{"id":"W3115515976","doi":"10.1109/tcst.2020.3042815","title":"Provably Safe and Scalable Multivehicle Trajectory Planning","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Reachability; Scalability; Computer science; Trajectory; Computation; Leverage (statistics); Toolbox; Mathematical optimization; Trajectory optimization; Motion planning; Theoretical computer science; Distributed computing; Algorithm; Artificial intelligence; Mathematics; Robot; Optimal control","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":[0.0005704112,0.0009335157,0.000682165,0.0003940187,0.0005977475,0.0008380225,0.001014736,0.0007591557,0.003651295],"category_scores_gemma":[0.002579228,0.0004508561,0.0008051398,0.0003496472,0.0009891947,0.001017432,0.002352549,0.001415209,0.0005510455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008372168,"about_ca_system_score_gemma":0.001666758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006355209,"about_ca_topic_score_gemma":0.008696544,"domain_scores_codex":[0.9994863,0.00008369612,0.00002705554,0.0001262629,0.0001849205,0.00009171433],"domain_scores_gemma":[0.9990197,0.0005894427,0.00008283569,0.0001261574,0.0001188181,0.00006290602],"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":[0.00006668732,0.00001820602,0.0003513976,0.0000775501,0.00001584362,0.0001166946,0.00005071707,0.9489216,0.002691596,0.02330391,0.001763923,0.02262179],"study_design_scores_gemma":[0.000006343799,0.000007602619,0.00003502709,0.000003971586,0.000002693488,0.00001237736,0.00000884328,0.9869633,0.0006654334,0.01182224,0.0004693143,0.000002826439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02194823,0.000166264,0.9689802,0.0002713006,0.00004864176,0.00005551705,0.0002419392,0.00190453,0.006383335],"genre_scores_gemma":[0.7658664,0.0002466253,0.2269486,0.0001517636,0.00004632685,0.0001703929,0.0008373383,0.0003889963,0.005343545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006355209,"threshold_uncertainty_score":0.01263642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0184781317648522,"score_gpt":0.2310467134436858,"score_spread":0.2125685816788336,"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."}}