{"id":"W3121941838","doi":"10.48550/arxiv.2101.06679","title":"End-to-end Interpretable Neural Motion Planner","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Planner; Computer science; SAFER; Position (finance); Set (abstract data type); Motion (physics); Artificial intelligence; Trajectory; Data set; Volume (thermodynamics); Raw data; Motion planning; Lidar; Artificial neural network; Machine learning; Robot; Geography","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":[0.0004163288,0.001029548,0.0005271937,0.0003438529,0.0002800884,0.0005954845,0.001327274,0.001060006,0.003930577],"category_scores_gemma":[0.001758702,0.0004551588,0.0005319995,0.000344746,0.0007281858,0.00104589,0.001151842,0.001484829,0.0005874391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010147,"about_ca_system_score_gemma":0.00114487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004927787,"about_ca_topic_score_gemma":0.009104882,"domain_scores_codex":[0.999785,0.00004195399,0.000009645102,0.00008274835,0.00005492943,0.00002565036],"domain_scores_gemma":[0.9996238,0.0001927542,0.00003978545,0.00005856354,0.00005791411,0.0000271374],"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":[0.00006581374,0.00003946647,0.000377002,0.00004528216,0.00001495338,0.00008123263,0.00003379746,0.9274556,0.001763773,0.006311613,0.001806063,0.0620054],"study_design_scores_gemma":[0.000004526157,0.00001302726,0.00003415879,0.000002681263,0.000001973543,0.00001044314,0.000006076604,0.9934812,0.0005373278,0.005537967,0.0003686974,0.00000190684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01385529,0.00008919704,0.9815074,0.000214226,0.00002760201,0.00006185815,0.0002463258,0.001835666,0.002162437],"genre_scores_gemma":[0.6312437,0.0001440833,0.3617952,0.0002612323,0.00003481497,0.0002629126,0.00106026,0.0003022197,0.004895582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004927787,"threshold_uncertainty_score":0.01314908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812640960583203,"score_gpt":0.1584026066046025,"score_spread":0.1302761969987704,"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."}}