{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078566,0.0001056809,0.0001295408,0.00007683992,0.00007877082,0.000003998175,0.00006641587,0.0001533893,0.00006765807],"category_scores_gemma":[0.00000219441,0.0001153964,0.00002115153,0.0001743548,0.00003158673,0.00009587173,0.00001175733,0.0001349742,0.00002813933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001543585,"about_ca_system_score_gemma":0.00001860872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002784139,"about_ca_topic_score_gemma":0.00001900413,"domain_scores_codex":[0.9993938,0.00001869836,0.0001880745,0.0001178986,0.0000649714,0.0002165645],"domain_scores_gemma":[0.9997775,0.0000198116,0.00002236177,0.0001441043,0.000009017618,0.00002722139],"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":[0.00006527056,0.00006840438,0.005412264,0.00002988351,0.00002482586,0.00007908002,0.0001991537,0.9606401,0.008674424,0.0004476933,0.0004306009,0.0239283],"study_design_scores_gemma":[0.000381477,0.00002209718,0.009484535,0.00003138772,0.000003925801,0.0000187776,0.000008684576,0.9822599,0.007485387,0.0001191548,0.00004870722,0.0001360211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.92421,0.0002458792,0.07309118,0.00005283353,0.00008217252,0.0001285159,0.000001290368,0.0006486872,0.001539466],"genre_scores_gemma":[0.9698996,0.00002944468,0.02989999,0.00005817348,0.00001750159,0.00001115812,0.00002626837,0.00002002627,0.00003785573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0456896,"threshold_uncertainty_score":0.4705728,"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."}}