Systems Engineering for Autonomous Vehicles; Supervising AI using Large Language Models (SSuperLLM)

TL;DR

This paper explores integrating Large Language Models into autonomous vehicle systems engineering for requirements development, verification, and supervisory control, demonstrated through a simulation-based proof-of-concept.

Contribution

It introduces a novel approach of using LLMs in AV systems engineering, specifically for requirements handling and supervisory control, with an open-source simulation tool.

Findings

LLMs can assist in AV requirements development and verification.

A proof-of-concept supervisory control system using LLMs was successfully demonstrated.

Open-source simulation environment enables further research and development.

Abstract

Generative Artificial Intelligence (GAI) and the idea to use hierarchical models has been around for some years now. GAI has proved to be an extremely useful tool for Autonomous Vehicles (AVs). AVs need to perform robustly in their environment. Thus the AV behavior and short-term trajectory planning needs to be: a) designed and architected using safeguarding and supervisory systems and b) verified using proper Systems Engineering (SysEng) Principles. Can AV Systems Engineering also use Large Language Models (LLM) to help Autonomous vehicles (AV) development? This reader-friendly paper advocates the use of LLMs in 1) requirements (Reqs) development and 2) Reqs verification and 3) provides a proof-of-concept of AV supervisory control. The latter uses a simulation environment of a simple planar (bicycle) vehicle dynamics model and a Linear Quadratic Regulator (LQR) control with an LLM Application Interface (API). The Open-Source simulation SW is available from the author accessible to the readers so that they can engage into the AV stack, LLM API and rules, SysEng and Reqs and fundamental vehicle dynamics and control.