{"data":{"id":"10.4121/5a2d4e43-dd3e-4705-90f0-e1f3fe3eb2fa","type":"dois","attributes":{"doi":"10.4121/5a2d4e43-dd3e-4705-90f0-e1f3fe3eb2fa","prefix":"10.4121","suffix":"5a2d4e43-dd3e-4705-90f0-e1f3fe3eb2fa","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Md Shadab Alam","nameType":"Personal","givenName":"Md Shadab","familyName":"Alam","nameIdentifiers":[{"nameIdentifier":"0000-0001-9184-9963","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Zi Wang","nameType":"Personal","givenName":"Zi","familyName":"Wang","nameIdentifiers":[{"nameIdentifier":"0000-0002-8162-3482","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Linghan Zhang","nameType":"Personal","givenName":"Linghan","familyName":"Zhang","nameIdentifiers":[{"nameIdentifier":"0000-0001-8295-4024","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]},{"name":"Pavlo Bazilinskyy","nameType":"Personal","givenName":"Pavlo","familyName":"Bazilinskyy","nameIdentifiers":[{"nameIdentifier":"0000-0001-9565-8240","nameIdentifierScheme":"https://orcid.org/"}],"affiliation":[]}],"titles":[{"title":"Supplementary material for the paper \"Exploring Veo 3's Capabilities for Generating Urban Traffic Scenes in 76 Cities Worldwide\""}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2026,"subjects":[{"subject":"Building","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1202"},{"subject":"Other Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0999"},{"subject":"Urban and Regional Planning","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1205"},{"subject":"Ground Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"8801"},{"subject":"Automotive Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0902"},{"subject":"Other Technology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1099"},{"subject":"Expanding Knowledge","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"97"},{"subject":"Engineering","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"09"},{"subject":"Transport","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"88"},{"subject":"Built Environment and Design","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"12"},{"subject":"Technology","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"10"},{"subject":"Veo 3"},{"subject":"Traffic scenes"},{"subject":"Cross-cultural"},{"subject":"Generative AI"},{"subject":"Dashcam"}],"contributors":[{"name":"TU Eindhoven, Department of Industrial Design, Future Mobility","nameType":"Organizational","contributorType":"Other","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-07","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1145/3828158.3838250","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://github.com/Shaadalam9/llm-traffic-scene","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.4121/5a2d4e43-dd3e-4705-90f0-e1f3fe3eb2fa.v1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[".csv; .py; .txt; .yaml; .toml; .lock"],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Supplementary material for the paper: Alam, M. S., \u0026amp; Wang, Z., \u0026amp; Zhang, L., \u0026amp; Bazilinskyy, P. (2026) \"Exploring Veo 3's Capabilities for Generating Urban Traffic Scenes in 76 Cities Worldwide\". Adjunct Proceedings of the 18th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). Gothenburg, Sweden. https://doi.org/10.1145/3828158.3838250\u003cbr\u003eThis study explores the potential of Google Veo 3, a generative video model, to synthesise 8-second dashcam-style urban traffic scenes based solely on text prompts in 76 cities across six continents. YOLOv11x was used to count road users, traffic lights, and stop signs, revealing variations across cities: Karachi had the most objects detected (79), while Muscat had only four cars. Audio analysis using dBFS showed that Montevideo was the loudest, while Copenhagen was the quietest. Through a qualitative visual analysis, the authors assessed and confirmed the perceived authenticity of most traffic scenes and highlighted AI errors, including the inability to handle non-English languages in these videos. Moreover, we compared 10 synthetic videos of New York City and Kampala, each, and verified that Veo 3 is consistent. To summarise, Veo 3 is capable of synthesising authentic, logical traffic scenes worldwide; nevertheless, it still poses non-negligible errors.\u003cbr\u003eIt has the following structure:* code/* code/run.py: Main Python script used to run YOLO detection, video analysis, summary calculations, and figure generation.* code/common.py: Contains functions for configuration management, file handling, country lookup, and data serialisation.* code/custom_logger.py: Implements a custom logger class for formatted logging at different levels.* code/default.config: Configuration file specifying paths, YOLO settings, plot settings, and logging options.* code/logmod.py: Initialises and configures the logger with display and colour options.* code/requirements.txt: Lists the Python packages required to run the project.