{"id":"W2967833234","doi":"10.1016/j.trc.2019.08.003","title":"Risk-based autonomous vehicle motion control with considering human driver’s behaviour","year":2019,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Innovate UK","keywords":"Trajectory; Motion (physics); Path (computing); Control (management); Computer science; Active safety; Vehicle dynamics; Motion planning; Model predictive control; Simulation; Engineering; Control theory (sociology); Automotive engineering; 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":[0.001213653,0.001208433,0.00137117,0.0005305601,0.0004155774,0.000921722,0.001202982,0.001061355,0.0008262718],"category_scores_gemma":[0.002539809,0.0005529415,0.0007758467,0.0002855103,0.0006107486,0.0008981015,0.001550292,0.0007586437,0.0001564286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000648776,"about_ca_system_score_gemma":0.001066041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006874213,"about_ca_topic_score_gemma":0.004427667,"domain_scores_codex":[0.9992114,0.0001851111,0.00004080484,0.0002270002,0.0002070815,0.0001285896],"domain_scores_gemma":[0.998741,0.0005129864,0.0002232122,0.0000576768,0.0003733369,0.00009179411],"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":[0.00018167,0.0000696356,0.001330731,0.00006205714,0.0000855778,0.00009068967,0.0001017345,0.9740596,0.002381208,0.002669265,0.0002799418,0.01868789],"study_design_scores_gemma":[0.000006060099,0.00006155934,0.0003872856,0.000002936932,0.00001294846,0.00001200235,0.000007143919,0.998542,0.0002222408,0.000662901,0.00007809023,0.000004876573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09386621,0.000384279,0.903155,0.000200125,0.00009497088,0.00005186085,0.00004068967,0.0002260403,0.001980918],"genre_scores_gemma":[0.9920011,0.00005209928,0.00684092,0.00002444058,0.00002208896,0.00002996419,0.00002723716,0.00001455183,0.0009875488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006874213,"threshold_uncertainty_score":0.01366836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02035050304572326,"score_gpt":0.2770651136658813,"score_spread":0.256714610620158,"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."}}