# 6-Layer Model for a Structured Description and Categorization of Urban Traffic and Environment

arXiv: https://arxiv.org/abs/2012.06319 · HTML: https://mlsanity.com/paper/2012.06319/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 26 of 26)

- [SIND: A Drone Dataset at Signalized Intersection in China](https://mlsanity.com/paper/2209.02297/) (2022)
- [Engineering Safety Requirements for Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2403.16289/) (2024)
- [Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving](https://mlsanity.com/paper/2205.01532/) (2022)
- [A Review of Testing Object-Based Environment Perception for Safe Automated Driving](https://mlsanity.com/paper/2102.08460/) (2021)
- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05701/) (2023)
- [A Framework for a Capability-driven Evaluation of Scenario Understanding for Multimodal Large Language Models in Autonomous Driving](https://mlsanity.com/paper/2503.11400/) (2025)
- [ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving](https://mlsanity.com/paper/2209.10078/) (2022)
- [Grasping Causality for the Explanation of Criticality for Automated Driving](https://mlsanity.com/paper/2210.15375/) (2022)
- [Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios](https://mlsanity.com/paper/2404.04040/) (2024)
- [Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data](https://mlsanity.com/paper/2202.06608/) (2022)
- [The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations](https://mlsanity.com/paper/2401.14831/) (2024)
- [Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection](https://mlsanity.com/paper/2507.08743/) (2025)
- [Why Braking? Scenario Extraction and Reasoning Utilizing LLM](https://mlsanity.com/paper/2507.15874/) (2025)
- [Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving](https://mlsanity.com/paper/2607.14387/) (2026)
- [Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing](https://mlsanity.com/paper/2302.01161/) (2023)
- [Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving](https://mlsanity.com/paper/2308.07106/) (2023)
- [Causality-based Transfer of Driving Scenarios to Unseen Intersections](https://mlsanity.com/paper/2404.02046/) (2024)
- [An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving](https://mlsanity.com/paper/2409.06607/) (2024)
- [Context-Awareness and Interpretability of Rare Occurrences for Discovery and Formalization of Critical Failure Modes](https://mlsanity.com/paper/2504.16117/) (2025)
- [Towards provable probabilistic safety for scalable embodied AI systems](https://mlsanity.com/paper/2506.05171/) (2025)
- [Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning](https://mlsanity.com/paper/2510.18034/) (2025)
- [Driving scenario generation and evaluation using a structured layer representation and foundational models](https://mlsanity.com/paper/2511.01541/) (2025)
- [AI Sandboxes: A Threat Model, Taxonomy, and Measurement Framework](https://mlsanity.com/paper/2606.18532/) (2026)
- [From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving](https://mlsanity.com/paper/2608.08941/) (2026)
- [Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systems](https://mlsanity.com/paper/2609.24397/) (2026)

## Abstract (arXiv)

Verification and validation of automated driving functions impose large challenges. Currently, scenario-based approaches are investigated in research and industry, aiming at a reduction of testing efforts by specifying safety relevant scenarios. To define those scenarios and operate in a complex real-world design domain, a structured description of the environment is needed. Within the PEGASUS research project, the 6-Layer Model (6LM) was introduced for the description of highway scenarios. This paper refines the 6LM and extends it to urban traffic and environment. As defined in PEGASUS, the 6LM provides the possibility to categorize the environment and, therefore, functions as a structured basis for subsequent scenario description. The model enables a structured description and categorization of the general environment, without incorporating any knowledge or anticipating any functions of actors. Beyond that, there is a variety of other applications of the 6LM, which are elaborated in this paper. The 6LM includes a description of the road network and traffic guidance objects, roadside structures, temporary modifications of the former, dynamic objects, environmental conditions and digital information. The work at hand specifies each layer by categorizing its items. Guidelines are formulated and explanatory examples are given to standardize the application of the model for an objective environment description. In contrast to previous publications, the model and its design are described in far more detail. Finally, the holistic description of the 6LM presented includes remarks on possible future work when expanding the concept to machine perception aspects.