{"id":"W2795933029","doi":"10.1109/vppc.2017.8330908","title":"Energy Efficient Path Planning for Low Speed Autonomous Electric Vehicle","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Energy consumption; Motion planning; Automotive engineering; Path (computing); Context (archaeology); Energy (signal processing); Computer science; Electric vehicle; Work (physics); Minification; Efficient energy use; Aerodynamics; Engineering; Aerospace engineering; Robot; Electrical engineering; Power (physics); Mathematics; Mechanical engineering; Artificial intelligence","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":[0.0002051007,0.0006591584,0.0004174202,0.0005102078,0.0004269577,0.000400389,0.0005067781,0.000407334,0.001880611],"category_scores_gemma":[0.0005641293,0.000308999,0.0003207618,0.0004415954,0.0003859907,0.0004514141,0.0005363728,0.0003906229,0.0002403934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005687925,"about_ca_system_score_gemma":0.001089151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006276011,"about_ca_topic_score_gemma":0.006065627,"domain_scores_codex":[0.999837,0.00003581644,0.000006532707,0.00003933952,0.00005557229,0.00002563756],"domain_scores_gemma":[0.9998249,0.0000837066,0.0000305712,0.00001483221,0.00003482117,0.00001128298],"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":[0.00003274959,0.00001234325,0.0001955061,0.00004123005,0.000009660351,0.00005352726,0.00004089602,0.9657336,0.002846816,0.004106417,0.000441184,0.02648593],"study_design_scores_gemma":[0.000006518293,0.00002552579,0.00009950357,0.000003145668,0.000003733137,0.0000186352,0.00001577198,0.9941965,0.0009582202,0.003733116,0.0009345852,0.000004642647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0364265,0.0002192434,0.9586107,0.00008872819,0.00001803259,0.00006020369,0.00009071372,0.0005749244,0.003910959],"genre_scores_gemma":[0.7658269,0.0002535191,0.2285976,0.00003337052,0.000009390253,0.0002081244,0.0002410065,0.00009705981,0.004733156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006276011,"threshold_uncertainty_score":0.01247895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02299981335710384,"score_gpt":0.2682311666096362,"score_spread":0.2452313532525324,"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."}}