A supramolecular and liquid crystalline water‐based alignment medium based on azobenzene‐substituted 1,3,5‐benzenetricarboxamides

A supramolecular, lyotropic liquid crystalline alignment medium based on an azobenzene‐containing 1,3,5‐benzenetricarboxamide (BTA) building block is described and investigated. As we demonstrate, this water‐based system is suitable for the investigation of various water‐soluble analytes and allows for a scaling of alignment strength through variation of temperature. Additionally, alignment is shown to reversibly collapse above a certain temperature, yielding an isotropic solution. This collapse allows for isotropic reference measurements, which are typically needed in addition to those in an anisotropic environment, to be performed using the same sample just by varying the temperature. The medium described thus provides easy access to anisotropic NMR observables and simplifies structure elucidation techniques based thereon

Providentia - A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data

Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data.Comment: Accepted for publication in the Journal of Field Robotic

Particle removal by separators including fluidized lamella packings

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Vorausschauende Wahrnehmung für sicheres automatisiertes Fahren

Intelligente Infrastruktursysteme können den Wahrnehmungshorizont von automatisierten Fahrzeugen stark erweitern und dadurch sicheres, vorausschauendes Fahren ermöglichen. Dafür muss klar sein, wie genau das von ihnen erstellte Abbild der aktuellen Verkehrssituation ist. Aufgrund der fehlenden Grundwahrheit der Fahrzeugpositionen gestaltet sich eine Validierung jedoch schwierig, es bedarf neuer Ideen. In diesem Artikel wird am Beispiel des Providentia-Systems ein Konzept präsentiert, wie intelligente Infrastruktursysteme mittels Luftbildauswertung validiert werden können