{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":9,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":9,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"c7e3ac91a535","filters":{"venue":"2022 IEEE Intelligent Vehicles Symposium (IV)"}},"results":[{"id":"W3110317294","doi":"10.1109/iv51971.2022.9827055","title":"Multi-Modal Hybrid Architecture for Pedestrian Action Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Huawei Technologies (Canada)","funders":"","keywords":"Pedestrian; Computer science; Modal; Artificial intelligence; Pedestrian detection; Feed forward; Architecture; Range (aeronautics); Machine learning; Pedestrian crossing; Engineering; Transport engineering; Control engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02033659369225852,"gpt":0.2409992678532265,"spread":0.220662674160968,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003833925,0.0003385712,0.0003050287,0.0002972602,0.0006364972,0.00003657398,0.0004662389,0.0001696358,0.0002108002],"category_scores_gemma":[0.00001527558,0.0003896034,0.0002453813,0.0002699807,0.00008243768,0.0001299992,0.0001133739,0.0009426784,0.00004850996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005449532,"about_ca_system_score_gemma":0.00005296065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003370324,"about_ca_topic_score_gemma":0.00005120492,"domain_scores_codex":[0.9980558,0.00008707658,0.0005128648,0.0004928354,0.0002919468,0.0005595089],"domain_scores_gemma":[0.9991832,0.0001118521,0.00009810333,0.0004516132,0.00004122818,0.0001140146],"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":[0.0003765805,0.0002771997,0.001083886,0.0001810287,0.0002589041,0.00002529867,0.0008886742,0.6301566,0.3167995,0.0002094417,0.006265547,0.04347735],"study_design_scores_gemma":[0.001108739,0.0006303445,0.0005383128,0.00001831534,0.0001197029,0.000208356,0.000683015,0.6371849,0.2154696,0.0009596029,0.1424157,0.0006635007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7855868,0.0004992789,0.2040712,0.0009086808,0.00389295,0.001405929,0.0007608587,0.002474642,0.0003996778],"genre_scores_gemma":[0.9962543,0.0002650436,0.001109489,0.0001206344,0.000325982,0.0007865934,0.0002048282,0.0001094271,0.0008236598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2106675,"threshold_uncertainty_score":0.9998556,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226144994","doi":"10.1109/iv51971.2022.9827450","title":"Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Generator (circuit theory); Domain (mathematical analysis); Domain adaptation; Machine learning; Consistency (knowledge bases); Class (philosophy); Labeled data; Adversarial system; Synthetic data; Generative grammar; Baseline (sea); Pattern recognition (psychology); Adaptation (eye); Data mining; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01474175575509452,"gpt":0.227972998793619,"spread":0.2132312430385245,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005354983,0.0002150606,0.0002338508,0.0003820171,0.0002964028,0.00003125632,0.0002190235,0.0001478992,0.00002872925],"category_scores_gemma":[0.0000128267,0.0002673774,0.0000638956,0.000371033,0.00004214983,0.000119991,0.00007623948,0.0004114419,0.0000234267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006690452,"about_ca_system_score_gemma":0.00003121713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009024552,"about_ca_topic_score_gemma":0.0007314525,"domain_scores_codex":[0.9985183,0.00008129916,0.0004582073,0.000395987,0.0001721795,0.0003740439],"domain_scores_gemma":[0.9994209,0.0001454021,0.00007469041,0.0002510546,0.00002717457,0.00008075382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002021825,0.0001222785,0.005942157,0.00008522612,0.00006412865,0.00001135009,0.004123662,0.3373656,0.5812535,0.00254999,0.0001781032,0.06810188],"study_design_scores_gemma":[0.0005582582,0.0004408196,0.01494756,0.0000194581,0.00003060538,0.00003248596,0.002357299,0.9365429,0.02983929,0.00258218,0.01213611,0.0005130573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9145535,0.00009853992,0.08223729,0.000582947,0.0005778065,0.0008126395,0.00003365597,0.0005656725,0.000537972],"genre_scores_gemma":[0.9976723,0.0001360382,0.00101445,0.0000625237,0.00007368973,0.0007539859,0.00003584127,0.0000575699,0.0001936138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5991773,"threshold_uncertainty_score":0.9999778,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4286285615","doi":"10.1109/iv51971.2022.9827148","title":"Cooperative Adaptive Cruise Control using Vehicle-to-Vehicle communication and Deep Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Traffic control and management","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Cooperative Adaptive Cruise Control; Cruise control; Computer science; Vehicular communication systems; Vehicle-to-vehicle; Cruise; Intelligent