{"id":"W4399500931","doi":"10.1016/j.engappai.2024.108767","title":"Exploring Contextual Representation and Multi-modality for End-to-end Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Finnish Center for Artificial Intelligence; Ministry of Culture, Sports and Tourism; Academy of Finland","keywords":"End-to-end principle; Computer science; Representation (politics); Modality (human–computer interaction); End user; Artificial intelligence; Human–computer interaction; Computer vision; World Wide Web","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":[0.0005112679,0.001083019,0.0006345063,0.0004915213,0.0003478337,0.0007408836,0.001331363,0.000929912,0.001623952],"category_scores_gemma":[0.00157528,0.0002563802,0.0007952978,0.0003257742,0.0004027616,0.001582307,0.001499741,0.001236242,0.0005798556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004349202,"about_ca_system_score_gemma":0.0004812832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005254418,"about_ca_topic_score_gemma":0.009053617,"domain_scores_codex":[0.9996184,0.00006451632,0.0000140814,0.0001677443,0.00007157998,0.00006360453],"domain_scores_gemma":[0.9996725,0.00008427451,0.00002236155,0.00008276272,0.0001051442,0.00003296161],"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":[0.0007966623,0.000561812,0.004294381,0.0003681365,0.0002103368,0.0005309687,0.0006998574,0.1827012,0.06447905,0.006381083,0.008461636,0.7305149],"study_design_scores_gemma":[0.00002186095,0.0001902243,0.001819518,0.0000278285,0.00005865646,0.0001369677,0.0002045214,0.9654543,0.01856658,0.008501994,0.004988855,0.00002859778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1176123,0.001565945,0.8657729,0.0003631428,0.0001834637,0.0001011754,0.0006709261,0.01007155,0.00365862],"genre_scores_gemma":[0.8501214,0.000274026,0.1462936,0.0001990053,0.00006169694,0.00007284966,0.001004366,0.0001642654,0.001808772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005254418,"threshold_uncertainty_score":0.01044768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07495864507117614,"score_gpt":0.3032767093150892,"score_spread":0.228318064243913,"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."}}