Pulmonary artery end-diastolic pressure recordings after myocardial infarction (author's transl)

29 patients with acute myocardial infarction were subdivided into 3 groups using the information gained by continuous measurement of the pulmonary artery end-diastolic pressure (PAEDP) during a stay of 3 to 5 days in the coronary care unit of this hospital. Group I comprised patients with a PAEDP of below 12 mm Hg (without treatment), group II those with a PAEDP of between 12 and 20 mm Hg and group III those patients with a PAEDP of above 20 mm Hg. 3 to 6 months after rehabilitation and ambulant "coronary training' a follow-up control PAEDP measurement was performed at rest and during ergometric stress with the bicycle exercise test. 2 out of the 15 patients in group I had a pathological PAEDP at rest, whilst exercise of 50 watts raised the PAEDP to pathological values in 40% of this group of patients. Group II: 58% of the patients with an initially-raised PAEDP showed a normal value at follow-up examination 3 months subsequently. Exercise of 50 watts raised the PAEDP to pathological values in 66% of the patients in this group. Group III. The pathologically high PAEDP recordings at rest made it impossible to subject these patients to stress with the bicycle ergometer. The prognostic value of the classification of patients with myocardial infarction into pressure groups and the importance of PAEDP follow-up measurements on patients after myocardial infarction at rest and after ergometric stress are discussed.     

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around $270\text{km h}{}^{-1}$ and $28ms{}^{-2}$. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.