Incorporating the Scenario-Based Framework Underpinned by Operational Design Domain (ODD) for Automated Driving Stack (ADS) Evaluation

<div class="section abstract"><div class="htmlview paragraph">The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing pass/fail criteria, the framework provides objective data to highlight performance differences between both systems. The proposed methodology provides an unbiased foundation for informed decision-making, promoting the safe and effective integration of ADS technologies.</div></div>

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Publication Details

Journal
SAE technical papers on CD-ROM/SAE technical paper series
Published
2025-03-31
DOI
https://doi.org/10.4271/2025-01-8067
Citations
1
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.68
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article

Incorporating the Scenario-Based Framework Underpinned by Operational Design Domain (ODD) for Automated Driving Stack (ADS) Evaluation

Xizhe Zhang, J. R. Mitchell, Siddartha Khastgir, Emil Chodowiec et al.
1 citations
SAE technical papers on CD-ROM/SAE technical paper series
Autonomous Vehicle Technology and Safety
0.68
article

Incorporating the Scenario-Based Framework Underpinned by Operational Design Domain (ODD) for Automated Driving Stack (ADS) Evaluation

Xizhe Zhang, J. R. Mitchell, Siddartha Khastgir, Emil Chodowiec, Patricia A. Jennings, Peter B. Baker
article en
1 citations

Abstract

<div class="section abstract"><div class="htmlview paragraph">The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing pass/fail criteria, the framework provides objective data to highlight performance differences between both systems. The proposed methodology provides an unbiased foundation for informed decision-making, promoting the safe and effective integration of ADS technologies.</div></div>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
University of Warwick (GB)
Openalex Percentile: Top 32%
Autonomous Vehicle Technology and Safety
0.68
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Incorporating the Scenario-Based Framework Underpinned by Operational Design Domain (ODD) for Automated Driving Stack (ADS) Evaluation — Xizhe Zhang, J. R. Mitchell, et al. · SAE technical papers on CD-ROM/SAE technical paper series (2025) | TGRS Research Map | TGRS