{"id":"W3015947429","doi":"10.1109/tits.2020.2984210","title":"Integrated Steering and Differential Braking for Emergency Collision Avoidance in Autonomous Vehicles","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CarSim; Collision avoidance; Control theory (sociology); Controller (irrigation); Obstacle avoidance; Vehicle dynamics; Differential (mechanical device); Electronic stability control; Engineering; Model predictive control; Collision; Yaw; Control engineering; Computer science; Automotive engineering; Control (management); Mobile robot; Robot; Aerospace engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001012015,0.0002446513,0.0003445531,0.0001497061,0.00009162436,0.00005806719,0.0001074937,0.0001283078,0.00001701149],"category_scores_gemma":[0.000001902802,0.0002569887,0.0001093676,0.0002757366,0.00001441885,0.0001486868,1.784394e-7,0.0002159811,0.000006403659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000090084,"about_ca_system_score_gemma":0.00001577321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001653937,"about_ca_topic_score_gemma":0.000443027,"domain_scores_codex":[0.998484,0.00003184739,0.0007543662,0.0003077568,0.0001735769,0.0002484679],"domain_scores_gemma":[0.9995769,0.00005662506,0.00006751806,0.0001096971,0.00006679544,0.0001225221],"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":[0.0001115637,0.00004140777,0.0003252875,0.0005069618,0.00007915629,0.00000415454,0.002214912,0.9666415,0.02007007,0.000275925,0.00001517176,0.009713923],"study_design_scores_gemma":[0.0006444196,0.0001097298,0.0008475079,0.0001924849,0.00003539694,0.000001729191,0.0008750851,0.9902836,0.005973563,0.000007913416,0.00074839,0.0002801531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4187034,0.0001963388,0.5793125,0.00002752719,0.0008653782,0.0005603818,0.0001515595,0.0001631439,0.00001979342],"genre_scores_gemma":[0.9991416,0.0002275853,0.00008864779,0.00001104801,0.00006459723,0.0003294273,0.00002895727,0.00005633736,0.0000518121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5804382,"threshold_uncertainty_score":0.9999883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01825593622823687,"score_gpt":0.2191881324434176,"score_spread":0.2009321962151807,"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."}}