{"id":"W4414549062","doi":"10.1007/978-3-031-98349-8_9","title":"Safety of Pedestrians in AI-Optimized VANETs for Autonomous Vehicles via Real-Time Vehicle-to-Vehicle Communication","year":2025,"lang":"en","type":"book-chapter","venue":"Information systems engineering and management","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brampton Civic Hospital","funders":"","keywords":"Pedestrian; Intelligent transportation system; Platoon; Control (management); Situation awareness; Transformative learning; Vehicular communication systems","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":[0.0003835787,0.0008392436,0.0008012723,0.0005712362,0.0008340441,0.001378068,0.001333868,0.0007631356,0.004993814],"category_scores_gemma":[0.0008179211,0.0003691377,0.0005793367,0.0007540211,0.000646564,0.001165855,0.001525723,0.0007864067,0.001101663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007613142,"about_ca_system_score_gemma":0.001280204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007012772,"about_ca_topic_score_gemma":0.007389561,"domain_scores_codex":[0.9996613,0.00007554442,0.000009836012,0.00007949717,0.00009305513,0.00008064003],"domain_scores_gemma":[0.9997422,0.00009348644,0.00002654599,0.00002206809,0.00008418094,0.00003146404],"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":[0.0003221451,0.00007237971,0.001332637,0.000162086,0.00005344707,0.0002771063,0.0002044011,0.8067459,0.003699998,0.04590651,0.01122376,0.1299995],"study_design_scores_gemma":[0.000008203003,0.000136144,0.0004926493,0.00003761905,0.00002471903,0.0001303627,0.0002551585,0.9530669,0.001588523,0.03481997,0.009421923,0.00001786406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06932441,0.003540152,0.8511043,0.001034375,0.0007066929,0.000104055,0.0005013616,0.000867939,0.0728168],"genre_scores_gemma":[0.8822575,0.002478237,0.0624202,0.0001674687,0.0002247659,0.00008054877,0.0005537844,0.0001653381,0.05165217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007012772,"threshold_uncertainty_score":0.01670599,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004982442379079383,"score_gpt":0.1891658093687562,"score_spread":0.1841833669896769,"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."}}