An architectural framework for self-configuration and self-improvement at runtime

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Holistic Temporal Situation Interpretation for Traffic Participant Prediction

For a profound understanding of traffic situations including a prediction of traf- fic participants’ future motion, behaviors and routes it is crucial to incorporate all available environmental observations. The presence of sensor noise and depen- dency uncertainties, the variety of available sensor data, the complexity of large traffic scenes and the large number of different estimation tasks with diverging requirements require a general method that gives a robust foundation for the de- velopment of estimation applications. In this work, a general description language, called Object-Oriented Factor Graph Modeling Language (OOFGML), is proposed, that unifies formulation of esti- mation tasks from the application-oriented problem description via the choice of variable and probability distribution representation through to the inference method definition in implementation. The different language properties are dis- cussed theoretically using abstract examples. The derivation of explicit application examples is shown for the automated driv- ing domain. A domain-specific ontology is defined which forms the basis for four exemplary applications covering the broad spectrum of estimation tasks in this domain: Basic temporal filtering, ego vehicle localization using advanced interpretations of perceived objects, road layout perception utilizing inter-object dependencies and finally highly integrated route, behavior and motion estima- tion to predict traffic participant’s future actions. All applications are evaluated as proof of concept and provide an example of how their class of estimation tasks can be represented using the proposed language. The language serves as a com- mon basis and opens a new field for further research towards holistic solutions for automated driving

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

Big data analytics for large-scale wireless networks: Challenges and opportunities

© 2019 Association for Computing Machinery. The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large-scale wireless networks. Big data of large-scale wireless networks has the key features of wide variety, high volume, real-time velocity, and huge value leading to the unique research challenges that are different from existing computing systems. In this article, we present a survey of the state-of-art big data analytics (BDA) approaches for large-scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage, and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large-scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area

Probabilistic Maneuver Recognition in Traffic Scenarios

In this work an approach is presented to model and recognize traffic maneuvers in terms of interactions between different traffic participants on extra urban roads. Results of the recognition concept are presented and evaluated using different sensor setups and its benefit is outlined by an integration into a software framework in the field of Car-to-Car (C2C) communications. Furthermore, recognition results are used in this work to robustly predict vehicle’s trajectories while driving dynami

Traffic Scene Perception for Automated Driving with Top-View Grid Maps

Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen. Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung. Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden. Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps. Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum. Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt. Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht. Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können. Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt

Understanding Cityscapes: Efficient Urban Semantic Scene Understanding

Semantic scene understanding plays a prominent role in the environment perception of autonomous vehicles. The car needs to be aware of the semantics of its surroundings. In particular it needs to sense other vehicles, bicycles, or pedestrians in order to predict their behavior. Knowledge of the drivable space is required for safe navigation and landmarks, such as poles, or static infrastructure such as buildings, form the basis for precise localization. In this work, we focus on visual scene understanding since cameras offer great potential for perceiving semantics while being comparably cheap; we also focus on urban scenarios as fully autonomous vehicles are expected to appear first in inner-city traffic. However, this task also comes with significant challenges. While images are rich in information, the semantics are not readily available and need to be extracted by means of computer vision, typically via machine learning methods. Furthermore, modern cameras have high resolution sensors as needed for high sensing ranges. As a consequence, large amounts of data need to be processed, while the processing simultaneously requires real-time speeds with low latency. In addition, the resulting semantic environment representation needs to be compressed to allow for fast transmission and down-stream processing. Additional challenges for the perception system arise from the scene type as urban scenes are typically highly cluttered, containing many objects at various scales that are often significantly occluded. In this dissertation, we address efficient urban semantic scene understanding for autonomous driving under three major perspectives. First, we start with an analysis of the potential of exploiting multiple input modalities, such as depth, motion, or object detectors, for semantic labeling as these cues are typically available in autonomous vehicles. Our goal is to integrate such data holistically throughout all processing stages and we show that our system outperforms comparable baseline methods, which confirms the value of multiple input modalities. Second, we aim to leverage modern deep learning methods requiring large amounts of supervised training data for street scene understanding. Therefore, we introduce Cityscapes, the first large-scale dataset and benchmark for urban scene understanding in terms of pixel- and instance-level semantic labeling. Based on this work, we compare various deep learning methods in terms of their performance on inner-city scenarios facing the challenges introduced above. Leveraging these insights, we combine suitable methods to obtain a real-time capable neural network for pixel-level semantic labeling with high classification accuracy. Third, we combine our previous results and aim for an integration of depth data from stereo vision and semantic information from deep learning methods by means of the Stixel World (Pfeiffer and Franke, 2011). To this end, we reformulate the Stixel World as a graphical model that provides a clear formalism, based on which we extend the formulation to multiple input modalities. We obtain a compact representation of the environment at real-time speeds that carries semantic as well as 3D information

Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

LiDAR-based Semantic Labeling : Automotive 3D Scene Understanding

Mobile Roboter und autonome Fahrzeuge verwenden verschiedene Sensormodalitäten zur Erkennung und Interpretation ihrer Umgebung. Neben Kameras und RaDAR Sensoren repräsentieren LiDAR Sensoren eine zentrale Komponente für moderne Methoden der Umgebungswahrnehmung. Zusätzlich zu einer präzisen Distanzmessung dieser Sensoren, ist ein umfangreiches semantisches Szeneverständnis notwendig, um ein effizientes und sicheres Agieren autonomer Systeme zu ermöglichen. In dieser Arbeit wird das neu entwickelte LiLaNet, eine echtzeitfähige, neuronale Netzarchitektur zur semantischen, punktweisen Klassifikation von LiDAR Punktwolken, vorgestellt. Hierfür finden die Ansätze der 2D Bildverarbeitung Verwendung, indem die 3D LiDAR Punktwolke als 2D zylindrisches Bild dargestellt wird. Dadurch werden Ergebnisse moderner Ansätze zur LiDAR-basierten, punktweisen Klassifikation übertroffen, was an unterschiedlichen Datensätzen demonstriert wird. Zur Entwicklung von Ansätzen des maschinellen Lernens, wie sie in dieser Arbeit verwendet werden, spielen umfangreiche Datensätze eine elementare Rolle. Aus diesem Grund werden zwei Datensätze auf Basis von modernen LiDAR Sensoren erzeugt. Durch das in dieser Arbeit entwickelte automatische Verfahren zur Datensatzgenerierung auf Basis von mehreren Sensormodalitäten, speziell der Kamera und des LiDAR Sensors, werden Kosten und Zeit der typischerweise manuellen Datensatzgenerierung reduziert. Zusätzlich wird eine multimodale Datenkompression vorgestellt, welche ein Kompressionsverfahren der Stereokamera auf den LiDAR Sensor überträgt. Dies führt zu einer Reduktion der LiDAR Daten bei gleichzeitigem Erhalt der zugrundeliegenden semantischen und geometrischen Information. Daraus resultiert eine erhöhte Echtzeitfähigkeit nachgelagerter Algorithmen autonomer Systeme. Außerdem werden zwei Erweiterungen zum vorgestellten Verfahren der semantischen Klassifikation umrissen. Zum einen wird die Sensorabhängigkeit durch Einführung des PiLaNets, einer neuen 3D Netzarchitektur, reduziert indem die LiDAR Punktwolke im 3D kartesischen Raum belassen wird, um die eher sensorabhängige 2D zylindrische Projektion zu ersetzen. Zum anderen wird die Unsicherheit neuronaler Netze implizit modelliert, indem eine Klassenhierarchie in den Trainingsprozess integriert wird. Insgesamt stellt diese Arbeit neuartige, performante Ansätze des 3D LiDAR-basierten, semantischen Szeneverstehens vor, welche zu einer Verbesserung der Leistung, Zuverlässigkeit und Sicherheit zukünftiger mobile Roboter und autonomer Fahrzeuge beitragen