{"id":"W3108004145","doi":"10.1109/icra48506.2021.9561910","title":"Multimodal dynamics modeling for off-road autonomous vehicles","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Computer science; Leverage (statistics); Robot; Artificial intelligence; Modalities; Modality (human–computer interaction); Lidar; Robotics; Dynamics (music); Computer vision; Simulation; Human–computer interaction; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000179419,0.0004416685,0.0005717351,0.000142869,0.0001139884,0.00007091783,0.0004829387,0.001285604,0.00005225329],"category_scores_gemma":[0.00002903913,0.0004976535,0.0002958441,0.00007362291,0.00005101642,0.00008097083,0.0004649161,0.001056173,0.00001590238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000410047,"about_ca_system_score_gemma":0.0001552142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000968228,"about_ca_topic_score_gemma":0.0002693813,"domain_scores_codex":[0.9983236,0.00001475676,0.0005163903,0.0005490358,0.0001054152,0.0004908472],"domain_scores_gemma":[0.9990089,0.00004798966,0.00005412543,0.0007061446,0.0001043874,0.00007845476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000697493,0.00002676893,0.00004649439,0.0002361336,0.000172257,0.000008262256,0.0001129286,0.7954951,0.00008786832,0.001487441,0.00005587561,0.2022639],"study_design_scores_gemma":[0.0002992602,0.00001287044,0.0001072411,0.00006406099,0.00005888591,0.000007749308,0.0002016374,0.9965007,0.0005152077,0.001485163,0.000196078,0.0005511252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4526469,0.001519337,0.5375237,0.0002200323,0.0008491321,0.0005571332,0.0001168495,0.003196942,0.003369921],"genre_scores_gemma":[0.9591142,0.0003737571,0.03919632,0.00004925036,0.0001077891,0.0002187827,0.0004266602,0.0001197579,0.0003934713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5064673,"threshold_uncertainty_score":0.9997475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366570232005829,"score_gpt":0.2306386963409902,"score_spread":0.2169729940209319,"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."}}