* code/mapping.csv: CSV file containing city, country, continent, and mapping information used in the analysis.*code/utils/* code/utils/analysis.py: Contains functions for reading YOLO output files and computing object counts.* code/utils/figures.py: Contains functions for generating and saving Plotly figures.* code/utils/frames_extractor.py: Contains functions for extracting the first frame from each video.* code/utils/information.py: Contains functions for extracting video metadata, city information, country flags, and summary statistics.* code/utils/sound.py: Contains functions for extracting audio from videos and calculating sound levels.* code/utils/yolo_detection.py: Contains functions for running YOLO object detection and tracking on video files.* videos/: Contains the dashcam videos generated by Veo3.* data/: Contains the YOLO v11 output of dashcam videos generated by Veo3.\u003cbr\u003eConfiguration of project:Configuration of the project needs to be defined in \"code/config\". Please see the \"code/default.config\" file for the required structure of the file. If no custom config file is provided, \"code/default.config\" is used. The config file needs to have the following parameters:* \"videos\": location of the input video files.* \"mapping\": location of the CSV file containing city and country information.* \"data\": location of the YOLO output files.* \"snaps\": location for saving the first frame from each generated video.* \"confidence\": confidence threshold used by YOLO.* \"model\": YOLO model used for object detection, for example \"yolo11x.pt\".* \"tracking_mode\": whether YOLO object tracking is enabled.* \"always_analyse\": whether to run the analysis again even if output files already exist.* \"display_frame_tracking\": whether to display frame tracking during analysis.* \"save_annoted_img\": whether to save annotated images generated by YOLO.* \"save_tracked_img\": whether to save tracked images generated by YOLO.* \"delete_labels\": whether to delete YOLO label files after analysis.* \"delete_frames\": whether to delete extracted frame files after analysis.* \"delete_runs_files\": whether to delete YOLO run files after analysis.* \"font_family\": font family used in the generated plots.* \"font_size\": font size used in the generated plots.* \"plotly_template\": template used to make graphs in the analysis.* \"logger_level\": logging level used by the project, for example \"info\".\u003cbr\u003eNotes:* The analysis code was tested with Python 3.9.23.\u003cbr\u003e","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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      <creatorName nameType="Personal">Md Shadab Alam</creatorName>
      <givenName>Md Shadab</givenName>
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      <creatorName nameType="Personal">Zi Wang</creatorName>
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      <familyName>Bazilinskyy</familyName>
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  <titles>
    <title>Supplementary material for the paper "Exploring Veo 3's Capabilities for Generating Urban Traffic Scenes in 76 Cities Worldwide"</title>
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  <publisher>4TU.ResearchData</publisher>
  <publicationYear>2026</publicationYear>
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    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="8801">Ground Transport</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0902">Automotive Engineering</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="1099">Other Technology</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="97">Expanding Knowledge</subject>
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    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="10">Technology</subject>
    <subject>Veo 3</subject>
    <subject>Traffic scenes</subject>
    <subject>Cross-cultural</subject>
    <subject>Generative AI</subject>
    <subject>Dashcam</subject>
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  <contributors>
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      <contributorName nameType="Organizational">TU Eindhoven, Department of Industrial Design, Future Mobility</contributorName>
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    <date dateType="Issued">2026-08-07</date>
