{"id":"W4386076117","doi":"10.1109/cvpr52729.2023.00139","title":"Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Occupancy; Occupancy grid mapping; Trajectory; Convolutional neural network; Artificial intelligence; Curse of dimensionality; Field (mathematics); Perception; Pedestrian; Planner; Object (grammar); Grid; Machine learning; Computer vision; Robot; Mobile robot","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":[0.0004170775,0.0005453543,0.0005287109,0.0004686675,0.0002773244,0.0006685376,0.001412451,0.0006402663,0.001366272],"category_scores_gemma":[0.001380392,0.0004983176,0.0004373642,0.0003939977,0.0005432765,0.001622266,0.0007826214,0.001088809,0.0002263843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009518535,"about_ca_system_score_gemma":0.0008500945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01679775,"about_ca_topic_score_gemma":0.01353662,"domain_scores_codex":[0.9998769,0.00001973798,0.000005067072,0.00003773932,0.0000310315,0.00002959197],"domain_scores_gemma":[0.9996319,0.0001730821,0.00004253007,0.00004142123,0.00007716257,0.00003385016],"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":[0.0001523252,0.0001019532,0.001823875,0.00004719791,0.00003272148,0.00005321554,0.00009689334,0.8681241,0.004538219,0.008602883,0.00164299,0.1147836],"study_design_scores_gemma":[0.000002508169,0.00000827918,0.0001992428,0.000002884551,0.000002551857,0.00000523297,0.000003883628,0.9963973,0.0004102928,0.002790514,0.0001738762,0.00000337382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08109913,0.0006773934,0.9142171,0.0003902468,0.00009194035,0.00003305559,0.0002400816,0.001426765,0.001824373],"genre_scores_gemma":[0.9372667,0.0003343294,0.05968175,0.0001124777,0.00005778281,0.00004331527,0.0002720819,0.00007505695,0.002156612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01679775,"threshold_uncertainty_score":0.03339994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006293288333038312,"score_gpt":0.2144622641133119,"score_spread":0.2081689757802736,"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."}}