{"id":"W4386634516","doi":"10.1109/jiot.2023.3314373","title":"Group Frenet Frame CAV Path Planning on Highways","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Motion planning; Frame (networking); Frenet–Serret formulas; Computer science; Path (computing); Reference frame; Motion (physics); Term (time); Real-time computing; Simulation; Robot; Artificial intelligence; Computer network; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002474531,0.0006287529,0.0004413417,0.0005429992,0.0005163774,0.0005288873,0.0007690426,0.0006255142,0.00306144],"category_scores_gemma":[0.0004359056,0.0002594955,0.0004469046,0.0004569329,0.0005335548,0.0006328524,0.0007361942,0.0004098713,0.0003815411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000812784,"about_ca_system_score_gemma":0.0009507074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02379562,"about_ca_topic_score_gemma":0.02921535,"domain_scores_codex":[0.9998349,0.00003346138,0.000005376625,0.00005077441,0.00003886558,0.00003663085],"domain_scores_gemma":[0.9998891,0.00003096093,0.00001412179,0.00001302045,0.00003965395,0.00001307034],"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":[0.00005091295,0.0000103775,0.0004797633,0.00003356013,0.000009072238,0.00009426103,0.00007533889,0.9601785,0.0012976,0.009620018,0.0009933658,0.02715722],"study_design_scores_gemma":[0.000005433653,0.00003042072,0.0001804445,0.000004974876,0.000004736525,0.00001793009,0.000047948,0.9927833,0.0005328473,0.004134985,0.002251986,0.000005128677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07574948,0.0002588487,0.9110698,0.0001361146,0.00004543805,0.00009668864,0.0002762413,0.0006287292,0.01173865],"genre_scores_gemma":[0.8023875,0.0001622468,0.18947,0.00003672126,0.00001393192,0.0001158764,0.0005154734,0.00008895063,0.007209177],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02379562,"threshold_uncertainty_score":0.04731423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393550439496434,"score_gpt":0.2269332175977071,"score_spread":0.2129977132027427,"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."}}