{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008420747,0.0003490283,0.0004877993,0.0003649081,0.0003279264,0.00008174877,0.0003494073,0.0008606221,0.0000136834],"category_scores_gemma":[0.0001051358,0.0003900369,0.0001331719,0.000174359,0.0001871521,0.0001379517,0.0003069046,0.002282008,0.00000931178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461147,"about_ca_system_score_gemma":0.0002398242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002274805,"about_ca_topic_score_gemma":0.00002867569,"domain_scores_codex":[0.9976681,0.0001191801,0.0003798348,0.0006797208,0.000357666,0.0007955365],"domain_scores_gemma":[0.9988022,0.0003178346,0.00005661561,0.0004191295,0.0001882996,0.0002158734],"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":[0.0001254644,0.00004204631,0.001331637,0.001734643,0.0003113612,0.00002089359,0.001062941,0.9756441,0.003632816,0.0009660395,0.0005661983,0.01456186],"study_design_scores_gemma":[0.0007597688,0.000277009,0.004905608,0.000205414,0.00004486035,0.000004966778,0.000262208,0.9837309,0.0005630739,0.008828141,0.0001375771,0.000280507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1523159,0.002220285,0.8355242,0.000569458,0.0002549927,0.003379696,0.001338983,0.00285554,0.001540945],"genre_scores_gemma":[0.9973779,0.000274467,0.0008292233,0.00001377207,0.0002863188,0.0008873616,0.0001534508,0.0001124826,0.0000650072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.845062,"threshold_uncertainty_score":0.9998552,"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."}}