{"id":"W2995122008","doi":"10.1109/lra.2019.2961051","title":"MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map","year":2019,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Computer Research Institute of Montréal","funders":"Toyota Research Institute","keywords":"Intersection (aeronautics); Computer science; Planner; Global Positioning System; Road map; Fuse (electrical); Scope (computer science); Frame (networking); Computer vision; Scale (ratio); Point (geometry); Plan (archaeology); Navigation system; Artificial intelligence; Path (computing); Autonomous system (mathematics); Real-time computing; Geography; Cartography; Engineering; Telecommunications; Computer network; Mathematics","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":[0.00009204892,0.0001534072,0.0001675016,0.0001146265,0.00007556244,0.00004572655,0.00007897385,0.0001414462,0.0000216573],"category_scores_gemma":[0.000002730352,0.0001612475,0.00003965033,0.0000982655,0.00004070033,0.0002191204,0.00001419653,0.0002031795,0.0001961185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001128514,"about_ca_system_score_gemma":0.00000919895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000337362,"about_ca_topic_score_gemma":0.00000328881,"domain_scores_codex":[0.9992708,0.00001773139,0.0002458013,0.0001847439,0.00008848108,0.0001924317],"domain_scores_gemma":[0.9996766,0.00002242635,0.00006147468,0.0001805747,0.00001919485,0.00003971731],"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":[0.00002475778,0.00004097137,0.02392637,0.0004682914,0.0001677744,0.0000144745,0.001199463,0.7498687,0.1822487,0.003039197,0.0009267126,0.03807465],"study_design_scores_gemma":[0.0005700027,0.00003867746,0.01938992,0.0000803976,0.00002813182,0.00003916302,0.00005542246,0.9729905,0.005529609,0.0002205311,0.0007623728,0.0002953301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9008499,0.00003976403,0.09580512,0.001087351,0.0007864387,0.0002591957,0.000002612233,0.001009891,0.0001597701],"genre_scores_gemma":[0.99546,0.000007777587,0.004120834,0.0001946971,0.00005032745,0.00001418924,0.00001944455,0.00002767939,0.0001050275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2231218,"threshold_uncertainty_score":0.6575481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00475685519518063,"score_gpt":0.1967174257838828,"score_spread":0.1919605705887021,"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."}}