{"id":"W3156199997","doi":"10.1109/itsc48978.2021.9565113","title":"Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","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; Obstacle avoidance; Control theory (sociology); Simulation; Algorithm; Control (management); Robot; Mathematics; Mobile robot","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001721831,0.0002919094,0.0002763192,0.00009147891,0.00014769,0.00006089478,0.0002686644,0.0005215202,0.000306464],"category_scores_gemma":[0.00001450424,0.0002407821,0.0001464372,0.00009321817,0.00005912289,0.00009215595,0.0002477585,0.001161982,0.00003038859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000185069,"about_ca_system_score_gemma":0.00003721997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008696743,"about_ca_topic_score_gemma":0.00006580432,"domain_scores_codex":[0.9988035,0.00005196197,0.0003706571,0.0003540898,0.0001427739,0.0002770336],"domain_scores_gemma":[0.9991932,0.00005051121,0.00006124801,0.0006052246,0.0000490608,0.00004079219],"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":[0.000002794311,0.00003555342,0.0009927042,0.00008454132,0.000154733,0.000003608359,0.0005407086,0.9764166,0.001158231,0.00002188322,0.00007472193,0.02051389],"study_design_scores_gemma":[0.0001954223,0.000006645775,0.03314377,0.00004872422,0.00006733759,0.000003306595,0.0001852109,0.9637533,0.001823662,0.00009115128,0.000463573,0.000217925],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2496974,0.0009723901,0.743647,0.00009507826,0.001205425,0.0003827728,0.00001630964,0.002188477,0.001795138],"genre_scores_gemma":[0.9967651,0.0001105676,0.002190088,0.00003289941,0.0001129226,0.0000821029,0.0001479085,0.00005255901,0.0005057955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7470677,"threshold_uncertainty_score":0.9818808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747730950923697,"score_gpt":0.2338954679509317,"score_spread":0.2164181584416947,"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."}}