{"id":"W4413077800","doi":"10.1088/1742-6596/3058/1/012004","title":"Vision Based Navigation System for 8x8 Scaled Combat Vehicle","year":2025,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Robot; Steering wheel; Computer vision; Computer science; Navigation system; Artificial intelligence; Obstacle avoidance; Automotive industry; Lidar; Particle filter; Obstacle; Simulation; Filter (signal processing); Mobile robot; Engineering; Automotive engineering; Aerospace 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":[0.0001878261,0.0004762968,0.0004736504,0.0004571468,0.0002709407,0.000373775,0.0006088353,0.0005908636,0.005385112],"category_scores_gemma":[0.0002410401,0.000157062,0.000188539,0.0001636899,0.0001210783,0.0002013614,0.0003083919,0.0003034654,0.001586469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003275652,"about_ca_system_score_gemma":0.0006279994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006641286,"about_ca_topic_score_gemma":0.004593901,"domain_scores_codex":[0.9998165,0.0000162266,0.000009141391,0.00005000538,0.00007934392,0.000028808],"domain_scores_gemma":[0.9998482,0.000009748361,0.00001416751,0.00001214845,0.00009845383,0.00001718167],"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":[0.002121939,0.0004727196,0.006118435,0.0005868193,0.0001231172,0.002232234,0.000849805,0.08504155,0.4565572,0.001961015,0.01641842,0.4275168],"study_design_scores_gemma":[0.0003926672,0.0030311,0.01398102,0.00009595735,0.0001178723,0.0009470497,0.0003298912,0.8116133,0.137315,0.0007348158,0.03132987,0.0001114657],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6965653,0.0005674076,0.2586713,0.0006430405,0.0008202109,0.0006834654,0.0009377283,0.0173384,0.02377326],"genre_scores_gemma":[0.9508519,0.00007420609,0.03883318,0.0001191605,0.00002087832,0.0001851376,0.0004737113,0.00003370404,0.009408204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006641286,"threshold_uncertainty_score":0.01801497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01013384847912125,"score_gpt":0.2347076356272478,"score_spread":0.2245737871481265,"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."}}