transportation system; Control (management); Automotive engineering; Latency (audio); Real-time computing; Vehicular ad hoc network; Wireless; Engineering; Artificial intelligence; Computer network; Telecommunications; Wireless ad hoc network; Aerospace engineering; Transport engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01403929005731888,"gpt":0.2210637997733722,"spread":0.2070245097160533,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005172296,0.0003606583,0.0004077075,0.000250705,0.0007791965,0.0001329069,0.0004478072,0.00006985247,0.000219722],"category_scores_gemma":[0.0000186823,0.0004110695,0.0001135457,0.0004300119,0.0000798309,0.0002023931,0.0003162861,0.000720121,0.00005672926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000467254,"about_ca_system_score_gemma":0.00002593053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001715236,"about_ca_topic_score_gemma":0.0001198264,"domain_scores_codex":[0.9976893,0.0003742247,0.0005064695,0.0004639457,0.0004320447,0.000534002],"domain_scores_gemma":[0.9989223,0.0002322704,0.000094603,0.0004346135,0.00009803204,0.0002181614],"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":[0.0002158786,0.00008773885,0.0003164683,0.00002790269,0.0002103667,0.00001715208,0.003410645,0.8940197,0.08375582,0.0005888499,0.0003322153,0.01701724],"study_design_scores_gemma":[0.001093161,0.0004043531,0.0005850365,0.00003502702,0.0001303234,0.00001365102,0.003517238,0.9705235,0.002701971,0.00005894653,0.02040379,0.000533051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580502,0.004084371,0.03201506,0.0007794764,0.0007583812,0.001642417,0.00005972561,0.0007818369,0.00182855],"genre_scores_gemma":[0.9977056,0.000554989,0.0003234323,0.000436851,0.00009821704,0.0003419603,0.00001851294,0.00008991601,0.000430498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08105385,"threshold_uncertainty_score":0.9998341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285813121","doi":"10.1109/iv51971.2022.9827156","title":"A Sufficient Condition for Convex Hull Property in General Convex Spatio-Temporal Corridors","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Convex hull; Smoothness; Regular polygon; Mathematical optimization; Computer science; Motion planning; Parametric equation; Parametric statistics; Convex combination; Property (philosophy); Hull; Mathematics; Convex optimization; Geometry; Robot; Mathematical analysis; Engineering; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02546569992971375,"gpt":0.2626503696655514,"spread":0.2371846697358376,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001402218,0.0004398936,0.0005721088,0.0004885776,0.0005377959,0.0002225508,0.001647585,0.0001244774,0.0001743883],"category_scores_gemma":[0.00005109405,0.0004051244,0.0002372994,0.0008697855,0.0001400824,0.0003656029,0.000526161,0.000609628,0.0001018421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007702551,"about_ca_system_score_gemma":0.0003699945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006371863,"about_ca_topic_score_gemma":0.00003620963,"domain_scores_codex":[0.9954676,0.0004563596,0.0009903808,0.001165882,0.00101014,0.0009096078],"domain_scores_gemma":[0.998075,0.0002631605,0.0003986999,0.0008387148,0.0001875235,0.0002369038],"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":[0.0005641091,0.002034463,0.02508296,0.0002236238,0.000163006,0.0005172042,0.01328035,0.8666484,0.02960741,0.005639134,0.04970558,0.006533742],"study_design_scores_gemma":[0.0009844558,0.001022487,0.0005877737,0.00004134252,0.00002172644,0.00008876412,0.0004575252,0.9612136,0.01225176,0.0003867052,0.02230811,0.0006357188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5815546,0.0002312484,0.4035549,0.003090289,0.007084745,0.003068096,0.0002750741,0.0005333498,0.0006077716],"genre_scores_gemma":[0.9741743,0.00003280149,0.0170371,0.001482155,0.0003274874,0.001481035,0.0004333269,0.00008124919,0.004950562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3926197,"threshold_uncertainty_score":0.9998401,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285813009","doi":"10.1109/iv51971.2022.9827272","title":"Vehicle-to-Everything (V2X) in Scenarios: Extending Scenario Description Language for Connected Vehicle Scenario Descriptions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Transport Canada","keywords":"Computer science; Human–computer