{"id":"W4287203366","doi":"10.48550/arxiv.2104.11212","title":"Imagining The Road Ahead: Multi-Agent Trajectory Prediction via\\n Differentiable Simulation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Air Force Research Laboratory; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research; Western Canada Research Grid; Compute Canada; Defense Advanced Research Projects Agency; Mitacs","keywords":"Trajectory; Computer science; Kinematics; Differentiable function; State (computer science); Acceleration; Artificial neural network; Artificial intelligence; Control theory (sociology); Simulation; Algorithm; Control (management); Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0003383525,0.0005647342,0.0004634495,0.0002371853,0.0002802631,0.0006679355,0.001242108,0.001097548,0.002933506],"category_scores_gemma":[0.001430563,0.000473445,0.0006346775,0.000211146,0.0008544498,0.000907018,0.001013449,0.001340854,0.0005423473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009747652,"about_ca_system_score_gemma":0.0008217589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01618483,"about_ca_topic_score_gemma":0.01729752,"domain_scores_codex":[0.9998645,0.0000387439,0.00000433132,0.00004393858,0.00002771166,0.0000208617],"domain_scores_gemma":[0.9995788,0.0002425415,0.00003940493,0.00005565774,0.00003707807,0.0000465454],"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":[0.0000264322,0.000009807755,0.0004695489,0.000008552486,0.00001042761,0.00002952518,0.00002359306,0.9887326,0.0004437613,0.004344699,0.0004830213,0.00541797],"study_design_scores_gemma":[0.000001420131,0.00000231023,0.00001903921,9.862697e-7,6.434356e-7,0.000002410157,0.000001336501,0.9985821,0.00009461582,0.001191721,0.0001024654,8.778549e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09849519,0.0003123914,0.8921067,0.0008518546,0.00007989885,0.00004034845,0.0004678808,0.002015162,0.005630493],"genre_scores_gemma":[0.9400049,0.0001260151,0.05431582,0.0001585947,0.0000266128,0.00005663083,0.0005510449,0.0001739719,0.004586422],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01618483,"threshold_uncertainty_score":0.03218126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04353308186930707,"score_gpt":0.1767002500976307,"score_spread":0.1331671682283236,"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."}}