{"id":"W9523852","doi":"10.1016/s0022-2143(98)90100-7","title":"RoadLab: An In-Vehicle Laboratory for Developing On-Board i-ADAS.","year":2010,"lang":"en","type":"article","venue":"Computer Applications in Industry and Engineering","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Western University","funders":"","keywords":"Context (archaeology); Advanced driver assistance systems; Scalability; Computer science; Intelligent transportation system; Instrumentation (computer programming); Road traffic; On board; Vehicle safety; Engineering; Computer security; Systems engineering; Transport engineering; Aeronautics; Artificial intelligence; Automotive 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003683106,0.00216702,0.0006999986,0.002595723,0.001062996,0.001466188,0.002371149,0.001043522,0.07203501],"category_scores_gemma":[0.001369225,0.000694268,0.0008996504,0.0007063975,0.0006995882,0.001185659,0.001763984,0.001184946,0.05506469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000911679,"about_ca_system_score_gemma":0.001884657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009371142,"about_ca_topic_score_gemma":0.001330613,"domain_scores_codex":[0.9977526,0.0004390433,0.0001145599,0.0006224581,0.000792286,0.0002791426],"domain_scores_gemma":[0.9976953,0.0001603137,0.000207545,0.0005008836,0.000606416,0.0008295414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001556432,0.0009915066,0.003203817,0.0003946641,0.00009377948,0.0003776782,0.0004768613,0.0005337453,0.7984126,0.004906379,0.02646329,0.1625892],"study_design_scores_gemma":[0.0004184415,0.004365261,0.002974798,0.0001141309,0.0001701449,0.001910924,0.0002211291,0.001956104,0.4491453,0.000558171,0.5381023,0.00006317542],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2222265,0.01003142,0.5502642,0.002971647,0.003085619,0.008377549,0.02226838,0.04261518,0.1381595],"genre_scores_gemma":[0.2605866,0.005190196,0.3629164,0.00146408,0.0005464785,0.004528835,0.04972012,0.008490215,0.3065571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07203501,"threshold_uncertainty_score":0.2409811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008076695276608549,"score_gpt":0.2221094674315044,"score_spread":0.2140327721548959,"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."}}