interaction","retraction":null,"screen_n_in":null,"score":{"opus":0.0175188401406825,"gpt":0.2357673056986028,"spread":0.2182484655579203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001262673,0.0006628033,0.0007268303,0.001134086,0.001084352,0.0001717992,0.001062593,0.0003755905,0.0003368517],"category_scores_gemma":[0.00009443348,0.0008194979,0.0003296004,0.001482531,0.000137492,0.0005246621,0.0003886569,0.001643488,0.0001762692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002312147,"about_ca_system_score_gemma":0.0001465117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004530397,"about_ca_topic_score_gemma":0.0008179794,"domain_scores_codex":[0.9955637,0.0002017133,0.00119914,0.00101894,0.0005796491,0.001436792],"domain_scores_gemma":[0.9982492,0.00029887,0.0001770105,0.0008603843,0.0001111274,0.0003034414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004392592,0.000379184,0.004844577,0.0001586706,0.0001606272,0.0001220203,0.00915762,0.3557556,0.613545,0.001324788,0.002488816,0.01162386],"study_design_scores_gemma":[0.002430174,0.0009503724,0.002922292,0.0002654425,0.0001667195,0.0001535425,0.01009492,0.8619496,0.09459005,0.001086578,0.02329928,0.002091104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9749214,0.001115056,0.01634141,0.001111285,0.002191768,0.00189418,0.0002011269,0.001805689,0.0004180729],"genre_scores_gemma":[0.9951827,0.0001959358,0.001312253,0.0006573521,0.0002157354,0.001153861,0.0001468187,0.0002234726,0.0009118806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.518955,"threshold_uncertainty_score":0.9994256,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4286285554","doi":"10.1109/iv51971.2022.9827316","title":"Ordered-logit pedestrian stress model for traffic flow with automated vehicles","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Traffic control and management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pedestrian; Logit; Mixed logit; Logistic regression; Stress (linguistics); Computer science; Block (permutation group theory); Simulation; Transport engineering; Engineering; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01403343041155633,"gpt":0.2157601792164146,"spread":0.2017267488048582,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003190188,0.0005052662,0.0004790292,0.0002827879,0.000492772,0.0001484579,0.0006927635,0.0001000469,0.0001359565],"category_scores_gemma":[0.000007350337,0.0004966414,0.0002296318,0.000429487,0.00006617752,0.0001626258,0.0001345007,0.0004113761,0.00003150329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003615273,"about_ca_system_score_gemma":0.00008596485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003460331,"about_ca_topic_score_gemma":0.0003042753,"domain_scores_codex":[0.9972777,0.00007255324,0.000590805,0.000637906,0.0005961431,0.0008248992],"domain_scores_gemma":[0.9989327,0.0001305702,0.0001019433,0.0005556472,0.00007676421,0.0002024248],"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":[0.0002901166,0.0002474537,0.00003410479,0.0002240423,0.0002918659,0.00003000119,0.001157571,0.9609182,0.004720798,0.0001356038,0.01033659,0.02161361],"study_design_scores_gemma":[0.001253217,0.0004011989,0.00003793782,0.00003253782,0.000163169,0.00001417801,0.0007924817,0.9809183,0.001631939,0.00005227078,0.01407649,0.0006263063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9209099,0.001259915,0.06184064,0.001258102,0.002085185,0.003291369,0.001159127,0.007011403,0.001184343],"genre_scores_gemma":[0.9952227,0.0002193664,0.00117771,0.0001585868,0.0001648576,0.001231079,0.0001861155,0.0001636867,0.001475847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07431284,"threshold_uncertainty_score":0.9997485,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4286285618","doi":"10.1109/iv51971.2022.9827035","title":"A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pedestrian; Computer science; Replicate; Process (computing); Planner; Scenario testing; Routing (electronic design automation); Fidelity; Simulation; Real-time computing; Artificial intelligence; Transport engineering; Embedded system; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03748177553622607,"gpt":0.2961167319703962,"spread":0.2586349564341702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003728201,0.0003275322,0.0003122991,0.0004969679,0.0004324524,0.00008263365,0.0004194535,0.0001193126,0.0002643092],"category_scores_gemma":[0.00001236947,0.0004072539,0.0001481128,0.0005802985,0.00003528846,0.0001132942,0.0001471711,0.0006590636,0.00004682148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007346795,"about_ca_system_score_gemma":0.00006224692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006645083,"about_ca_topic_score_gemma":0.0003921671,"domain_scores_codex":[0.9975564,0.0001427286,0.0007419722,0.0004806259,0.000569414,0.0005088223],"domain_scores_gemma":[0.9991437,0.00007270954,0.00006303868,0.0004289116,0.00004994097,0.0002417138],"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":[0.00004691319,0.0002832492,0.0007275693,0.00002400402,0.00001180597,0.00005132299,0.001630252,0.9300267,0.06450344,0.0003342635,0.0001772384,0.002183207],"study_design_scores_gemma":[0.0003482311,0.0001880799,0.0008059808,0.00001038829,0.00007040015,0.00001124414,0.0001784947,0.9960583,0.001372224,0.00008565897,0.0004205284,0.0004504717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875982,0.0000515302,0.00996487,0.0001343671,0.0005301619,0.000935228,0.000178596,0.0003600686,0.000246926],"genre_scores_gemma":[0.9972191,0.00003421661,0.0003125888,0.0001740092,0.0001152758,0.001088026,0.0002550628,0.0001112753,0.0006904546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06603156,"threshold_uncertainty_score":0.9998379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285813090","doi":"10.1109/iv51971.2022.9827137","title":"Proprioceptive