We report progress in visual road following by autonomous robot vehicles. We present results and work in progress in the areas of system architecture, image rectification and camera calibration, oriented edge tracking, color classification and road-region segmentation, extracting geometric structure. and the use of a map. In test runs of an outdoor robot vehicle, the Terregator, under control of the Warp computer, we have demonstrated continuous motion vision-guided road-following at speeds up to 1 .OB km/hour with image processing and steering servo loop times of 3 sec.
ocusing primarily on system architecture, this article describes the current status of autonomous land vehicle (ALV) research at F Carnegie Mellon University's Robotics Institute. We will (1) discuss issues concerning outdoor navigation; (2) describe our system's perception, planning, and control components that address these issues; (3) examine Codger, the software system that integrates these components into a single system, synchronizing the dataflow between them (thereby rnaximizing parallelism); and (4) present the results of our experiments, problems uncovered in the process, and plans for addressing those problems.Carnegie Mellon's ALV group has created an autonomous mobile robot system capable of operating in outdoor environments. Using two sensors-a color camera and a laser range finder-our system can drive a robot vehicle continuously on a network of sidewalks, up a bicycle slope, and over a curved road through an area populated with trees. The complexity of real-world domains and requirements for continuous and real-time motion require that such robot systems provide architectural support for multiple sensors and parallel processing-capabilities not found in simpler robot systems. At CMU, we are studying mobile robot system architecture and have developed a navigation system working at two test sites and on two experimental vehicles.
Aims/Introduction
Knowing the collective clinical factors that determine patient response to glucose‐lowering medication would be beneficial in the treatment of type 2 diabetes. We carried out a retrospective cohort study to explore the combination of clinical factors involved in its therapeutic efficacy.
Materials and Methods
The results of cohort studies retrieved using the CoDiC® database across Japan from January 2005 to July 2018 were analyzed based on criterion that using insulin therapy indicates severe type 2 diabetes.
Results
A logistic regression analysis showed that age at diagnosis, disease duration, hemoglobin A1c (HbA1c) and serum C‐peptide reactivity (CPR) at medication commencement were associated with the probability of insulin treatment. Receiver operating characteristic curve showed that these clinical factors predicted insulin treatment positivity with an area under the curve of >0.600. The area under the curve increased to 0.674 and 0.720 for the disease duration‐to‐age at diagnosis ratio and HbA1c‐to‐CPR ratio, respectively. Furthermore, area under the curve increased to 0.727 and 0.750 in the indices (duration‐to‐age ratio at diagnosis × 43 + HbA1c) and (duration‐to‐age ration at diagnosis × 21 + HbA1c‐to‐CPR ratio), respectively. After stratification to three groups according to the indices, monthly HbA1c levels during 6 months of treatment were higher in the upper one‐third than in the lower one‐third of patients, and many patients did not achieve the target HbA1c level (53 mmol/mol) in the upper one‐third, although greater than fourfold more patients were administered insulin in the upper one‐third.
Conclusions
The combination of disease duration‐to‐age at diagnosis and HbA1c‐to‐CPR ratios is a collective risk factor that predicts response to the medications.
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