The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.

Modern automobiles and particularly those with off-road lineage possess subsystems that can be configured to better negotiate certain terrain types. Different terrain classes amount to different adherence (or surface grip) and compressibility properties that impact vehicle ma-noeuvrability and should therefore incur a tailored throttle response, suspension stiffness and so on. This thesis explores prospective terrain recognition for an anticipating terrain response driver assistance system. Recognition of terrain and road terrain is cast as a semantic segmen-tation task whereby forward driving images or point clouds are pre-segmented into atomic units and subsequently classified. Terrain classes are typically of amorphous spatial extent con-taining homogenous or granularly repetitive patterns. For this reason, colour and texture ap-pearance is the saliency of choice for monocular vision. In this work, colour, texture and sur-face saliency of atomic units are obtained with a bag-of-features approach. Five terrain classes are considered, namely grass, dirt, gravel, shrubs and tarmac. Since colour can be ambiguous among terrain classes such as dirt and gravel, several texture flavours are explored with scalar and structured output learning in a bid to devise an appropriate visual terrain saliency and predictor combination. Texture variants are obtained using local binary patters (LBP), filter responses (or textons) and dense key-point descriptors with daisy. Learning algorithms tested include support vector machine (SVM), random forest (RF) and logistic regression (LR) as scalar predictors while a conditional random field (CRF) is used for structured output learning. The latter encourages smooth labelling by incorporating the prior knowledge that neighbouring segments with similar saliency are likely segments of the same class. Once a suitable texture representation is devised the attention is shifted from monocular vision to stereo vision. Sur-face saliency from reconstructed point clouds can be used to enhance terrain recognition. Pre-vious superpixels span corresponding supervoxels in real world coordinates and two surface saliency variants are proposed and tested with all predictors: one using the height coordinates of point clouds and the other using fast point feature histograms (FPFH). Upon realisation that road recognition and terrain recognition can be assumed as equivalent problems in urban en-vironments, the top most accurate models consisting of CRFs are augmented with composi-tional high order pattern potentials (CHOPP). This leads to models that are able to strike a good balance between smooth local labelling and global road shape. For urban environments the label set is restricted to road and non-road (or equivalently tarmac and non-tarmac). Ex-periments are conducted using a proprietary terrain dataset and a public road evaluation da-taset.

A segurança no tráfego de carros é um assunto em foco nos dias de hoje e, dentro dele, podem-se citar os sistemas de auxílio ao motorista que vêm sendo desenvolvidos com a finalidade de reduzir o grande número de fatalidades em acidentes de trânsito. Tais sistemas de auxílio buscam mitigar falhas humanas como falta de atenção e imprudência. Visto isso, o projeto SENA, desenvolvido pelo Laboratório de Robótica Móvel da Escola de Engenharia de São Carlos, busca contribuir com a evolução dessa assistência ao motorista. O presente trabalho realiza um estudo sobre uma técnica de inteligência artificial chamada de Redes Bayesianas. Essa técnica merece atenção em virtude de sua capacidade de tratar dados incertos em forma de probabilidades. A rede desenvolvida por esse trabalho utiliza, como dados de entrada, os classificadores em desenvolvimento no projeto SENA e tem como resposta um comportamento que o veículo deve executar, por um ser humano ou por um planejador de trajetórias. Em função da alta dimensionalidade do problema abordado, foram realizados dois experimentos em ambiente simulado de duas situações distintas. A primeira, um teste de frenagem próximo a um ponto de intersecção e a segunda, um cenário de entroncamento. Os testes feitos com a rede indicam que classificadores pouco discriminantes deixam o sistema mais propenso a erros e que erros na localização do ego-veículo afetam mais o sistema se comparado a erros na localização dos outros veículos. Os experimentos realizados mostram a necessidade de um sistema de tempo real e um hardware mais adequado para tratar as informações mais rapidamente / The safety of cars in traffic scenarios is being addressed on the past few years. One of its topics is the Advanced Driver-Assistance Systems which have been developed to reduce the fatality numbers of traffic accidents. These systems try to decrease human failures, such as imprudence and lack of attention while driving. For these reasons, the SENA project, in progress on the Mobile Robotics Laboratory at the Sao Carlos School of Engineering (EESC), aims to contribute for the evolution of these assistance systems. This work studies an artificial intelligence technique called Bayesian Networks. It deserves our attention due to its capability of handling uncertainties with probability distributions. The network developed in this Masters Thesis has, as input, the result of the classifiers used on SENA project and has, as output, a behavior which has to be performed by the vehicle with a driver or autonomously by the means of a path planner. Due to the high dimensionality of this issue, two different tests have been carried out. The first one was a braking experiment near a intersection point and the other one was a T-junction scenario. The tests made indicate that weak classifiers leaves the system more instable and error-prone and localization errors of the ego-vehicle have a stronger effect than just localization errors of other traffic participants. The experiments have shown that there is a necessity for a real-time system and a hardware more suitable to deal quickly with the information

This diploma thesis focuses on the issue of seniors in transportation and examines the typical causes of traffic accidents caused by the participation of persons of this age category. The theoretical part of the thesis explains the demographic development of the society with regard to the population’s mobility and describes the personality of the person in the transport environment from the traffic psychology point of view focusing on seniors. The empirical part of the thesis focuses on the analysis of traffic accidents with the participation of seniors, defining the causes. It sets out the practical tools that can be used to objectively assess the driver's ability to drive in terms of driver's age and the possibility of reducing the number of traffic accidents caused by the elderly.

