Probabilistic, Variable and Interaction-aware Situation Recognition

Future advanced driver assistance systems (ADAS) as well as autonomous driving functions will extend their applicability to more complex highway scenarios and inner-city traffic. For these systems it is a prerequisite to know how an encountered traffic scene is most likely going to evolve. Situation recognition aims to predict the high level behavior patterns traffic participants pursue. Thus, it provides valuable information that helps to predict the next few seconds of a traffic scene. The extension of ADAS and autonomous driving functions to more complex scenarios poses a problem to state-of-the-art situation recognition systems due to the variability of the encountered scene layouts, the presence of multiple interacting traffic participants and the concomitant large number of possible situation classes. This thesis proposes and discusses approaches that tackle these challenges. A novel discriminative maneuver estimation framework provides the possibility to assess traffic scenes with varying layout. It is based on reusable, partial classifiers that are combined online using a technique called pairwise probability coupling. The real-world evaluations indicate that the assembled probabilistic maneuver estimation is able to provide superior classification results. A novel interaction-aware situation recognition framework constructs a probabilistic situation assessment over multiple traffic participants without relying on independence assumptions. It allows to assess each traffic participant individually by using maneuver estimation systems that determine complete conditional distributions. A real-world evaluation outlines its applicability and shows its benefits. The challenges associated with the increasing number of possible situation classes are addressed in two ways. Both frameworks allow to reuse classifiers in different contexts. This reduces the number of models required to cope with a large variety of traffic scenes. Moreover, a situation hypotheses selection scheme provides an efficient way for reducing the number of situation hypotheses. This lowers the computational demands and eases the load on subsequent systems

Multi-task near-field perception for autonomous driving using surround-view fisheye cameras

Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0 - 10 Metern und 360° Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.The formation of eyes led to the big bang of evolution. The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors. The human eye is one of the most sophisticated developments of evolution, but it still has defects. Humans have evolved a biological perception algorithm capable of driving cars, operating machinery, piloting aircraft, and navigating ships over millions of years. Automating these capabilities for computers is critical for various applications, including self-driving cars, augmented reality, and architectural surveying. Near-field visual perception in the context of self-driving cars can perceive the environment in a range of 0 - 10 meters and 360° coverage around the vehicle. It is a critical decision-making component in the development of safer automated driving. Recent advances in computer vision and deep learning, in conjunction with high-quality sensors such as cameras and LiDARs, have fueled mature visual perception solutions. Until now, far-field perception has been the primary focus. Another significant issue is the limited processing power available for developing real-time applications. Because of this bottleneck, there is frequently a trade-off between performance and run-time efficiency. We concentrate on the following issues in order to address them: 1) Developing near-field perception algorithms with high performance and low computational complexity for various visual perception tasks such as geometric and semantic tasks using convolutional neural networks. 2) Using Multi-Task Learning to overcome computational bottlenecks by sharing initial convolutional layers between tasks and developing optimization strategies that balance tasks