Das steigende Verkehrsaufkommen erfordert hochtechnologische Sicherheitslösungen, um der wachsenden Anzahl an Verkehrstoten entgegenzuwirken. Um zukünftige Fahrzeuge sicher und zuverlässig zu halten, ist eine kontinuierliche Entwicklung neuer Sicherheitsanwendungen obligatorisch. Automatische Notrufsysteme, wie das europäische eCall, sollen Gegenstand des täglichen Lebens von Autofahrern werden. Das Ziel der hier entwickelten Ansätze konzentriert sich auf zwei neuartige Sicherheitsanwendungen. Zum Einen werden Herzschlag und Atembewegung für die Entwicklung eines "Driver State Monitor" (DSM) benötigt. Ein DSM überwacht die Konzentration des Fahrers auf den Verkehr und soll somit dazu beitragen, Unfälle durch Übermüdung zu vermeiden. In diesem Zusammenhang sind Herzschlag und Atmung gute Indikatoren für den Zustand des Fahrers. Ein weiteres Arbeitsgebiet wird in einer Untersuchung der amerikanischen Verkehrssicherheitsbehörde ("National Highway Traffic Safety Administration" - NHTSA) definiert. Diese verzeichnet ein steigendes Risiko für den Tod durch Hitzschlag bei unbeaufsichtigt zurückgelassenen Kindern in geparkten Fahrzeugen an Sommertagen. Um solche Fälle zu vermeiden, evaluiert die NHTSA verschiedene Konzepte zur Erkennung von zurückgelassenen Insassen. Durch die Detektion des menschlichen Tremors kann dieser Bereich abgedeckt werden.

    The increasing traffic volume requires highly technical safety solutions to prevent a raise in the number of traffic deaths. To keep future vehicles safe and reliable a continuous development of new safety devices is obligatory. Automated emergency calls like the European eCall will become subjects of the driver's daily life. The objectives of the developed safety approaches are focused on two upcoming safety devices. Heartbeat and respiration information are needed for the development of a driver state monitor (DSM). The DSM is intended for the observation of the driver's concentration on the actual traffic. It should prevent accidents due to drowsiness and fatigue. In this context the heartbeat and respiration are good indications of the driver's health status. Another field of work is defined by a research of the National Highway Traffic Safety Administration (NHTSA). It records an increasing risk of death by heat stroke for unattended left behind children in parked cars on summer days. To prevent these avoidable deaths the NHTSA evaluates several concepts of left behind occupant recognition devices. This area can be covered by the detection of human tremor signals. Content of the thesis is the development of two new sensing approaches to fulfill future safety needs of the automotive market. The first one is based on high sensitive analogue accelerometers that monitor vibrations occurring at the car chassis. Investigations showed a recognizable signal produced by human beings seated in the parked vehicle. Its origin is medically known as human tremor. The human tremor is an unintentional, rhythmic, oscillating muscle movement which can not be suppressed by the individual itself. The second approach is founded on a re-engineered series product called "passive occupant detection system" model B (PODS-B). It contains a silicon oil-filled bladder mat which is integrated in the front passenger seat. The mat includes a connected pressure sensor to measure the applied loading forces on the seat cushion. An electronic control unit (ECU) uses the weight information for the classification of the seated passenger. In case of a classified child the ECU disarms the airbag to protect the child from injuries caused by the fired airbag in a crash situation. Below the weight signal, significant information of the seated occupant is buried - heartbeat and respiration movement. With the help of developed electronics these physiological parameters are extracted. After the evaluation of both sensing concepts the classification by machine learning techniques is prepared. This involves a feature extraction, normalization and correlation-based selection to gain proper classification spaces for each approach. Additionally a fused space of both sensors is constructed. Subsequently four data mining algorithms (SVM, k-NN, J48, PNN) are evaluated on the given feature bases to determine the best for each task. Finally a conclusion of the thesis is given and further steps up to the first automotive A-samples are mentioned.


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    Title :

    New active safety approaches - Left behind occupant detection and physiological parameter sensing


    Contributors:

    Publication date :

    2010


    Size :

    158 Seiten, 86 Bilder, 24 Tabellen, 84 Quellen



    Type of media :

    Theses


    Type of material :

    Print


    Language :

    English




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