{"id":"W3154724615","doi":"10.1016/j.trc.2021.103453","title":"A context-aware pedestrian trajectory prediction framework for automated vehicles","year":2021,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pedestrian; Interpretability; Computer science; Trajectory; Context (archaeology); Machine learning; Block (permutation group theory); Artificial intelligence; Pedestrian detection; Resource (disambiguation); Focus (optics); Data mining; Engineering; Transport engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002843181,0.0008324221,0.0009932072,0.0008432608,0.0006866411,0.0007179915,0.001573982,0.0006700069,0.001758225],"category_scores_gemma":[0.0007479018,0.0004131918,0.0007527414,0.0008933721,0.0002013521,0.0009682268,0.001205555,0.0009463383,0.0006552862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006266219,"about_ca_system_score_gemma":0.001902939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05606426,"about_ca_topic_score_gemma":0.07335309,"domain_scores_codex":[0.9997811,0.00001899143,0.00001003711,0.00008708991,0.00006232041,0.00004053472],"domain_scores_gemma":[0.999836,0.00003286228,0.00001540629,0.00002573125,0.0000635989,0.00002643708],"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":[0.0004956,0.0004259793,0.007085419,0.0001752536,0.0002156389,0.0004616785,0.0001833163,0.5584034,0.01265547,0.009833647,0.01288431,0.3971802],"study_design_scores_gemma":[0.00000576279,0.00001805857,0.0003254151,0.000005536157,0.00001484755,0.00003169335,0.00001656064,0.996242,0.000769228,0.001607068,0.000955908,0.000007846062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03730601,0.0009343147,0.9539296,0.0001621584,0.000122921,0.0001112157,0.001098038,0.00492285,0.001412965],"genre_scores_gemma":[0.6584801,0.0007448237,0.3347598,0.0001143334,0.00008695004,0.0001591809,0.002402289,0.0001638953,0.003088526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05606426,"threshold_uncertainty_score":0.1114758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05551739709267645,"score_gpt":0.3363987626836402,"score_spread":0.2808813655909637,"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."}}