05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Effiziente Synthese konsistenter Graphen und ihre Anwendung in der Lokalisierung akustischer Quellen(2015) Kreißig, Martin; Yang, Bin (Prof. Dr.-Ing.)In dieser Arbeit wird das Problem der simultanen, akustischen Mehrquellenlokalisierung in echobehafteten Umgebungen genauer betrachtet und daran beispielhaft die Anwendungsmöglichkeit der Synthese konsistenter Graphen gezeigt und analysiert. Dafür werden die Grundlagen der akustischen Lokalisierung eingeführt und unterschiedliche Ansätze vorgestellt. Im Besonderen werden die laufzeitdifferenzbasierten Lokalisierungsverfahren betrachtet, die das Problem der uneindeutigen Zuweisung der Laufzeitdifferenzen zu den Quellen haben. Anhand dieses konkreten Anwendungsbeispiels der Lokalisierung wird die Problematik auf ein graphentheoretisches Problem abstrahiert. Deshalb werden zunächst die graphentheoretischen Grundlagen und bereits bekannte Algorithmen, wie die Tiefen- und Breitensuche eingeführt, die beide einen aufspannenden Baum suchen. Der aufspannende Baum ist notwendig, um die Menge der fundamentalen Maschen zu bestimmen. Die Synthese konsistenter Graphen erfolgt durch das Zusammenführen der konsistenten fundamentalen Maschen. Dabei unterscheidet man folgende Vorgehensweisen: die Synthese voll konsistenter Graphen, die jeder Kante des Eingangsgraphen ein konsistentes Kantengewicht zuweisen und die Synthese partiell konsistenter Graphen, die nur eine Teilmenge von Kanten beinhalten. Beide Vorgehensweisen basieren auf den konsistenten fundamentalen Maschen, welche die Nullsummenbedingung erfüllen. Die Synthese voll konsistenter Graphen wird über ein Backtracking-Verfahren realisiert. Die Synthese partiell konsistenter Graphen wird aus dem Kompatibilitäts-Konflikt-Graph abgeleitet, der ein neuer Typus von Graph ist und die drei unterschiedlichen Zustände zwischen den konsistenten fundamentalen Maschen beschreibt: 1) zwei konsistente Maschen haben keine gemeinsame Kanten, 2) zwei konsistente Maschen haben gemeinsame Kanten und identische Kantengewichte (kompatibel) und 3) zwei konsistente Maschen haben gemeinsame Kanten und unterschiedliche Kantengewichte (Konflikt). Um alle möglichen, partiell konsistenten Graphen zu synthetisieren, wird der neue Algorithmus CCGsearch eingeführt und auf Vollständigkeit bewiesen. Die Berechnungskomplexitäten der beiden Syntheseverfahren werden sowohl analytisch hergeleitet als auch durch Simulationen verifiziert.Item Open Access Direction of arrival estimation using a multiple-input-multiple-output radar with applications to automobiles(2017) Rambach, Kilian; Yang, Bin (Prof. Dr.-Ing.)The thesis at hand investigates the direction of arrival (DOA) estimation using a Multiple-Input-Multiple-Output (MIMO) radar system. The application of MIMO radars in automobiles is studied. A MIMO radar consists of several transmitting (Tx) and receiving (Rx) antennas. We focus on a time division multiplexed (TDM) MIMO radar with colocated Tx and Rx antennas. The motivation is the use of a radar as a security system in automotive applications, e.g. to identify a dangerous situation and react automatically. Security systems must be very reliable. Hence, besides a good estimation of the distance and velocity, a high performance in DOA estimation is necessary. This is a demanding task, since only a small number of antennas is used and the radar is limited to a small geometrical size. Compared to the corresponding Single-Input-Multiple-Output (SIMO) radar, a MIMO radar with colocated antennas can achieve a higher accuracy in DOA estimation due to its larger virtual aperture. Therefore it is a promising technique for the use in automobiles. The obtained results of this thesis enable us to find optimal TDM schemes which yield a very high DOA accuracy for targets which are stationary as well as for targets which are moving relative to the radar system. The results are not confined to MIMO radars in automobiles, but can be used in other applications as well.Item Open Access Driver alertness monitoring using steering, lane keeping and eye tracking data under real driving conditions(2020) Friedrichs, Fabian; Yang, Bin (Prof. Dr.-Ing.)Since humans operate trains, vehicles, aircrafts and industrial machinery, fatigue has always been one of the major causes of accidents. Experts assert that sleepiness is among the major causes of severe road accidents. In-vehicle fatigue detection has been a research topic since the early 80’s. Most approaches are based on driving simulator studies, but do not properly work under real driving conditions. The Mercedes-Benz ATTENTION ASSIST is the first highly sophisticated series equipment driver assistance system on the market that detects early signs of fatigue. Seven years of research and development with an unparalleled demand of resources were necessary for its series introduction in 2009 for passenger cars and 2012 for busses. The system analyzes the driving behavior and issues