Motivation - Steigende Produkt- und Prozessanforderungen bei gleichzeitig verkürzten Entwicklungszeiten - Verstärkter Einsatz von Finite-Elemente-Analysen (FEA) in der Produktentwicklung notwendig - Erforderliches Expertenwissen jedoch hauptsächlich auf erfahrenen Berechnungsingenieure konzentriert - Konsturktionsbgleitende FEA bisher zu selten durchgeführt oder zu wenig aussagekräftig

Objective: State-of-the-art collision avoidance and collision mitigation systems predict the behavior of pedestrians based on trivial models that assume a constant acceleration or velocity. New sources of sensor information—for example, smart devices such as smartphones, tablets, smartwatches, etc.—can support enhanced pedestrian behavior models. The objective of this article is the development and implementation of a V2Xpedestrian collision avoidance system that uses new information sources. Methods: A literature review of existing state-of-the-art pedestrian collision avoidance systems, pedestrian behavior models in advanced driver assistance systems (ADAS), and traffic simulations is conducted together with an analysis of existing studies on typical pedestrian patterns in traffic. Based on this analysis, possible parameters for predicting pedestrian behavior were investigated. The results led to new requirements from which a concept was developed and implemented. Results: The analysis of typical pedestrian behavior patterns in traffic situations showed the complexity of predicting pedestrian behavior. Requirements for an improved behavior prediction were derived. A concept for a V2X collision avoidance system, based on a cost function that predicts pedestrian near future presence, and its implementation is presented. The concept presented considers several challenges such as information privacy, inaccuracies of the localization, and inaccuracies of the prediction. Conclusion: A concept for an enhanced V2X pedestrian collision avoidance system was developed and introduced. The concept uses new information sources such as smart devices to improve the prediction of the pedestrian's presence in the near future and considers challenges that come along with the usage of these information sources.

Autonomous vehicles is going to be a part of future transport of goods and people, but to make them usable in unpredictable situations presented in real traffic, there is need for backup systems for manual vehicle control. Teleoperation, where a driver controls the vehicle remotely, has been proposed as a backup system for this purpose. This technique is highly dependent on stable and large wireless network bandwidth to transmit high resolution video from the vehicle to the driver station. Reduction in network bandwidth, resulting in a reduced level of detail in the video stream, could lead to a higher risk of driver error. This thesis is a two part investigation. One part looking into whether lower resolution and increased lossy compression of video at the operator station affects driver performance and safety of operation during teleoperation. The second part covers implementation of two vision-based driver assistance systems, one which detects and highlights vehicles and pedestrians in front of the vehicle, and one which detects and highlights lane markings. A driving test was performed at an asphalt track with white markings for track boundaries, with different levels of video quality presented to the driver. Reducing video quality did have a negative effect on lap time and increased the number of times the track boundary was crossed. The test was performed with a small group of drivers, so the results can only be interpreted as an indication toward that video quality can negatively affect driver performance. The vision-based driver assistance systems for detection and marking of pedestrians was tested by showing a test group pre-recorded video shot in traffic, and them reacting when they saw a pedestrian about to cross the road. The results of a one-way analysis of variance, shows that video quality significantly affect reaction times, with p = 0.02181 at significance level α = 0.05. A two-way analysis of variance was also conducted, accounting for video quality, the use of a driver assistance system marking pedestrians, and the interaction between these two. The results point to that marking pedestrians in very low quality video does help reduce reaction times, but the results are not significant at significance level α = 0.05.

Introduction: We describe the use of telepathology in countries with restricted resources using two diagnosis assistance systems (Isabel and Memem7) in addition to the diagnoses made by experts in pathology via the iPath-Network. Methods: A total of 156 cases, largely from Afghanistan, were analysed; 18 cases had to be excluded because of poor image quality. Results: Of the remaining 138 cases (100%), a responsible physician provided a tentative diagnosis for 61.6% of them.With a diagnosis from a consultant pathologist, it was then possible to make a definite diagnosis in 84.8% of cases on the basis of images taken from hematoxylin and eosin staining sections alone. The use of the diagnosis assistance systems resulted in an ordered list of differential diagnoses in 82.6% (IsabelHealth) and in 74.6% (Memem7) of cases, respectively. Adding morphological terminology reduced the list of possible diagnoses to 52.2% (72 cases, Memem7), but improved their quality. Discussion: In summary, diagnosis assistance systems are promising approaches to provide physicians in countries with restricted resources with lists of probable differential diagnoses, thus increasing the plausibility of the diagnosis of the consultant pathologist.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18689号 / 工博第3967号 / 新制||工||1611(附属図書館) / 31622 / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 椹木 哲夫, 教授 西脇 眞二, 教授 松原 厚 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Over the last years, bicycles have been addressed in newly developed driver assistance systems for passenger cars on a voluntary basis, and beginning with the blind spot assist systems, this tendency has been picked up by vehicle regulations and systems are made mandatory. This paper intends to give a detailed summary of which vehicle regulations are currently addressing bicycles, when they come into force and if they will be mandatory in the EU. Also, the performance of already available active safety systems for bicycles (not covered by regulatory requirements) and their technological potential will be included.