{"id":"W4383108838","doi":"10.1109/icra48891.2023.10160577","title":"Real-Time Unified Trajectory Planning and Optimal Control for Urban Autonomous Driving Under Static and Dynamic Obstacle Constraints","year":2023,"lang":"en","type":"article","venue":"","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Flexibility (engineering); Computer science; Model predictive control; Obstacle; Controller (irrigation); Scheme (mathematics); Variety (cybernetics); Control (management); Control theory (sociology); Obstacle avoidance; Control engineering; Motion planning; Optimal control; Code (set theory); Mathematical optimization; Engineering; Robot; Mobile robot; 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":[0.00104089,0.0007218654,0.0007200289,0.0003421884,0.0004812617,0.0009335571,0.0007962871,0.0006848091,0.001620004],"category_scores_gemma":[0.00195682,0.0004541377,0.0004074177,0.0005247784,0.0009513072,0.0008010872,0.001240173,0.0007833145,0.0002105216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103453,"about_ca_system_score_gemma":0.001740194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01619277,"about_ca_topic_score_gemma":0.01095793,"domain_scores_codex":[0.9995271,0.0001184439,0.0000215986,0.00009496745,0.0001396951,0.00009829163],"domain_scores_gemma":[0.999511,0.000198529,0.00009118528,0.00004726671,0.0001022459,0.00004985147],"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":[0.00005736129,0.00001419074,0.0001124087,0.00002997729,0.000008209383,0.00003107063,0.00003684676,0.981011,0.000809354,0.008361693,0.0003325721,0.009195324],"study_design_scores_gemma":[0.000004137271,0.000009637865,0.00002854715,0.000001332908,0.000001417858,0.000002254361,0.000003957644,0.9985644,0.000132011,0.001098833,0.0001518777,0.000001543146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02326686,0.000232795,0.972493,0.0001171697,0.00003079118,0.00004511923,0.00004913018,0.0004627107,0.003302385],"genre_scores_gemma":[0.9308953,0.000135166,0.0665163,0.00003866894,0.00002102093,0.0001146653,0.0001072204,0.00006407859,0.002107592],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01619277,"threshold_uncertainty_score":0.032197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007036327013365925,"score_gpt":0.2160131967205432,"score_spread":0.2089768697071773,"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."}}