{"id":"W2122886340","doi":"10.1109/ivs.2008.4621158","title":"Realtime experiments in Markov-based lane position estimation using wireless ad-hoc network","year":2008,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Computer science; Real-time computing; Particle filter; Automatic vehicle location; Wireless ad hoc network; Positioning system; Wireless; Assisted GPS; Markov process; Filter (signal processing); Software; Hidden Markov model; Noise (video); Telecommunications; Engineering; Artificial intelligence; Computer vision","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":[0.000994657,0.0004762014,0.0004164784,0.0003925537,0.0002965658,0.0003870265,0.0007327114,0.0004062504,0.00118183],"category_scores_gemma":[0.003923019,0.0002361725,0.0001666477,0.0004141759,0.0003738452,0.0008013193,0.0005025184,0.0003704219,0.0001994741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003439367,"about_ca_system_score_gemma":0.0003006611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002726358,"about_ca_topic_score_gemma":0.001496516,"domain_scores_codex":[0.9992156,0.0002633448,0.00005026153,0.0001254054,0.0002393313,0.000106002],"domain_scores_gemma":[0.9965786,0.002108977,0.0003207388,0.000277723,0.0005321128,0.0001817806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00344445,0.001674609,0.01148609,0.0004774362,0.0001277849,0.0005835785,0.0008596462,0.6953726,0.1696498,0.00323648,0.001822541,0.111265],"study_design_scores_gemma":[0.00007254976,0.001057623,0.003113336,0.000008783618,0.00001789791,0.0000906158,0.00009531459,0.9581002,0.03619804,0.0006720654,0.0005498343,0.00002368367],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.89872,0.0001388621,0.09775431,0.00010251,0.00008311768,0.00009274446,0.0001555612,0.001151834,0.001800918],"genre_scores_gemma":[0.9858619,0.00003906709,0.01345889,0.00001232905,0.000006436508,0.00004637487,0.00009815884,0.00002340743,0.000453427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002726358,"threshold_uncertainty_score":0.005421042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427252085679473,"score_gpt":0.2318317662578143,"score_spread":0.2175592454010196,"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."}}