# Towards a Functional System Architecture for Automated Vehicles

arXiv: https://arxiv.org/abs/1703.08557 · HTML: https://mlsanity.com/paper/1703.08557/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2017.

## Built on by (most cited first, 6 of 6)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [High-Definition Map Generation Technologies For Autonomous Driving](https://mlsanity.com/paper/2206.05400/) (2022)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [Inferring Driving Maps by Deep Learning-based Trail Map Extraction](https://mlsanity.com/paper/2505.10258/) (2025)
- [PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Prediction](https://mlsanity.com/paper/2507.19701/) (2025)
- [Security and Resilience in Autonomous Vehicles: A Proactive Design Approach](https://mlsanity.com/paper/2604.12408/) (2026)

## Abstract (arXiv)

This paper presents a functional system architecture for an automated vehicle. It provides an overall, generic structure that is independent of a specific implementation of a particular vehicle project. Yet, it has been inspired and cross-checked with a real world automated driving implementation in the Stadtpilot project at the Technische Universit"at Braunschweig. The architecture entails aspects like environment and self perception, planning and control, localization, map provision, Vehicle-To-X-communication, and interaction with human operators.