{"id":"W2798873012","doi":"10.1109/cvpr.2018.00615","title":"LiDAR-Video Driving Dataset: Learning Driving Policies Effectively","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lidar; Computer science; Dashboard; Point cloud; Scale (ratio); Computer vision; Artificial intelligence; Remote sensing; Data science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007385883,0.002473986,0.0009327618,0.001677348,0.0006194486,0.0007344584,0.002742487,0.00204799,0.002303963],"category_scores_gemma":[0.00245787,0.0004551803,0.001097708,0.001463063,0.0004083258,0.001005655,0.0009563982,0.001584006,0.003018538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207944,"about_ca_system_score_gemma":0.001427216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03691672,"about_ca_topic_score_gemma":0.08994583,"domain_scores_codex":[0.9993048,0.00007872032,0.00005111109,0.0002615304,0.0002028837,0.0001009693],"domain_scores_gemma":[0.9992551,0.0001362861,0.00007326758,0.0002148537,0.0002342882,0.0000861405],"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":[0.000936924,0.002068566,0.02904313,0.001412007,0.0006744894,0.0006795416,0.0001328279,0.07384544,0.009275286,0.001353024,0.7002677,0.1803111],"study_design_scores_gemma":[0.0009147472,0.001108038,0.06848507,0.0003684516,0.0002801042,0.001265495,0.0006753078,0.6509227,0.03210681,0.006443088,0.2370654,0.0003648882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2042643,0.002767748,0.02361179,0.00187748,0.00114481,0.0006839748,0.7352329,0.02339894,0.007017995],"genre_scores_gemma":[0.1311118,0.0003436635,0.02266159,0.0002603824,0.00009757973,0.0002755327,0.8430082,0.0002389021,0.002002294],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03691672,"threshold_uncertainty_score":0.07340372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01586056699650176,"score_gpt":0.291744235751821,"score_spread":0.2758836687553192,"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."}}