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

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

High Performance Multiview Video Coding

Following the standardization of the latest video coding standard High Efficiency Video Coding in 2013, in 2014, multiview extension of HEVC (MV-HEVC) was published and brought significantly better compression performance of around 50% for multiview and 3D videos compared to multiple independent single-view HEVC coding. However, the extremely high computational complexity of MV-HEVC demands significant optimization of the encoder. To tackle this problem, this work investigates the possibilities of using modern parallel computing platforms and tools such as single-instruction-multiple-data (SIMD) instructions, multi-core CPU, massively parallel GPU, and computer cluster to significantly enhance the MVC encoder performance. The aforementioned computing tools have very different computing characteristics and misuse of the tools may result in poor performance improvement and sometimes even reduction. To achieve the best possible encoding performance from modern computing tools, different levels of parallelism inside a typical MVC encoder are identified and analyzed. Novel optimization techniques at various levels of abstraction are proposed, non-aggregation massively parallel motion estimation (ME) and disparity estimation (DE) in prediction unit (PU), fractional and bi-directional ME/DE acceleration through SIMD, quantization parameter (QP)-based early termination for coding tree unit (CTU), optimized resource-scheduled wave-front parallel processing for CTU, and workload balanced, cluster-based multiple-view parallel are proposed. The result shows proposed parallel optimization techniques, with insignificant loss to coding efficiency, significantly improves the execution time performance. This , in turn, proves modern parallel computing platforms, with appropriate platform-specific algorithm design, are valuable tools for improving the performance of computationally intensive applications

Image-Based Scene Analysis for Computer-Assisted Laparoscopic Surgery

This thesis is concerned on image-based scene analysis for computer-assisted laparoscopic surgery. The focus lies on how to extract different types of information from laparoscopic video data. Methods for semantic analysis can be used to determine what instruments and organs are currently visible and where they are located. Quantitative analysis provides numerical information on the size and distances of structures. Workflow analysis uses information from previously seen images to estimate the progression of surgery. To demonstrate that the proposed methods function in real-world scenarios, multiple evaluations on actual laparoscopic image data recorded from surgeries were performed. The proposed methods for semantic and quantitative analysis were successfully evaluated in live phantom and animal studies and also used during a live gastric bypass on a human patient

A Framework for Tumor Localization in Robot-Assisted Minimally Invasive Surgery

Manual palpation of tissue is frequently used in open surgery, e.g., for localization of tumors and buried vessels and for tissue characterization. The overall objective of this work is to explore how tissue palpation can be performed in Robot-Assisted Minimally Invasive Surgery (RAMIS) using laparoscopic instruments conventionally used in RAMIS. This thesis presents a framework where a surgical tool is moved teleoperatively in a manner analogous to the repetitive pressing motion of a finger during manual palpation. We interpret the changes in parameters due to this motion such as the applied force and the resulting indentation depth to accurately determine the variation in tissue stiffness. This approach requires the sensorization of the laparoscopic tool for force sensing. In our work, we have used a da Vinci needle driver which has been sensorized in our lab at CSTAR for force sensing using Fiber Bragg Grating (FBG). A computer vision algorithm has been developed for 3D surgical tool-tip tracking using the da Vinci \u27s stereo endoscope. This enables us to measure changes in surface indentation resulting from pressing the needle driver on the tissue. The proposed palpation framework is based on the hypothesis that the indentation depth is inversely proportional to the tissue stiffness when a constant pressing force is applied. This was validated in a telemanipulated setup using the da Vinci surgical system with a phantom in which artificial tumors were embedded to represent areas of different stiffnesses. The region with high stiffness representing tumor and region with low stiffness representing healthy tissue showed an average indentation depth change of 5.19 mm and 10.09 mm respectively while maintaining a maximum force of 8N during robot-assisted palpation. These indentation depth variations were then distinguished using the k-means clustering algorithm to classify groups of low and high stiffnesses. The results were presented in a colour-coded map. The unique feature of this framework is its use of a conventional laparoscopic tool and minimal re-design of the existing da Vinci surgical setup. Additional work includes a vision-based algorithm for tracking the motion of the tissue surface such as that of the lung resulting from respiratory and cardiac motion. The extracted motion information was analyzed to characterize the lung tissue stiffness based on the lateral strain variations as the surface inflates and deflates

