{"id":2,"date":"2025-04-08T17:07:39","date_gmt":"2025-04-08T15:07:39","guid":{"rendered":"https:\/\/projets.litislab.fr\/adav\/?page_id=2"},"modified":"2025-06-26T17:01:43","modified_gmt":"2025-06-26T15:01:43","slug":"page-d-exemple","status":"publish","type":"page","link":"https:\/\/projets.litislab.fr\/adav\/","title":{"rendered":"Index"},"content":{"rendered":"\n
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AdaV: Autonomous Driving in Adverse Visibility<\/p>\n\n\n\n

Multimodal Sensor Fusion for Reliable Perception in All Weather Conditions<\/p>\n\n\n\n

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The AdaV project, financed by the ANR JCJC, addresses a core limitation of current autonomous driving systems: their reduced performance in adverse weather conditions such as fog, rain, and snow. These conditions impair the visibility and reliability of conventional sensors. AdaV proposes a multimodal perception system combining RGB cameras, LiDAR, RADAR, IMU, and polarimetric cameras. The goal is to improve robustness and reliability of object detection by adapting the sensor fusion process to environmental conditions.<\/p>\n\n\n\n

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Objectives<\/p>\n\n\n\n

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  • Develop adaptive fusion methods for object detection in low-visibility scenes.<\/li>\n\n\n\n
  • Quantify sensor reliability based on environmental conditions.<\/li>\n\n\n\n
  • Propose uncertainty-aware deep learning models based on Dempster-Shafer Theory.<\/li>\n\n\n\n
  • Construct and validate a new multimodal dataset with diverse conditions.<\/li>\n<\/ul>\n<\/blockquote>\n\n\n\n
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    Impact<\/p>\n\n\n\n

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    • Enhanced perception for autonomous vehicles in real-world, low-visibility settings.<\/li>\n\n\n\n
    • Contributions to AI-based uncertainty modeling and multimodal deep learning.<\/li>\n\n\n\n
    • Public dataset and open research tools for the ADAS community.<\/li>\n<\/ul>\n<\/blockquote>\n\n\n\n
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      The AdaV platform is equipped with a multimodal sensor suite designed to capture diverse environmental information in adverse weather conditions. This setup supports robust perception, calibration, and sensor fusion tasks.<\/p>\n\n\n\n

      \ud83d\udcf7 Cameras<\/h3>\n\n\n\n
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      • 2 RGB cameras<\/strong> (ATP050S-CC)<\/em> with 8 mm F1.4 Azure lenses<\/li>\n\n\n\n
      • 2 monochrome cameras<\/strong> (TRT009S-EC)<\/em> with 8 mm F1.4 lenses for polarization imaging<\/li>\n\n\n\n
      • 1 polarimetric color camera<\/strong> (TRI050S1-QC)<\/em> with an 8 mm F2.8 lens<\/li>\n\n\n\n
      • 1 thermal camera (LWIR)<\/strong> (ADK)<\/em> for low-visibility scenes<\/li>\n<\/ul>\n\n\n\n

        \ud83c\udf00 LiDARs<\/h3>\n\n\n\n
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        • 1 Ouster OS2-128<\/strong> \u2013 high-resolution, 128-channel LiDAR<\/li>\n\n\n\n
        • 3 Robosense M1<\/strong> \u2013 solid-state LiDARs for 360\u00b0 coverage<\/li>\n<\/ul>\n\n\n\n

          \ud83d\udccd Localization<\/h3>\n\n\n\n
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          • ATLANS A7<\/strong> \u2013 GNSS\/INS system for precise vehicle pose<\/li>\n\n\n\n
          • GNSS Antenna<\/strong> (PolaNTt-x MF)<\/em> \u2013 for reliable satellite reception<\/li>\n<\/ul>\n<\/div>\n\n\n\n
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            \"\"<\/figure>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

            The AdaV project, financed by the ANR JCJC, addresses a core limitation of current autonomous driving systems: their reduced performance in adverse weather conditions such as fog, rain, and snow. These conditions impair the visibility and reliability of conventional sensors. AdaV proposes a multimodal perception system combining RGB cameras, LiDAR, RADAR, IMU, and polarimetric cameras. […]<\/p>\n","protected":false},"author":1,"featured_media":13,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"blog-alternative","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/comments?post=2"}],"version-history":[{"count":24,"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":141,"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/pages\/2\/revisions\/141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/media\/13"}],"wp:attachment":[{"href":"https:\/\/projets.litislab.fr\/adav\/wp-json\/wp\/v2\/media?parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}