{"id":"W3110717661","doi":"10.21203/rs.3.rs-122422/v1","title":"Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Automotive Safety and Energy","keywords":"Trajectory; CarSim; Model predictive control; Kinematics; Computer science; Vehicle dynamics; Collision avoidance; Control theory (sociology); Field (mathematics); Simulation; Control (management); Collision; Engineering; Automotive engineering; Artificial intelligence; Mathematics","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":[0.0003294396,0.000569821,0.0003867945,0.0003673767,0.0004887469,0.0004247988,0.0007593437,0.000442623,0.0007338029],"category_scores_gemma":[0.0005209886,0.0002092876,0.000265603,0.0002436551,0.0004241263,0.0004550602,0.0006631571,0.0004456363,0.000174863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00034352,"about_ca_system_score_gemma":0.0008483014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00835807,"about_ca_topic_score_gemma":0.004133012,"domain_scores_codex":[0.9997826,0.00003641785,0.00000850179,0.0000501001,0.00008431715,0.00003808886],"domain_scores_gemma":[0.9997242,0.00004917162,0.00007218301,0.0000265545,0.00009716807,0.00003066891],"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":[0.0001415977,0.00009586636,0.001424129,0.00005975541,0.00003238779,0.0002720207,0.0001981601,0.9017475,0.02045033,0.0055481,0.0008803155,0.06914979],"study_design_scores_gemma":[0.000006027334,0.00005063498,0.0001615565,0.000002168504,0.000003433014,0.00001422515,0.00001176013,0.9978911,0.0009370032,0.0006314493,0.0002866758,0.000003986747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1083525,0.000177976,0.8855048,0.0002401704,0.00006572283,0.00006072146,0.00002718109,0.0007085514,0.004862303],"genre_scores_gemma":[0.9863362,0.0000366978,0.01245009,0.00002302657,0.0000113902,0.00003118481,0.00002331333,0.000008735698,0.001079441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00835807,"threshold_uncertainty_score":0.01661885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03658462155171072,"score_gpt":0.3043904993533197,"score_spread":0.267805877801609,"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."}}