussian processes, and bounded disturbances—enabling a probabilistic stochastic stability theory. Leveraging linear matrix inequalities (LMIs) and convex optimization, a robust controller with guaranteed optimal cost is synthesized. Contributions/Results: The approach yields sufficient conditions for closed-loop stochastic stability and quantifies robustness bounds. Extensive car-following simulations demonstrate significant improvements in both control performance and reliability under perception uncertainty, validating the method’s practical efficacy.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Modeling AI perception uncertainties in autonomous vehicles
• Analyzing closed-loop stability and robustness statistically
• Developing control synthesis methods using LMI techniques

Innovation

• Modeling AI perception uncertainties with Markov chains
• Establishing closed-loop stability via linear matrix inequalities
• Developing stochastic optimal control through convex optimization