{"id":"W2610801907","doi":"10.1117/12.2263514","title":"Unobtrusive and assistive obstacle avoidance for tele-operation of ground vehicles","year":2017,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Golder Associates (Canada)","funders":"Ontario Centres of Excellence","keywords":"SAFER; Obstacle avoidance; Obstacle; Computer science; Collision avoidance; Operator (biology); Robot; Kinematics; Unmanned ground vehicle; Simulation; Interference (communication); Mobile robot; Engineering; Control engineering; Artificial intelligence; Computer security; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006557797,0.0002387468,0.0003822986,0.00006819736,0.0002469998,0.0003000388,0.001537037,0.0001426287,8.212982e-7],"category_scores_gemma":[0.001271313,0.000206358,0.000269563,0.0001208865,0.0003208642,0.001209377,0.0003138271,0.0001878107,4.098903e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009172099,"about_ca_system_score_gemma":0.00004747669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001949156,"about_ca_topic_score_gemma":1.132439e-7,"domain_scores_codex":[0.9982411,1.930168e-8,0.0005221116,0.0004251222,0.0005060716,0.0003055718],"domain_scores_gemma":[0.9969732,0.000253325,0.0006607042,0.0001122925,0.001908713,0.00009175071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005992959,0.00009248818,0.001278248,0.0004725184,0.0003064666,1.314567e-7,0.0006126305,0.0003728502,0.3299548,0.6639255,0.0007102235,0.002214202],"study_design_scores_gemma":[0.002331797,0.0008003531,0.03816313,0.0006977285,0.0001545026,0.00003263896,0.0009507668,0.7278454,0.2210195,0.006651633,0.0007509403,0.0006015222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9900407,0.00009837391,0.006427392,0.002052338,0.000278162,0.000528555,0.00004768844,0.00005015948,0.0004766879],"genre_scores_gemma":[0.4781141,0.00003444447,0.5213697,0.00004313247,0.0002004175,0.0001021055,0.000003184727,0.00002491149,0.0001079735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7274726,"threshold_uncertainty_score":0.8415034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01685971454708412,"score_gpt":0.2478738182676423,"score_spread":0.2310141037205581,"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."}}