{"id":"W4226033290","doi":"10.1109/tvt.2021.3132558","title":"A Multi-Vehicle Longitudinal Trajectory Collision Avoidance Strategy Using AEBS With Vehicle-Infrastructure Communication","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Traffic control and management","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Engineering and Physical Sciences Research Council; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Collision avoidance; Controller (irrigation); Vehicle dynamics; Trajectory; Automotive engineering; Collision; Computer science; Key (lock); Active safety; Engineering; Real-time computing; Computer security","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":[0.0001445824,0.0005366906,0.0003789618,0.0003561803,0.0004369394,0.0003537811,0.0008668094,0.0003371627,0.001175101],"category_scores_gemma":[0.000262708,0.0001458243,0.0002627689,0.0002399967,0.000193647,0.0004685945,0.0007869645,0.0003808073,0.0003453309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000208724,"about_ca_system_score_gemma":0.0005353607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003431958,"about_ca_topic_score_gemma":0.003052127,"domain_scores_codex":[0.9998026,0.00002431764,0.00001089165,0.00005505018,0.00007211272,0.00003504513],"domain_scores_gemma":[0.9998204,0.00001973266,0.00004244768,0.00001850514,0.00007384934,0.00002501957],"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":[0.000486158,0.0002640397,0.004493674,0.0002702062,0.000113229,0.0006093649,0.00036611,0.5219445,0.1418389,0.01153251,0.002691354,0.31539],"study_design_scores_gemma":[0.00003374427,0.0004898243,0.00141999,0.00001327715,0.00002873923,0.0002129707,0.00007154242,0.9820635,0.01094264,0.001350196,0.003354225,0.00001941048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1014214,0.0003461171,0.8905095,0.0001503935,0.00008102428,0.00008195201,0.0000386234,0.0006550296,0.006715907],"genre_scores_gemma":[0.972852,0.0000917134,0.02405795,0.00003466885,0.00001559697,0.00005197992,0.0000550675,0.000008016221,0.002833155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003431958,"threshold_uncertainty_score":0.006823957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184249467465714,"score_gpt":0.2233385433656603,"score_spread":0.2114960486910032,"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."}}