{"id":"W4315629604","doi":"10.1109/globecom48099.2022.10001152","title":"LidNet: Boosting Perception and Motion Prediction from a Sequence of LIDAR Point Clouds for Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pooling; Computer science; Encoder; Convolution (computer science); Boosting (machine learning); Artificial intelligence; Computer vision; Lidar; Point cloud; Residual; Algorithm; Artificial neural network; Remote sensing; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005326539,0.0002520369,0.0003098302,0.000095352,0.001416887,0.0001356314,0.002640691,0.00007384308,0.00007600171],"category_scores_gemma":[0.0001177451,0.0003184339,0.0001079969,0.0009807396,0.0002187909,0.0006629868,0.002237073,0.0004414193,0.000006156185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006262517,"about_ca_system_score_gemma":0.0002349931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004862745,"about_ca_topic_score_gemma":0.000218913,"domain_scores_codex":[0.9974374,0.0004088346,0.0006753993,0.0007188207,0.00039617,0.0003633472],"domain_scores_gemma":[0.9963634,0.0004092385,0.0005306788,0.002300793,0.0002592167,0.0001366601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001064846,0.001026983,0.01955812,0.00009271572,0.0002006749,0.000005088315,0.004898719,0.01774864,0.09351023,0.3554239,0.003969193,0.5034593],"study_design_scores_gemma":[0.0006670171,0.0002967227,0.0179681,0.00005229581,0.00006055924,0.00006145453,0.001009544,0.9144212,0.0002345571,0.0559439,0.00883492,0.0004497169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1062914,0.0005810512,0.8845624,0.00350896,0.0005080121,0.001249424,0.002017015,0.000409463,0.000872269],"genre_scores_gemma":[0.8978335,0.0003335456,0.1002467,0.0002105284,0.00004774583,0.0008007536,0.0004711129,0.00001489711,0.00004124007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8966726,"threshold_uncertainty_score":0.9999267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04719044268613398,"score_gpt":0.2982042658041004,"score_spread":0.2510138231179664,"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."}}