a warning to sleepy drivers. Essentially, this system extracts a single measure (so-called feature), the steering event rate by detecting a characteristic pattern in the steering wheel angle signal. This pattern is principally described by a steering pause followed by a sudden correction. Various challenges had to be tackled for the series-production readiness, such as handling individual driving styles and external influences from the road, traffic and weather. Fuzzy logic, driving style detection, road condition detection, change of driver detection, fixed-point parameter optimization and sensor surveillance were some of the side results from this thesis that were essential for the system’s maturity. Simply issuing warnings to sleepy drivers is faintly "experiencable" nor transparent. Thus, the next version 2.0 of the system was the introduction of the more vivid ATTENTION LEVEL, which is a permanently available bargraph monitoring the current driving performance. The algorithm is another result of this thesis and was introduced 2013 in the new S-Class. Fatigue is very difficult to grasp since a ground truth reference does not exist. Thus, the presented findings about camera-based driver monitoring are included as fatigue reference for algorithm training. Concurrently, the presented results build the basis for eye-monitoring cameras of the future generation of such systems. The driver monitoring camera will also play a key role in "automated driving" since it is necessary to know if the driver looks to the road while the vehicle is driving and if he is alert enough to take back control over the vehicle in complex situations. All these improvements represent major steps towards the paradigm of crash free driving. In order to develop and improve the ATTENTION ASSIST, the central goal of the present work was the development of pattern detection and classification algorithms to detect fatigue from driving sensors. One major approach to achieve a sufficiently high detection rate while maintaining the false alarm rate at a minimum was the incorporation of further patterns with sleepiness-associative ability. Features reported in literature were assessed as well as improved extraction techniques. Various new features were proposed for their applicability under real-road conditions. The mentioned steering pattern detection is the most important feature and was further optimized. Essential series sensor signals, available in most today’s vehicles were considered, such as steering wheel angle, lateral and longitudinal acceleration, yaw rate, wheel rotation rate, acceleration pedal, wheel suspension level, and vehicle operation. Another focus was on the lateral control using camera-based lane data. Under real driving conditions, the effects of sleepiness on the driving performance are very small and severely obscured by external influences such as road condition, curvature, cross-wind, vehicle speed, traffic, steering parameters etc. Furthermore, drivers also have very different individual driving styles. Short-term distraction from vehicle operation also has a big impact on the driving behavior. Proposals are given on how to incorporate such factors. Since lane features require an optional tracking camera, a proposal is made on how to estimate some lane deviation features from only inertial sensory by means of an extended Kalman filter. Every feature is related to a number of parameters and implementation details. A highly accelerated method for parameter optimization of the large amount of data is presented and applied to the most promising features. The alpha-spindle rate from the Electroencephalogram (EEG) and Electrooculogram (EOG) were assessed for their performance under real driving conditions. In contrast to the majority of results in literature, EEG was not observed to contribute any useful information to the fatigue reference (except for two drives with microsleeps). Generally, the subjective self-assessments according to the Karolinska Sleepiness Scale and a three level warning acceptance question were consequently used. Various correlation measures and statistical test were used to assess the correlation of features with the reference. This thesis is based on a database with over 27,000 drives that accumulate to over 1.5 mio km of real-road drives. In addition, various supervised real-road driving studies were conducted that involve advanced fatigue levels. The fusion of features is performed by different classifiers like Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Fair classification results are achieved with ANN and SVM using cross-validation. A selection of the most potential and independent features is given based on automatic SFFS feature selection. Classical machine learning methods are used in order to yield maximal system transparency and since the algorithms are targeted to run in present control units. The potential of using end-to-end deep learning algorithms is discussed. Whereas its application