{"id":"W2991567876","doi":"","title":"Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions","year":2019,"lang":"en","type":"article","venue":"The European Symposium on Artificial Neural Networks","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Representation (politics); Artificial intelligence; Spatial analysis; Power (physics); Machine learning; Computer vision; Engineering; Remote sensing; Systems engineering; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002804137,0.0001654368,0.0001459495,0.00004775084,0.0003044061,0.00006266138,0.0001585545,0.00007583934,0.00001331016],"category_scores_gemma":[0.00001016415,0.000141799,0.00006704303,0.0001204101,0.00007098416,0.0001046964,0.00006183785,0.0003240209,0.00002304051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003103447,"about_ca_system_score_gemma":0.000003893616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001884782,"about_ca_topic_score_gemma":0.00001277745,"domain_scores_codex":[0.9989519,0.0001407351,0.000271376,0.0002489462,0.00008806946,0.0002989543],"domain_scores_gemma":[0.9994363,0.0001311247,0.00006455961,0.0002921165,0.00002610063,0.00004979337],"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":[0.00018728,0.00005100324,0.0100515,0.00001176368,0.0000496648,0.0000124533,0.0005855364,0.899083,0.07946144,0.001116665,0.0001136858,0.009276022],"study_design_scores_gemma":[0.0002387179,0.0001591804,0.02296678,0.0000210275,0.00003981401,0.00001599102,0.00007651225,0.9741269,0.002076231,0.00006939891,0.00003919788,0.0001702977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887702,0.0000406175,0.007116242,0.0004454878,0.0007605822,0.0005569144,0.00001417343,0.0004747619,0.001821047],"genre_scores_gemma":[0.9993128,0.000006409312,0.00006111821,0.000116983,0.0003741094,0.000009421519,0.00001433088,0.00006524068,0.00003960553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07738521,"threshold_uncertainty_score":0.5782396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368452148302297,"score_gpt":0.2289709085938224,"score_spread":0.2152863871107994,"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."}}