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    <description descriptionType="Abstract">&lt;p&gt;Supplementary material for the paper: Alam, M. S., &amp;amp; Wang, Z., &amp;amp; Zhang, L., &amp;amp; Bazilinskyy, P. (2026) "Exploring Veo 3&amp;#x27;s Capabilities for Generating Urban Traffic Scenes in 76 Cities Worldwide". Adjunct Proceedings of the 18th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). Gothenburg, Sweden. https://doi.org/10.1145/3828158.3838250&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;This study explores the potential of Google Veo 3, a generative video model, to synthesise 8-second dashcam-style urban traffic scenes based solely on text prompts in 76 cities across six continents. YOLOv11x was used to count road users, traffic lights, and stop signs, revealing variations across cities: Karachi had the most objects detected (79), while Muscat had only four cars. Audio analysis using dBFS showed that Montevideo was the loudest, while Copenhagen was the quietest. Through a qualitative visual analysis, the authors assessed and confirmed the perceived authenticity of most traffic scenes and highlighted AI errors, including the inability to handle non-English languages in these videos. Moreover, we compared 10 synthetic videos of New York City and Kampala, each, and verified that Veo 3 is consistent. To summarise, Veo 3 is capable of synthesising authentic, logical traffic scenes worldwide; nevertheless, it still poses non-negligible errors.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;It has the following structure:&lt;/p&gt;&lt;p&gt;* code/&lt;/p&gt;&lt;p&gt;*   			code/run.py: Main Python script used to run YOLO detection, video analysis, summary calculations, and figure generation.&lt;/p&gt;&lt;p&gt;*   			code/common.py: Contains functions for configuration management, file handling, country lookup, and data serialisation.&lt;/p&gt;&lt;p&gt;*   			code/custom_logger.py: Implements a custom logger class for formatted logging at different levels.&lt;/p&gt;&lt;p&gt;*   			code/default.config: Configuration file specifying paths, YOLO settings, plot settings, and logging options.&lt;/p&gt;&lt;p&gt;*   			code/logmod.py: Initialises and configures the logger with display and colour options.&lt;/p&gt;&lt;p&gt;*   			code/requirements.txt: Lists the Python packages required to run the project.&lt;/p&gt;&lt;p&gt;*   			code/mapping.csv: CSV file containing city, country, continent, and mapping information used in the analysis.&lt;/p&gt;&lt;p&gt;*code/utils/&lt;/p&gt;&lt;p&gt;*     					code/utils/analysis.py: Contains functions for reading YOLO output files and computing object counts.&lt;/p&gt;&lt;p&gt;*     					code/utils/figures.py: Contains functions for generating and saving Plotly figures.&lt;/p&gt;&lt;p&gt;*     					code/utils/frames_extractor.py: Contains functions for extracting the first frame from each video.&lt;/p&gt;&lt;p&gt;*     					code/utils/information.py: Contains functions for extracting video metadata, city information, country flags, and summary statistics.&lt;/p&gt;&lt;p&gt;*     					code/utils/sound.py: Contains functions for extracting audio from videos and calculating sound levels.&lt;/p&gt;&lt;p&gt;*     					code/utils/yolo_detection.py: Contains functions for running YOLO object detection and tracking on video files.&lt;/p&gt;&lt;p&gt;* videos/: Contains the dashcam videos generated by Veo3.&lt;/p&gt;&lt;p&gt;* data/: Contains the YOLO v11 output of dashcam videos generated by Veo3.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Configuration of project:&lt;/p&gt;&lt;p&gt;Configuration of the project needs to be defined in "code/config". Please see the "code/default.config" file for the required structure of the file. If no custom config file is provided, "code/default.config" is used. The config file needs to have the following parameters:&lt;/p&gt;&lt;p&gt;* "videos": location of the input video files.&lt;/p&gt;&lt;p&gt;* "mapping": location of the CSV file containing city and country information.&lt;/p&gt;&lt;p&gt;* "data": location of the YOLO output files.&lt;/p&gt;&lt;p&gt;* "snaps": location for saving the first frame from each generated video.&lt;/p&gt;&lt;p&gt;* "confidence": confidence threshold used by YOLO.&lt;/p&gt;&lt;p&gt;* "model": YOLO model used for object detection, for example "yolo11x.pt".&lt;/p&gt;&lt;p&gt;* "tracking_mode": whether YOLO object tracking is enabled.&lt;/p&gt;&lt;p&gt;* "always_analyse": whether to run the analysis again even if output files already exist.&lt;/p&gt;&lt;p&gt;* "display_frame_tracking": whether to display frame tracking during analysis.&lt;/p&gt;&lt;p&gt;* "save_annoted_img": whether to save annotated images generated by YOLO.&lt;/p&gt;&lt;p&gt;* "save_tracked_img": whether to save tracked images generated by YOLO.&lt;/p&gt;&lt;p&gt;* "delete_labels": whether to delete YOLO label files after analysis.&lt;/p&gt;&lt;p&gt;* "delete_frames": whether to delete extracted frame files after analysis.&lt;/p&gt;&lt;p&gt;* "delete_runs_files": whether to delete YOLO run files after analysis.&lt;/p&gt;&lt;p&gt;* "font_family": font family used in the generated plots.&lt;/p&gt;&lt;p&gt;* "font_size": font size used in the generated plots.&lt;/p&gt;&lt;p&gt;* "plotly_template": template used to make graphs in the analysis.&lt;/p&gt;&lt;p&gt;* "logger_level": logging level used by the project, for example "info".&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Notes:&lt;/p&gt;&lt;p&gt;* The analysis code was tested with Python 3.9.23.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;</description>
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