to CAN-signals is problematic, there is a high potential for driver-camera based approaches. Finally, features were implemented in a real-time demonstrator using an own CAN-interface framework. While various findings are already rolled out in ATTENTION ASSIST 1.0, 2.0 and ATTENTION LEVEL, it was shown that further improvements are possible by incorporating a selection of steering- and lane-based features and sophisticated classifiers. The problem can only be solved on a system level considering all topics discussed in this thesis. After decades of research, it must be recognized that the limitations of indirect methods have been reached. Especially in view of emerging automated driving, direct methods like eye-tracking must be considered and have shown the greatest potential.Item Open Access Parameter estimation with additional information(2012) Uhlich, Stefan; Yang, Bin (Prof. Dr.-Ing.)This PhD thesis deals with the problem of estimating unknown parameters from noisy data using additional information. By additional information we denote any domain knowledge that is available to us except the data itself. Such prior domain knowledge very often arises naturally from the estimation problem at hand if the context is taken into account. The motivation to incorporate the prior domain knowledge is twofold: First, it allows us to ensure that the estimate will fulfill physical constraints which are given and second, we can also expect a better estimation performance.Item Open Access Improving automotive radar spectra object classification using deep learning and multi-class uncertainty calibration(2022) Patel, Kanil; Yang, Bin (Prof. Dr.-Ing.)Being a prerequisite for successful automated driving, ameliorating the perception capabilities of vehicles is of paramount importance for reliable and robust scene understanding. Required for decision-making in autonomous vehicles, scene understanding becomes particularly challenging in adverse weather and lighting conditions; situations also often posing challenges for human drivers. Automotive radars can greatly assist sensors currently deployed on vehicles for robust measurements, especially in challenging conditions where other sensors often fail to operate reliably. However, classification using radar sensors is often limited to a few classes (e.g. cars, humans, and stop signs), controlled laboratory settings, and/or simulations. Already offering reliable distance, azimuth and velocity estimates of the objects in the scene, improving radar-based classification greatly expands the usage of radar sensors for tackling multiple driving-related tasks which are often performed by other less robust sensors. This thesis investigates how automated driving perception can be improved using multi-class radar classification by using deep learning algorithms for exploiting object class characteristics captured in the radar spectra. Despite the highly-accurate predictions of deep learning models, such classifiers exhibit severe over-confidence which can lead decision-making systems to false conclusions, with possibly catastrophic consequences - often a matter of life and death for automated driving. Consequently, high-quality, robust, and interpretable uncertainty estimates are indispensable characteristics of any unassailable automated driving system. With the goal of uncertainty estimates for real-time predictive systems, this thesis also aims at tackling the prominent over-confidence of deep learning classification models, which persists for all data modalities. Being an important measure for the quality of uncertainty estimates, this work focuses on the accurate estimation of the calibration of trained classifiers, as well as present novel techniques for improving their calibration. The presented solutions offer high-quality real-time confidence estimates for classification models of all data modalities (e.g. non-radar applications), as well as classifiers which are already trained and used in practise and new training strategies for learning new classifiers. Furthermore, the presented uncertainty calibration algorithms could also be extended to tasks other than classification, for example, regression and segmentation. On a challenging new realistic automated driving radar dataset, the solutions proposed in this thesis show that radar classifiers are able to generalize to novel driving environments, driving patterns, and object instances in realistic static driving scenes. To further replicate realistic encounters of autonomous vehicles, we study the behaviour of the classifiers to spectra corruptions and outlier detection of unknown objects, showing significant performance improvements in safely handling these prevalent encounters through accurate uncertainty estimates. With the proposed generalization and requisite accurate uncertainty estimation techniques, the radar classifiers in this study greatly improve radar-based perception for scene understanding and lay a solid foundation for current sensor fusion techniques to leverage radar measurements for object classification.Item Open Access Rehearsal-based continual learning with deep neural networks for image classification(2024) Wiewel, Felix; Yang, Bin (Prof. Dr.