{"id":"W4406857000","doi":"10.1109/jiot.2025.3534737","title":"Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Object detection; Edge computing; Object (grammar); Vehicle safety; Artificial intelligence; Automotive engineering; Engineering; Pattern recognition (psychology)","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":[0.0005709705,0.0006878587,0.0005078301,0.0006887045,0.0003170741,0.001587599,0.001178221,0.0009089061,0.001078851],"category_scores_gemma":[0.001718218,0.0002493789,0.0005086595,0.000521183,0.0005684004,0.002063757,0.001001173,0.001261215,0.0007407847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007626765,"about_ca_system_score_gemma":0.0008050438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007713831,"about_ca_topic_score_gemma":0.007625189,"domain_scores_codex":[0.9997373,0.00004172877,0.000008936762,0.00007751384,0.00008018067,0.00005429798],"domain_scores_gemma":[0.9994413,0.0001848414,0.0000494563,0.000084945,0.000194302,0.0000450559],"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":[0.0004725655,0.0004984113,0.01650803,0.0002111524,0.0001611374,0.0002588137,0.0002902634,0.4439486,0.03275143,0.01966085,0.007570952,0.4776677],"study_design_scores_gemma":[0.00000384495,0.00004430664,0.0007408268,0.00001257819,0.00001258831,0.00003499553,0.00004255178,0.9871797,0.005587451,0.004628097,0.00170455,0.000008469727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2257526,0.00130569,0.7584569,0.00132434,0.0001905809,0.0001261735,0.0002967389,0.002277398,0.0102696],"genre_scores_gemma":[0.8787944,0.0005872801,0.1158687,0.000375395,0.00004924848,0.00003561618,0.0005042038,0.00008241474,0.003702601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007713831,"threshold_uncertainty_score":0.01533782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01279372322506358,"score_gpt":0.2810222540667627,"score_spread":0.2682285308416991,"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."}}