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Durch die voranschreitende Entwicklung der Technologie im Bereich der
Automobilindustrie können immer mehr digitale Spuren in Fahrzeugen festgestellt werden. Das führt zu einem immer wichtiger werdenden Gebiet, der digitalen Fahrzeugforensik. Dieses beschäftigt sich mit dem Auslesen der Fahrzeugspeicher, die meist proprietäre Dateitypen der Automobilhersteller enthalten. Somit ist der Aufbau dieser Dateien meist unbekannt, was einen Unterschied zur digitalen Forensik darstellt. Hier können durch sogenannte File Carver Dateitypen, anhand bekannter Byte Sequenzen, wie Header oder Footer, erkannt werden. Unbekannte proprietäre Dateien, wie die der Automobilindustrie können somit meist nicht gefunden werden.
Das Ziel dieser Arbeit ist es zu untersuchen, in wieweit klassische File Carver die spezifischen Dateitypen der Automobilbranche erkennen, und ob KI-basierte Ansätze hier möglicherweise einen Vorteil bieten können. Hierzu wird ein synthetischer Datensatz erstellt, um eine Basis mit relevanten Dateitypen zu schaffen.
Die Tests der Softwares auf dem erstellten Datensatz zeigen, dass sich der File Carver Autopsy am Besten für eine Untersuchung in der digitalen Fahrzeugforensik eignet. Jedoch lassen die erzielten Ergebnisse der KI-basierten Methoden auf ein deutliches Entwicklungspotential schließen.
Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap, we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly, it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features, and (ii) compressed orthogonal attention features shared between vehicles. Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30%, and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications, showcasing enhanced accuracy and enriched semantic-awareness in road environments.
Numerous prognostic factors are currently assessed histopathologically in biopsies of canine mast cell tumors to evaluate clinical behavior. In addition, PCR analysis of the c-Kit exon 11 mutational status is often performed to evaluate the potential success of a tyrosine kinase inhibitor therapy. This project aimed at training deep learning models (DLMs) to identify the c-Kit-11 mutational status of MCTs solely based on morphology without additional molecular analysis. HE slides of 195 mutated and 173 non-mutated tumors were stained consecutively in two different laboratories and scanned with three different slide scanners. This resulted in six different datasets (stain-scanner variations) of whole slide images. DLMs were trained with single and mixed datasets and their performances was assessed under scanner and staining domain shifts. The DLMs correctly classified HE slides according to their c-Kit 11 mutation status in, on average, 87% of cases for the best-suited stain-scanner variant. A relevant performance drop could be observed when the stain-scanner combination of the training and test dataset differed. Multi-variant datasets improved the average accuracy but did not reach the maximum accuracy of algorithms trained and tested on the same stain-scanner variant. In summary, DLM-assisted morphological examination of MCTs can predict c-Kit-exon 11 mutational status of MCTs with high accuracy. However, the recognition performance is impeded by a change of scanner or staining protocol. Larger data sets with higher numbers of scans originating from different laboratories and scanners may lead to more robust DLMs to identify c-Kit mutations in HE slides.