-Ing.)Item Open Access Least-squares based layerwise pruning of Deep Neural Networks(2024) Mauch, Lukas; Yang, Bin (Prof. Dr.-Ing.)Tiefe Neuronale Netze (DNNs) sind derzeit die leistungsstärksten Modelle im Bereich des maschinellen Lernens und lösen erfolgreich viele Aufgaben, wie zum Beispiel Bild- und Spracherkennung, semantische Segmentierung oder Datengenerierung. Aufgrund der inhärent hohen Rechenkomplexität von DNNs wurden schon früh Pruningverfahren angewandt um die Rechenkomplexität von DNNs zu reduzieren und um die Inferenz zu beschleunigen. Pruningverfahren entfernen (prunen) Parameter aus einem trainierten DNN, ohne ihre Leistung dadurch signifikant zu beeinträchtigen. Die dadurch erhaltenen Modelle können auch auf schwachen Rechenplattformen mit hoher Geschwindigkeit ausgewertet werden. In den letzten Jahren wurden Pruningverfahren nicht nur nach dem Training, sondern auch als Bestandteil von modernen Trainingsalgorithmen für DNNs eingesetzt. So wenden zum Beispiel viele speichereffiziente Trainingsalgorithmen oder Architektursuchverfahren pruning schon während des Trainings an, um unwichtige Parameter aus dem DNN zu entfernen. Problematisch ist, dass viele moderne Pruningverfahren auf regularisierten, überwachten Trainingverfahren beruhen und daher selbst sehr rechenaufwändig sind. Solche Pruningverfahren können nicht ohne Weiteres in andere Trainingsalgorithmen eingebettet werden. Es besteht daher ein wachsendes Interesse an Pruningmethoden, die sowohl schnell als auch genau sind. In dieser Arbeit untersuchen wir das layerbasierte Least-Squares (LS) Pruning – ein Framework für das strukturierte Pruning von DNNs. Wir zeigen, dass LS-Pruning eine schnelleund dennoch genaue Methode für die DNN-reduktion ist, die für Zero-Shot oder für die unüberwachte Netzwerkreduktion verwendet werden kann. In experimenten vergleichen wir LS-Pruning mit anderen schnellen Reduktionsmethoden, wie zum Beispiel dem magnitudenbasierten Pruning und der LS-Faktorisierung. Darüber hinaus vergleichen wir LS-Pruning mit überwachten Pruningverfahren.Item Open Access The polar transmitter : analysis and algorithms(2015) Ibrahim, Mohamed; Yang, Bin (Prof. Dr.-Ing.)The polar transmitter architecture is a promising candidate for future mobile communications. It can outperform traditional IQ transmitters in terms of power effciency and space consumption. The massive increase in bandwidth demand makes the design of the polar transmitter a challenging task. Since the polar transmitter incorporates digital signals at RF sampling rates, then signal processing principles and algorithms could be implemented to relax the physical constraints of designing the polar transmitter components. In this thesis, the polar transmitter is analyzed from the architectural point of view. Properties of the polar signals which result from the Cartesian-to-polar conversion will be investigated. Moreover, mathematical models of the polar transmitter components, as well as their distortions, will be introduced. The strict requirements imposed on the polar transmitter components will be relaxed by introducing several novel digital signal processing algorithms. The suitability of the presented algorithms will be evaluated by simulating LTE up-link signals using the polar transmitter.Item Open Access Driver drowsiness monitoring using eye movement features derived from electrooculography(2016) Ebrahim, Parisa; Yang, Bin (Prof. Dr.-Ing.)The increase in vehicle accidents due to the driver drowsiness over the last years highlights the need for developing reliable drowsiness assistant systems by a reference drowsiness measure. Therefore, the thesis at hand is aimed at classifying the driver vigilance state based on eye movements using electrooculography (EOG). In order to give an insight into the states of driving, which lead to critical safety situations, first, driver drowsiness, distraction and different terminologies in this context are described. Afterwards, countermeasures as techniques for keeping a driver awake and consequently preventing car crashes are reviewed. Since countermeasures do not have a long-lasting effect on the driver vigilance, intelligent driver drowsiness detection systems are needed. In the recent past, such systems have been developed on the market, some of which are introduced in this study. As also stated in previous studies, driver state is quantifiable by objective and subjective measures. The objective measures monitor the driver either directly or indirectly. For indirect monitoring of the driver, one uses the driving performance measures such as the lane keeping behavior or steering wheel movements. On the contrary, direct monitoring mainly comprises the driver’s physiological measures such as the brain