{"id":"W4286741712","doi":"10.48550/arxiv.1905.08758","title":"aUToTrack: A Lightweight Object Detection and Tracking System for the\\n SAE AutoDrive Challenge","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Christian Studies; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Benchmark (surveying); Inertial measurement unit; Global Positioning System; Computer science; Artificial intelligence; Computer vision; Position (finance); Object detection; Tracking (education); Ground truth; Lidar; Video tracking; Pedestrian; Object (grammar); Tracking system; Segmentation; Engineering; Geography; Kalman filter; Cartography; Remote sensing; Transport engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001661948,0.004387864,0.002497194,0.0031128,0.001786792,0.002331316,0.005248131,0.002746605,0.00992645],"category_scores_gemma":[0.00341427,0.0009663601,0.00160379,0.002469488,0.0005742933,0.003136452,0.004358382,0.002338679,0.01850899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643778,"about_ca_system_score_gemma":0.002546978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05036728,"about_ca_topic_score_gemma":0.1048448,"domain_scores_codex":[0.9971706,0.0002274779,0.0001585142,0.001276197,0.000899648,0.000267646],"domain_scores_gemma":[0.9984524,0.0001561514,0.0000866769,0.0006115573,0.000480698,0.0002124783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008496194,0.0006990065,0.005007355,0.0006110633,0.0003375803,0.0002968633,0.0001699252,0.004195007,0.01083947,0.001071463,0.7477205,0.2282023],"study_design_scores_gemma":[0.0009059125,0.001396505,0.03303615,0.0004354304,0.000304628,0.001644693,0.0006946514,0.3457405,0.05167848,0.008401076,0.5553184,0.0004436578],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1056921,0.007012974,0.1659088,0.001542953,0.003556634,0.002429973,0.3529051,0.330837,0.03011451],"genre_scores_gemma":[0.06378067,0.0005827775,0.1544506,0.0006769382,0.0001662648,0.0008356752,0.7633232,0.00248566,0.01369821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05036728,"threshold_uncertainty_score":0.1001483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03542319572148158,"score_gpt":0.1686719976614247,"score_spread":0.1332488019399431,"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."}}