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                <front>
                    <journal-meta>
                    <journal-id journal-id-type="publisher-id">ei</journal-id>
                    <journal-title>Electronic Imaging</journal-title>
                    <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub">2470-1173</issn>
                    <publisher>
                        <publisher-name>Society for Imaging Science and Technology</publisher-name>
                        <publisher-loc>IS&amp;T 7003 Kilworth Lane, Springfield, VA 22151 USA</publisher-loc>
                    </publisher>
                    </journal-meta>
                    <article-meta>
                    <article-id pub-id-type="doi">10.2352/EI.2023.35.16.AVM-118</article-id>
                    <article-id pub-id-type="publisher-id">AVM-118</article-id>
                    <article-categories>
                        <subj-group>
                        <subject>Article</subject>
                        </subj-group>
                    </article-categories>
                    <title-group>
                        <article-title>Using simulation to quantify the performance of automotive perception systems</article-title>
                    </title-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Liu</surname>
                            <given-names>Zhenyi </given-names>
                           </name> <xref ref-type="aff" rid="aff1author1"/></contrib><aff id="aff1author1">Stanford University, United States</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Shah</surname>
                            <given-names>Devesh </given-names>
                           </name> <xref ref-type="aff" rid="aff2author2"/></contrib><aff id="aff2author2">Ford Motor Company, United States</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Rahimpour</surname>
                            <given-names>Alireza </given-names>
                           </name> <xref ref-type="aff" rid="aff3author3"/></contrib><aff id="aff3author3">Ford Greenfield Labs, United States</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Upadhyay</surname>
                            <given-names>Devesh </given-names>
                           </name> <xref ref-type="aff" rid="aff2author4"/></contrib><aff id="aff2author4">Ford Motor Company, United States</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Farrell</surname>
                            <given-names>Joyce </given-names>
                           </name> <xref ref-type="aff" rid="aff1author5"/></contrib><aff id="aff1author5">Stanford University, United States</aff></contrib-group><contrib-group content-type="all"><contrib contrib-type="author"><name>
                            <surname>Wandell</surname>
                            <given-names>Brian </given-names>
                           </name> <xref ref-type="aff" rid="aff1author6"/></contrib><aff id="aff1author6">Stanford University, United States</aff></contrib-group><abstract>
                    <title>Abstract</title>
                    <p>The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance or to test its performance under conditions that are difficult to measure (e.g., nighttime for automotive perception systems). We describe the image system simulation software tools that we use to evaluate the performance of image systems for object (automobile) detection. We describe experiments with 13 different cameras with a variety of optics and pixel sizes. To measure the impact of camera spatial resolution, we designed a collection of driving scenes that had cars at many different distances. We quantified system performance by measuring average precision and we report a trend relating system resolution and object detection performance. We also quantified the large performance degradation under nighttime conditions, compared to daytime, for all cameras and a COCO pre-trained network.</p>
                    </abstract><pub-date>
                        <day>16</day>
                        <month>1</month>
                        <year>2023</year>
                        </pub-date><volume>35</volume>
                    <issue-acronym>AVM</issue-acronym>
                    <issue-title>Autonomous Vehicles and Machines 2023</issue-title>
                    <issue seq="118">16</issue>
                    <fpage>118-1</fpage>
                    <lpage>118-8</lpage>
                    <permissions>
                         <copyright-statement>© 2023, Society for Imaging Science and Technology</copyright-statement>
                        <copyright-year>2023</copyright-year>
                    </permissions><kwd-group><kwd>Autonomous driving</kwd><kwd>Image systems simulation</kwd><kwd>Automotive perception system</kwd><kwd>Neural network</kwd></kwd-group></article-meta>
                </front>
                </article>