activities, heart rate and eye movements. In order to assess these objective measures, subjective measures such as self-rating scores are required. This study introduces these measures and discusses the concerns about their interpretation and reliability. The developed drowsiness assistant systems on the market are all based on driving performance measures. These measures presuppose that the vehicle is steered solely by the driver himself. As long as other assistance systems with the concept to keep the vehicle in the middle of the lane are activated, driving performance measures would make wrong decisions about warnings. The reason is what the sensors measure is a combination of the driver’s behavior and the activated assistance system. In fact, the drowsiness warning system cannot determine the contribution of the driver in the driving task. This underscores the need for the direct monitoring of the driver. Previous works have introduced the drop of the alpha spindle rate (ASR) as a drowsiness indicator. This rate is a feature extracted out of the brain activity signals during the direct monitoring the driver. Additionally, ASR was shown to be sensitive to driver distraction, especially a visual one with an counteracting effect. We develop an algorithm based on eye movements to reduce the negative effect of the driver visual distraction on the ASR. This helps to partially improve the association of ASR with the driver drowsiness. Since the focus of this study is on driver eye movements, we introduce the human visual system and describe the idea of what and where to define the visual attention. Further, the structure of the human eye and relevant types of eye movements during driving are defined. We also categorize eye movements into two groups of slow and fast eye movements. We show that blinks, in principle, can belong to both of these groups depending on the driver’s vigilance state. EOG as a tool to measure the driver eye movements allows us to distinguish between drowsiness or distraction-related and driving situation dependent eye movements. Thus, in a pilot study, an experiment under fully controlled conditions is carried out on a proving ground to investigate the relationship between driver eye movements and different real driving scenarios. In this experiment, unwanted head vibrations within EOG signals and the sawtooth pattern (optokinetic nystagmus, OKN) of eyes are realized as situation dependent eye movements. The former occurs due to ground excitation and the latter happens during small radius (50m) curve negotiation. The statistical investigation expresses a significant variation of EOG due to unwanted head vibrations. Moreover, an analytical model is developed to explain the possible relationship of KON and tangent point of the curve. The developed model is validated against the real data on a high curvature track. In order to cover all relevant eye movement patterns during awake and drowsy driving, different experiments are conducted in this work including daytime and nighttime experiments under real road and simulated driving conditions. Based on the measured signals in the experiments, we study different eye movement detection approaches. We, first, investigate the conventional blink detection method based on the median filtering and show its drawback in detecting slow blinks and saccades. Afterwards, an adaptive detection approach is proposed based on the derivative of the EOG signal to simultaneously detect not only the eye blinks, but also other driving-relevant eye movements such as saccades and microsleep events. Moreover, in spite of the fact that drowsiness influences eye movement patterns, the proposed algorithm distinguishes between the often confused driving-related saccades and decreased amplitude blinks of a drowsy driver. The evaluation of results shows that the presented detection algorithm outperforms the common method based on median filtering so that fast eye movements are detected correctly during both awake and drowsy phases. Further, we address the detection of slower eye blinks, which are referred to as typical patterns of the drowsiness, by applying the continuous wavelet transform to EOG signals. In our proposed algorithm, by adjusting parameters of the wavelet transform, fast and slow blinks are detected simultaneously. However, this approach suffers from a larger false detection rate in comparison to the derivative-based method. As a result, for blink detection in this work, a combination of these two methods is applied. To improve the quality of the collected EOG signals, the discrete wavelet transform is benefited to remove noise and drift. For the noise removal, an adaptive thresholding strategy within the discrete wavelet transform is proposed which avoids sacrificing noise removal for saving blink amplitude or vice versa. In previous research, driver eye blink features (blink frequency, duration, etc.) have shown to be correlated to some extent with drowsiness. Hence, within