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Die vorliegende Arbeit beschäftigt sich mit der Konzeptionierung und Entwicklung eines neuartigen Fahrerassistenzsystems für Nutzfahrzeuge, welches die lichte Höhe von vor dem Fahrzeug befindlichen Hindernissen berechnet und über einen Abgleich mit der einstellbaren Fahrzeughöhe die Passierbarkeit bestimmt. Dabei werden die von einer Monokamera aufgenommenen Bildsequenzen genutzt, um durch indirekte und direkte Rekonstruktionsverfahren ein 3D-Abbild der Fahrumgebung zu erschaffen. Unter Hinzunahme einer Radodometrie-basierten Eigenbewegungsschätzung wird die erstellte 3D-Repräsentation skaliert und eine Prädiktion der longitudinalen und lateralen Fahrzeugbewegung ermittelt. Basierend auf dem vertikalen Höhenplan der Straßenoberfläche, welcher über die Aneinanderreihung mehrerer Ebenen modelliert wird, erfolgt die Klassifizierung des 3D-Raums in Fahruntergrund, Struktur und potentielle Hindernisse. Die innerhalb des Fahrschlauchs liegenden Hindernisse werden hinsichtlich ihrer Entfernung und Höhe bewertet. Ein daraus abgeleitetes Warnkonzept dient der optisch-akustischen Signalisierung des Hindernisses im Kombiinstrument des Fahrzeugs. Erfolgt keine entsprechende Reaktion durch den Fahrer, so wird bei kritischen Hindernishöhen eine Notbremsung durchgeführt. Die geschätzte Eigenbewegung und berechneten Hindernisparameter werden mithilfe von Referenzsensorik bewertet. Dabei kommt eine dGPS-gestützte Inertialplattform sowie ein terrestrischer und mobiler Laserscanner zum Einsatz. Im Rahmen der Arbeit werden verschiedene Umgebungssituationen und Hindernistypen im urbanen und ländlichen Raum untersucht und Aussagen zur Genauigkeit und Zuverlässigkeit des Verfahrens getroffen. Ein wesentlicher Einflussfaktor auf die Dichte und Genauigkeit der 3D-Rekonstruktion ist eine gleichmäßige Umgebungsbeleuchtung innerhalb der Bildsequenzaufnahme. Es wird in diesem Zusammenhang zwingend auf den Einsatz einer Automotive-tauglichen Kamera verwiesen. Die durch die Radodometrie bestimmte Eigenbewegung eignet sich im langsamen Geschwindigkeitsbereich zur Skalierung des 3D-Punktraums. Dieser wiederum sollte durch eine Kombination aus indirektem und direktem Punktrekonstruktionsverfahren erstellt werden. Der indirekte Anteil stützt dabei die Initialisierung des Verfahrens zum Start der Funktion und ermöglicht eine robuste Kameraschätzung. Das direkte Verfahren ermöglicht die Rekonstruktion einer hohen Anzahl an 3D-Punkten auf den Hindernisumrissen, welche zumeist die Unterkante beinhalten. Die Unterkante kann in einer Entfernung bis zu 20 m detektiert und verfolgt werden. Der größte Einflussfaktor auf die Genauigkeit der Berechnung der lichten Höhe von Hindernissen ist die Modellierung des Fahruntergrunds. Zur Reduktion von Ausreißern in der Höhenberechnung eignet sich die Stabilisierung des Verfahrens durch die Nutzung von zeitlich vorher zur Verfügung stehenden Berechnungen. Als weitere Maßnahme zur Stabilisierung wird zudem empfohlen die Hindernisausgabe an den Fahrer und den automatischen Notbremsassistenten mittels einer Hysterese zu stützen. Das hier vorgestellte System eignet sich für Park- und Rangiervorgänge und ist als kostengünstiges Fahrerassistenzsystem interessant für Pkw mit Aufbauten und leichte Nutzfahrzeuge. / The present work deals with the conception and development of a novel advanced driver assistance system for commercial vehicles, which estimates the clearance height of obstacles in front of the vehicle and determines the passability by comparison with the adjustable vehicle height. The image sequences captured by a mono camera are used to create a 3D representation of the driving environment using indirect and direct reconstruction methods. The 3D representation is scaled and a prediction of the longitudinal and lateral movement of the vehicle is determined with the aid of a wheel odometry-based estimation of the vehicle's own movement. Based on the vertical elevation plan of the road surface, which is modelled by attaching several surfaces together, the 3D space is classified into driving surface, structure and potential obstacles. The obstacles within the predicted driving tube are evaluated with regard to their distance and height. A warning concept derived from this serves to visually and acoustically signal the obstacle in the vehicle's instrument cluster. If the driver does not respond accordingly, emergency braking will be applied at critical obstacle heights. The estimated vehicle movement and calculated obstacle parameters are evaluated with the aid of reference sensors. A dGPS-supported inertial measurement unit and a terrestrial as well as a mobile laser scanner are used. Within the scope of the work, different environmental situations and obstacle types in urban and rural areas are investigated and statements on the accuracy and reliability of the implemented function are made. A major factor influencing the density and accuracy of 3D reconstruction is uniform ambient lighting within the image sequence. In this context, the use of an automotive camera is mandatory. The inherent motion determined by wheel odometry is suitable for scaling the 3D point space in the slow speed range. The 3D representation however, should be created by a combination of indirect and direct point reconstruction methods. The indirect part supports the initialization phase of the function and enables a robust camera estimation. The direct method enables the reconstruction of a large number of 3D points on the obstacle outlines, which usually contain the lower edge. The lower edge can be detected and tracked up to 20 m away. The biggest factor influencing the accuracy of the calculation of the clearance height of obstacles is the modelling of the driving surface. To reduce outliers in the height calculation, the method can be stabilized by using calculations from older time steps. As a further stabilization measure, it is also recommended to support the obstacle output to the driver and the automatic emergency brake assistant by means of hysteresis. The system presented here is suitable for parking and maneuvering operations and is interesting as a cost-effective driver assistance system for cars with superstructures and light commercial vehicles.

