{"id":"W4403938286","doi":"10.1109/tvt.2024.3485511","title":"A Novel Motion Planning for Autonomous Vehicles Using Point Cloud Based Potential Field","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Point cloud; Motion planning; Cloud computing; Field (mathematics); Computer science; Motion (physics); Point (geometry); Potential field; Aerospace engineering; Engineering; Artificial intelligence; Physics; Mathematics; Robot; Geometry","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":[0.0001608972,0.0002920219,0.0002824746,0.0008336302,0.0002633278,0.00003982765,0.0002449993,0.0008946442,0.00003303152],"category_scores_gemma":[0.000007609343,0.0003188731,0.0002225236,0.0005325977,0.0001149611,0.0001299365,0.000002722847,0.0008319697,0.00002730493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002167103,"about_ca_system_score_gemma":0.00005954703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001019842,"about_ca_topic_score_gemma":0.000005553471,"domain_scores_codex":[0.9986462,0.00001272504,0.0003487862,0.0004258587,0.0001086833,0.0004577713],"domain_scores_gemma":[0.9994007,0.00009434926,0.00003024135,0.000383196,0.00004164727,0.00004982171],"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":[0.00003706709,0.0000844391,0.000003225677,0.0001248737,0.0001698496,0.00007223279,0.00004584397,0.7796934,0.1633676,0.001159499,0.00006929631,0.05517267],"study_design_scores_gemma":[0.0003981918,0.0001457083,0.00000454947,0.00009657389,0.00009529418,0.0001540649,0.00005293109,0.701162,0.2956464,0.0009093201,0.001087451,0.0002474786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1615307,0.0003838943,0.8312161,0.001050745,0.001189741,0.0003637189,0.00004807578,0.004181928,0.00003511834],"genre_scores_gemma":[0.9837417,0.00001213489,0.01579807,0.0001036776,0.00007419553,0.000150568,0.000006793784,0.00008353066,0.00002930841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8222111,"threshold_uncertainty_score":0.9999263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302664332852313,"score_gpt":0.2384433009878232,"score_spread":0.2254166576593001,"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."}}