{"id":"W2756315406","doi":"10.3141/2621-03","title":"Vehicle-to-Pedestrian Communication Modeling and Collision Avoiding Method in Connected Vehicle Environment","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Dedicated short-range communications; Pedestrian; Bluetooth; Computer science; Wireless; Collision; Vehicular communication systems; Control (management); Computer network; Collision avoidance; Real-time computing; Embedded system; Simulation; Engineering; Vehicular ad hoc network; Transport engineering; Telecommunications; Computer security; Wireless ad hoc network; Artificial intelligence","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":[0.0003955889,0.0007153119,0.0005961097,0.0005238071,0.0005277155,0.0006130324,0.001445471,0.0008823566,0.001251408],"category_scores_gemma":[0.0006136415,0.0003730971,0.0009355511,0.0005320446,0.0004767606,0.0008347741,0.0006347098,0.0006907185,0.0002058488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006791992,"about_ca_system_score_gemma":0.001128951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01632896,"about_ca_topic_score_gemma":0.006123324,"domain_scores_codex":[0.9997175,0.00007914165,0.00001166921,0.00006834796,0.00007759892,0.00004567869],"domain_scores_gemma":[0.9997752,0.00007621913,0.00004166184,0.00001079237,0.00007447929,0.00002161073],"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":[0.000009557612,0.000008066681,0.0002653914,0.00001654044,0.000008756467,0.00004792416,0.00002882908,0.9904157,0.0004688652,0.006156084,0.0001203295,0.002454009],"study_design_scores_gemma":[0.000001014914,0.000007887543,0.00003589742,0.000001017804,0.00000261199,0.00000881324,0.000004612444,0.9990813,0.00005750408,0.0006890248,0.0001077503,0.000002605437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02228228,0.0001752234,0.9744489,0.00009275546,0.00004214029,0.00002698118,0.00005153243,0.0001109527,0.002769163],"genre_scores_gemma":[0.9492921,0.0005586917,0.04439884,0.00004802842,0.0000660794,0.0001501469,0.0001332803,0.00003890198,0.005313884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01632896,"threshold_uncertainty_score":0.03246784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07919991948185309,"score_gpt":0.3639788423042474,"score_spread":0.2847789228223943,"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."}}