{"id":"W3046576094","doi":"10.1007/978-3-030-54407-2_15","title":"Homography-Based Vehicle Pose Estimation from a Single Image by Using Machine-Learning for Wheel-Region and Tire-Road Contact Point Detection","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Homography; Vanishing point; Robustness (evolution); Metric (unit); Flagging; Machine vision; Truck; Image (mathematics); Automotive engineering; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001348317,0.0003030334,0.0003053603,0.0002764907,0.0002204051,0.0001120571,0.0002340249,0.0003264596,0.000003151564],"category_scores_gemma":[0.00005152447,0.0003216286,0.00007543281,0.0001713558,0.0002258,0.0002190356,0.00007610586,0.0005875501,0.000001771285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001854054,"about_ca_system_score_gemma":0.00004559253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007363207,"about_ca_topic_score_gemma":0.00008722987,"domain_scores_codex":[0.9987875,0.00001468139,0.0002580205,0.0005357899,0.0001482848,0.0002557835],"domain_scores_gemma":[0.999354,0.0002087571,0.0001134101,0.0002114453,0.00004579901,0.00006661192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003303909,0.00001140053,0.0001024703,0.00007508272,0.00001922996,0.00001459823,0.0001788628,0.08496104,0.06090669,0.00003222253,0.000002227163,0.8536631],"study_design_scores_gemma":[0.0003698511,0.0001861552,0.00009196503,0.0001684592,0.00002297469,0.00001028572,2.938058e-7,0.9680215,0.02693025,0.003805471,0.00007758718,0.000315251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02197734,0.0005937899,0.976191,0.0001647452,0.0002555048,0.0003295353,0.00002007016,0.0004262744,0.00004175583],"genre_scores_gemma":[0.9292406,0.00001135878,0.0704615,0.0001337956,0.00007110464,0.000006329454,0.00003030629,0.00004270164,0.000002327539],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9072632,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009516551168784543,"score_gpt":0.2013495389322788,"score_spread":0.1918329877634943,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}