{"id":"W2922703966","doi":"10.2316/j.2019.206-0072","title":"AN INTERACTION-AWARE PREDICTIVE MOTION PLANNER FOR UNMANNED GROUND VEHICLES IN DYNAMIC STREET SCENARIOS","year":2019,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Planner; Computer science; Motion (physics); Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001456149,0.0006231525,0.0005279417,0.0003175524,0.0004064656,0.0004043096,0.0008171114,0.000470683,0.00194059],"category_scores_gemma":[0.0003456886,0.0003059924,0.0003644118,0.0003030774,0.0002867742,0.0004297665,0.0007793627,0.0005931663,0.0003291364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003155825,"about_ca_system_score_gemma":0.0008382653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007486822,"about_ca_topic_score_gemma":0.01068988,"domain_scores_codex":[0.9998581,0.0000182515,0.000004495481,0.00003258128,0.00006214117,0.0000243617],"domain_scores_gemma":[0.9998841,0.0000412878,0.00001634037,0.00001156165,0.00003193207,0.00001469324],"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":[0.00009667511,0.00004426962,0.0004212146,0.0000693909,0.00002597017,0.0002196838,0.00008985436,0.8767602,0.01033046,0.004758609,0.002501873,0.1046816],"study_design_scores_gemma":[0.000003217756,0.00001321361,0.0000585219,0.000001972283,0.000002298471,0.00001316324,0.000008136821,0.9980713,0.0006698758,0.000621754,0.0005340435,0.000002653754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01029877,0.00008535397,0.9863659,0.00004744465,0.0000225333,0.00003486565,0.00005739873,0.0009042528,0.00218351],"genre_scores_gemma":[0.6163765,0.0001425547,0.3790961,0.0000524256,0.00002368207,0.0001556804,0.000263228,0.0001921253,0.003697594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007486822,"threshold_uncertainty_score":0.0148865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007594738576417841,"score_gpt":0.2514990632616926,"score_spread":0.2439043246852747,"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."}}