{"id":"W3135437426","doi":"10.5220/0010434600002932","title":"PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain","year":2021,"lang":"en","type":"article","venue":"Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Intertek (Canada); University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Lidar; Radar; Computer vision; Occupancy grid mapping; Radar imaging; Deep learning; Automotive industry; Advanced driver assistance systems; Segmentation; Process (computing); Real-time computing; Remote sensing; Mobile robot; Engineering; Telecommunications; Geography; Robot; Aerospace engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.000221056,0.0001584069,0.0002276886,0.000252568,0.0001236449,0.000104065,0.001426584,0.0001324489,0.00001059013],"category_scores_gemma":[0.00002591856,0.0001366852,0.00003617096,0.0009836853,0.0001335179,0.0005529727,0.000260877,0.000298678,0.000002481757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001082167,"about_ca_system_score_gemma":0.00007048336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005912383,"about_ca_topic_score_gemma":0.00002579542,"domain_scores_codex":[0.9986627,0.00001690249,0.000424883,0.0004494468,0.0002501989,0.000195898],"domain_scores_gemma":[0.9989396,0.00002274668,0.0002854624,0.0001774797,0.0005350595,0.00003960433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002710024,0.0001119537,0.02137683,0.0000243244,0.00004364301,0.000004310007,0.0005479488,0.0003694414,0.1092757,0.8669059,0.00000917231,0.001303671],"study_design_scores_gemma":[0.00140691,0.0002419815,0.01341922,0.001078742,0.00003288148,0.0001980815,0.006273453,0.1949869,0.6691384,0.1112408,0.001322573,0.0006601393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9212474,0.0001947265,0.06802588,0.006811319,0.000286344,0.000777051,0.00001657639,0.0001047928,0.002535945],"genre_scores_gemma":[0.9921579,0.0001154331,0.007394434,0.00009729364,0.00001510964,0.00004926461,0.00000514205,0.000009233611,0.0001562164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7556652,"threshold_uncertainty_score":0.5573862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05054007310239281,"score_gpt":0.3046243111669547,"score_spread":0.2540842380645618,"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."}}