Observer Design for Speed Estimation in Automated Driving Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Science and Engineering Research Council","keywords":"Slip (aerodynamics); Vehicle dynamics; Kinematics; Computer science; Control theory (sociology); Observer (physics); Road surface; Inertial measurement unit; Inertial frame of reference; Slip angle; Yaw; Simulation; Engineering; Automotive engineering; Artificial intelligence; Aerospace engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01769799776468902,"gpt":0.2307968837000656,"spread":0.2130988859353765,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001027928,0.0003589758,0.0004849379,0.0003114199,0.0002500971,0.0001596785,0.0004533917,0.0001240948,0.00004403212],"category_scores_gemma":[0.00001918584,0.0004019175,0.0001662658,0.0005069624,0.00002731919,0.000199703,0.00007624977,0.0003681383,0.00003956685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00106004,"about_ca_system_score_gemma":0.00006460242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002210137,"about_ca_topic_score_gemma":0.0000450644,"domain_scores_codex":[0.9973545,0.0002500713,0.0008345198,0.0004630641,0.0004890289,0.0006088151],"domain_scores_gemma":[0.9990398,0.000231942,0.0001565281,0.0003665209,0.00009109422,0.0001140818],"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":[0.00007129805,0.0000825215,0.000648463,0.0001612987,0.0001046657,0.00001474094,0.001081667,0.89986,0.09558874,0.0002789172,0.001129789,0.0009779343],"study_design_scores_gemma":[0.0006370912,0.0002333194,0.0003328098,0.00008685548,0.00003809303,0.00002341504,0.0008382569,0.9936041,0.002653008,0.00007658856,0.001026677,0.0004498078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8798702,0.000810973,0.1102444,0.0001376516,0.002960246,0.00398118,0.0001007997,0.001593558,0.0003009273],"genre_scores_gemma":[0.9974627,0.00005721535,0.0004653531,0.00003355593,0.0001157877,0.001202561,0.00007186546,0.0001279,0.0004630826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1175924,"threshold_uncertainty_score":0.9998432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226072710","doi":"10.1109/iv51971.2022.9827410","title":"Intend-Wait-Cross: Towards Modeling Realistic Pedestrian Crossing Behavior","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Intelligent Vehicles Symposium (IV)","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Pedestrian; Computer science; Context (archaeology); Focus (optics); Population; Obedience; Perception; Simulation; Artificial intelligence; Transport engineering; Engineering; Geography; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.03140767621371158,"gpt":0.2821605952823098,"spread":0.2507529190685982,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006094063,0.0004446046,0.0003917692,0.0002961271,0.000935917,0.0005710644,0.0006947006,0.0001448144,0.0007511234],"category_scores_gemma":[0.00003484538,0.0005254775,0.0002789642,0.000428563,0.0001272627,0.0002608961,0.0002384274,0.0008930114,0.0001111626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009297929,"about_ca_system_score_gemma":0.0001603935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004232041,"about_ca_topic_score_gemma":0.0001765198,"domain_scores_codex":[0.9969387,0.0001209586,0.0008990455,0.0005606796,0.0007679677,0.0007126266],"domain_scores_gemma":[0.9988274,0.00006756187,0.000111468,0.0006098087,0.000135189,0.0002485938],"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":[0.00006131019,0.0001377454,0.001334851,0.00009256038,0.00006328984,0.00006930431,0.002173693,0.9568059,0.03143651,0.0004321758,0.000551235,0.006841389],"study_design_scores_gemma":[0.0003614555,0.0001148198,0.0001926618,0.00003111091,0.00009846822,0.00008880805,0.001158262,0.988326,0.004774509,0.0003248753,0.00389654,0.0006325636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9434804,0.0005597026,0.04746241,0.0002074726,0.003214268,0.0005525404,0.0002781856,0.0009764347,0.003268623],"genre_scores_gemma":[0.9967192,0.000247219,0.0003557406,0.0002149756,0.0002793049,0.0003426578,0.0002048977,0.000165707,0.001470236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0532389,"threshold_uncertainty_score":0.9997197,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}