{"id":"W4366503402","doi":"10.1049/cth2.12441","title":"HOPAV: Hybrid optimization‐oriented path planning for non‐connected and connected automated vehicles","year":2023,"lang":"en","type":"article","venue":"IET Control Theory and Applications","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"Silesian University of Technology","keywords":"Path (computing); Motion planning; Scheme (mathematics); Function (biology); Computer science; Transport engineering; Penetration rate; Engineering; Automotive engineering; Simulation; Operations research; Real-time computing; Artificial intelligence; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003181608,0.0001587118,0.0002147431,0.0001167725,0.000340489,0.00002577318,0.00009542186,0.0001181427,0.000008262391],"category_scores_gemma":[0.00006869948,0.0001626623,0.00003132965,0.0002832992,0.0001208919,0.00008073457,0.00002286574,0.0001254117,0.00000981026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001338856,"about_ca_system_score_gemma":0.00001305207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.127097e-7,"about_ca_topic_score_gemma":4.799707e-7,"domain_scores_codex":[0.9992068,0.00003831527,0.0002236155,0.0002389822,0.00004169428,0.0002506123],"domain_scores_gemma":[0.9989834,0.0006467816,0.00004736482,0.0001967443,0.00005748344,0.00006825072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003730979,0.00008796297,0.000823432,0.0002065028,0.0005479964,0.00001189422,0.0007533325,0.4363362,0.02450874,0.510956,0.002819695,0.02257516],"study_design_scores_gemma":[0.001639955,0.00003667044,0.002037053,0.00002055166,0.00006258433,0.00001150037,0.0002233438,0.9805536,0.0009632544,0.009845066,0.004388246,0.0002181253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2723817,0.000404077,0.7186191,0.0003057453,0.00005776946,0.001295577,0.0003495326,0.00618087,0.0004056412],"genre_scores_gemma":[0.9975863,0.0000698011,0.000926591,0.00009323546,0.00004720786,0.0009973572,0.000193121,0.00003444981,0.00005197927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7252045,"threshold_uncertainty_score":0.6633178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004477260036320305,"score_gpt":0.2199790955786961,"score_spread":0.2155018355423758,"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."}}