A Systematic Methodology for Specifying the Operational Design Domain of Automated Vehicles

This paper outlines a systematic approach to specifying an Operational Design Domain (ODD) for an Automated Vehicle (AV). The approach addresses the complexities arising from diverse sensor setups, the vast range of operational domain attributes, and the wide range of driving assistant functionalities with their specific regulations that impact the ODD. Our methodology focuses on identifying and excluding non-safe areas within the operational domain. By narrowing down the operational domain to a manageable subspace, this approach saves time and effort in the testing process of AVs. As a proof of concept, the proposed methodology has been validated through a practical example, demonstrating its application and feasibility.

Authors

Institutions

Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/issrew63542.2024.00040
Citations
2
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.41

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Systematic Methodology for Specifying the Operational Design Domain of Automated Vehicles

Ali Shakeri, Frank Eichenseer, Shinjini Sarkar
2 citations
Autonomous Vehicle Technology and Safety
0.41
article

A Systematic Methodology for Specifying the Operational Design Domain of Automated Vehicles

Ali Shakeri, Frank Eichenseer, Shinjini Sarkar
article en
2 citations

Abstract

This paper outlines a systematic approach to specifying an Operational Design Domain (ODD) for an Automated Vehicle (AV). The approach addresses the complexities arising from diverse sensor setups, the vast range of operational domain attributes, and the wide range of driving assistant functionalities with their specific regulations that impact the ODD. Our methodology focuses on identifying and excluding non-safe areas within the operational domain. By narrowing down the operational domain to a manageable subspace, this approach saves time and effort in the testing process of AVs. As a proof of concept, the proposed methodology has been validated through a practical example, demonstrating its application and feasibility.

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Ministry of Economic Affairs
Openalex Percentile: Top 38%
Autonomous Vehicle Technology and Safety
0.41
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.