mlsanityData to 7 Oct 2026

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

arXiv 2501.10839 ↗First posted 18 Jan 2025eess.SY cs.SYcited by 0

On the map: End-to-end autonomous driving → Large language models for driving

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 28 references that are in the map. First posted on arXiv 18 Jan 2025.

Builds on

13 of 28 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving3 Oct

Other references · 12

  1. 2021MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Predictioncited by 246
  2. 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
  3. 2020A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systemscited by 71
  4. 2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
  5. 2023Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMscited by 42
  6. 2018Probabilistic Prediction of Vehicle Semantic Intention and Motioncited by 31
  7. 2022Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curriculacited by 17
  8. 2020Waymo's Safety Methodologies and Safety Readiness Determinationscited by 16
  9. 2024Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Taskscited by 12
  10. 2018Autonomous Systems -- An Architectural Characterizationcited by 5
  11. 2024Normative Requirements Operationalization with Large Language Modelscited by 5
  12. 2024Compositionally Safe Construction of Autonomous Driving Systemscited by 1

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Close in wording, no citation either way

  1. 2024Testing Large Language Models on Driving Theory Knowledge and Skills for Connected Autonomous Vehicles
  2. 2023Evaluation of Large Language Models for Decision Making in Autonomous Driving
  3. 2024Leveraging Large Language Models for Enhancing Autonomous Vehicle Perception
  4. 2025When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective
  5. 2023Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles
  6. 2025Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving
  7. 2024Specifications: The missing link to making the development of LLM systems an engineering discipline
  8. 2024Engineering Safety Requirements for Autonomous Driving with Large Language Models