<p>Deep Learning (DL)-based edge intelligence has garnered significant attention in recent years due to the rapid proliferation of the Internet of Things (IoT), embedded, and intelligent systems, collectively termed edge devices. Sensor data streams acquired by these edge devices are processed by a Deep Neural Network (DNN) application that runs on the device itself or in the cloud. However, the high computational complexity and energy consumption of processing DNNs often limit their deployment on these edge inference systems due to limited compute, memory and energy resources. Furthermore, high costs, strict application latency demands, data privacy, security constraints, and the absence of reliable edge-cloud network connectivity heavily impact edge application efficiency in the case of cloud-assisted DNN inference. Inevitably, performance and energy efficiency are of utmost importance in these edge inference systems, aside from the accuracy of the application. To facilitate energy- efficient edge inference systems running computationally complex DNNs, this dissertation makes three key contributions.</p> <p><br></p> <p>The first contribution adopts a full-system approach to Approximate Computing, a design paradigm that trades off a small degradation in application quality for significant energy savings. Within this context, we present the foundational concepts of AxIS, the first approximate edge inference system that jointly optimizes the constituent subsystems leading to substantial energy benefits compared to optimization of the individual subsystem. To illustrate the efficacy of this approach, we demonstrate multiple versions of an approximate smart camera system that executes various DNN-based unimodal computer vision applications, showcasing how the sensor, memory, compute, and communication subsystems can all be synergistically approximated for energy-efficient edge inference.</p> <p><br></p> <p>Building on this foundation, the second contribution extends AxIS to multimodal AI, harnessing data from multiple sensor modalities to impart human-like cognitive and perceptual abilities to edge devices. By exploring optimization techniques for multiple sensor modalities and subsystems, this research reveals the impact of synergistic modality-aware optimizations on system-level accuracy-efficiency (AE) trade-offs, culminating in the introduction of SysteMMX, the first AE scalable cognitive system that allows efficient multimodal inference at the edge. To illustrate the practicality and effectiveness of this approach, we present an in-depth case study centered around a multimodal system that leverages RGB and Depth sensor modalities for image segmentation tasks.</p> <p><br></p> <p>The final contribution focuses on optimizing the performance of an edge-cloud collaborative inference system through intelligent DNN partitioning and computation offloading. We delve into the realm of distributed inference across edge devices and cloud servers, unveiling the challenges associated with finding the optimal partitioning point in DNNs for significant inference latency speedup. To address these challenges, we introduce PArtNNer, a platform-agnostic and adaptive DNN partitioning framework capable of dynamically adapting to changes in communication bandwidth and cloud server load. Unlike existing approaches, PArtNNer does not require pre-characterization of underlying edge computing platforms, making it a versatile and efficient solution for real-world edge-cloud scenarios.</p> <p><br></p> <p>Overall, this thesis provides novel insights, innovative techniques, and intelligent solutions to enable energy-efficient AI at the edge. The contributions presented herein serve as a solid foundation for future researchers to build upon, driving innovation and shaping the trajectory of research in edge AI.</p>

<p>A thesis study investigating the development and evaluation of a computer vision (CV) system for a manufacturing assembly task is reported. The CV inference results are compared to a Manufacturing Process Plan and an automation method completes a buyoff in the software, Solumina. Research questions were created and three hypotheses were tested. A literature review was conducted recognizing little consensus of Industry 4.0 technology adoption in manufacturing industries. Furthermore, the literature review uncovered the need for additional research within the topic of CV. Specifically, literature points towards more research regarding the cognitive capabilities of CV in manufacturing. A CV system was developed and evaluated to test for 90% or greater confidence in part detection. A CV dataset was developed and the system was trained and validated with it. Dataset contextualization was leveraged and evaluated, as per literature. A CV system was trained from custom datasets, containing six classes of part. The pre-contextualization dataset and post-contextualization dataset was compared by a Two-Sample T-Test and statistical significance was noted for three classes. A python script was developed to compare as-assembled locations with as-defined positions of components, per the Manufacturing Process Plan. A comparison of yields test for CV-based True Positives (TPs) and human-based TPs was conducted with the system operating at a 2σ level. An automation method utilizing Microsoft Power Automate was developed to complete the cognitive functionality of the CV system testing, by completing a buyoff in the software, Solumina, if CV-based TPs were equal to or greater than human-based TPs.</p>