a level of uncertainty they can contribute to driver drowsiness warning systems. In order to improve such systems, we investigate characteristics of detected blinks with respect to their different origins. We observed that in a real road experiment, blinks occur both spontaneously or due to gaze shifts. Gaze shifts between fixed positions, which occurred due to secondary visuomotor task, induced and modulated the occurrence of blinks. Moreover, the direction of the gaze shifts affected the occurrence of such blinks. Based on the eye movements during another experiment in a driving simulator without a secondary task, we found that the amount of gaze shifts (between various positions) is positively correlated with the probability of the blink occurrence. Therefore, we recommend handling gaze shift-induced blinks (e.g. during visual distraction) differently from those occurring spontaneously in drowsiness warning systems that rely solely on the variation of blink frequency as a driver state indicator. After studying dependencies between blink occurrence and gaze shifts, we extract 19 features out of each detected blink event of 43 subjects collected under both simulated and real driving conditions during 67 hours of both daytime and nighttime driving. This corresponds to the largest number of extracted eye blink features and the largest number of subjects among previous studies. We propose two approaches for aggregating features to improve their association with the slowly evolving drowsiness. In the first approach, we solely investigate parts of the collected data which are best correlated with the subjective self-rating score, i.e. Karolinska Sleepiness Scale. In the second approach, however, the entire data set with the maximum amount of information regarding driver drowsiness is scrutinized. For both approaches, the dependency between single features and drowsiness is studied statistically using correlation coefficients. The results show that the drowsiness dependency to features evolves to a larger extent non-linearly rather than linearly. Moreover, we show that for some features, different trends with respect to drowsiness are possible among different subjects. Consequently, we challenge warning systems which rely only on a single feature for their decision strategy and underscore that they are prone to high false alarm rates. In order to study whether a single feature is suitable for predicting safety-critical events, we study the overall variation of the features for all subjects shortly before the occurrence of the first unintentional lane departure and first unintentional microsleep in comparison to the beginning of the drive. Based on statistical tests, before the lane departure, most of the features change significantly. Therefore, we justify the role of blink features for the early driver drowsiness detection. However, this is not valid for the variation of features before the microsleep. We also focus on all 19 blink-based features together as one set. We assess the driver state by artificial neural network, support vector machine and k-nearest neighbors classifiers for both binary and multi-class cases. There, binary classifiers are trained both subject-independent and subject-dependent to address the generalization aspects of the results for unseen data. For the binary driver state prediction (awake vs. drowsy) using blink features, we have attained an average detection rate of 83% for each classifier separately. For 3-class classification (awake vs. medium vs. drowsy), however, the result was only 67%, possibly due to inaccurate self-rated vigilance states. Moreover, the issue of imbalanced data is addressed using classifier-dependent and classifier-independent approaches. We show that for reliable driver state classification, it is crucial to have events of both awake and drowsy phases in the data set in a balanced manner. The reason is that the proposed solutions in previous researches to deal with imbalanced data sets do not generalize the classifiers, but lead to their overfitting. The drawback of driving simulators in comparison to real driving is also discussed and to this end we perform a data reduction approach as a first remedy. As the second approach, we apply our trained classifiers to unseen drowsy data collected under real driving condition to investigate whether the drowsiness in driving simulators is representative of the drowsiness under real road conditions. With an average detection rate of about 68% for all classifiers, we conclude their similarity. Finally, we discuss feature dimension reduction approaches to determine the applicability of extracted features for in-vehicle warning systems. On this account, filter and wrapper approaches are introduced and compared with each other. Our comparison results show that wrapper approaches outperform the filter-based methods.Item Open Access Object-level image segmentation with prior information(2019) Wang, Chunlai; Yang, Bin (Prof. Dr.-Ing.)