Multi-camera reconstruction and rendering for free-viewpoint video

While virtual environments in interactive entertainment become more and more lifelike and sophisticated, traditional media like television and video have not yet embraced the new possibilities provided by the rapidly advancing processing power. In particular, they remain as non-interactive as ever, and do not allow the viewer to change the camera perspective to his liking. The goal of this work is to advance in this direction, and provide essential ingredients for a free-viewpoint video system, where the viewpoint can be chosen interactively during playback. Knowledge of scene geometry is required to synthesize novel views. Therefore, we describe 3D reconstruction methods for two distinct kinds of camera setups. The first one is depth reconstruction for camera arrays with parallel optical axes, the second one surface reconstruction, in the case that the cameras are distributed around the scene. Another vital part of a 3D video system is the interactive rendering from different viewpoints, which has to perform in real-time. We cover this topic in the last part of this thesis.Während die virtuellen Welten in interaktiven Unterhaltungsmedien immer realitätsnäher werden, machen traditionellere Medien wie Fernsehen und Video von den neuen Möglichkeiten der rasant wachsenden Rechenkapazität bisher kaum Gebrauch. Insbesondere mangelt es ihnen immer noch an Interaktivität, und sie erlauben dem Konsumenten nicht, elementare Parameter wie zum Beispiel die Kameraperspektive seinen Wünschen anzupassen. Ziel dieser Arbeit ist es, die Entwicklung in diese Richtung voranzubringen und essentielle Bausteine für ein Videosystem bereitzustellen, bei dem der Blickpunkt während der Wiedergabe jederzeit völlig frei gewählt werden kann. Um neue Ansichten synthetisieren zu können, ist zunächst Kenntnis von der 3D Geometrie der Szene notwendig. Wir entwickeln daher Rekonstruktionsalgorithmen für zwei verschiedene Anordnungen von Kameras. Falls die Kameras eng beieinanderliegen und parallele optische Achsen haben, können lediglich Tiefenkarten geschätzt werden. Sind die Kameras jedoch im einer Halbkugel um die Szene herum montiert, so rekonstruieren wir sogar echte Oberflächengeometrie. Ein weiterer wichtiger Aspekt ist die interaktive Darstellung der Szene aus neuen Blickwinkeln, die wir im letzten Teil der Arbeit in Angriff nehmen

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Offshore marine visualization

In 85 B.C. a Greek philosopher called Posidonius set sail to answer an age-old question: how deep is the ocean? By lowering a large rock tied to a very long length of rope he determined that the ocean was 2km deep. These line and sinker methods were used until the 1920s when oceanographers developed the first echo sounders that could measure the water's depth by reflecting sound waves off the seafloor. The subsequent increase in sonar depth soundings resulted in oceanologists finally being able to view the alien underwater landscape. Paper printouts and records dominated the industry for decades until the mid 1980s when new digital sonar systems enabled computers to process and render the captured data streams.In the last five years, the offshore industry has been particularly slow to take advantage of the significant advancements made in computer and graphics technologies. Contemporary marine visualization systems still use outdated 2D representations of vessels positioned on digital charts and the potential for using 3D computer graphics for interacting with multidimensional marine data has not been fully investigated.This thesis is concerned with the issues surrounding the visualization of offshore activities and data using interactive 3D computer graphics. It describes the development of a novel 3D marine visualization system and subsequent study of marine visualization techniques through a number of offshore case studies that typify the marine industry. The results of this research demonstrate that presenting the offshore engineer or office based manager with a more intuitive and natural 3D computer generated viewing environment enables complex offshore tasks, activities and procedures to be more readily monitored and understood. The marine visualizations presented in this thesis take advantage of recent advancements in computer graphics technology and our extraordinary ability to interpret 3D data. These visual enhancements have improved offshore staffs' spatial and temporal understanding of marine data resulting in improved planning, decision making and real-time situation awareness of complex offshore data and activities

Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles

Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE