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1	Cooperative automation in automobiles Biester, Lars 29 May 2009 (has links) Das Ziel dieser Dissertation ist die systematische Entwicklung eines weiterführenden Konzeptes zur Fahrer-Fahrzeug Kooperation, dessen Tauglichkeit anhand empirischer Daten evaluiert und im Hinblick auf sein belegbares Potential in Bezug auf bestehende Ansätze bewertet werden soll.Da Annahmen und Prämissen der Mensch-Maschine-Interaktion den Ausgangspunkt bilden, beginnt die dezidierte Auseinandersetzung und begriffliche Differenzierung von Kooperation in eben diesem Kontext und führt folgerichtig zu einer definitorischen Abgrenzung gegenüber existierenden Ansätzen, der Forderung eines spezifischen Rollenverständnisses zur Interaktion sowie der Ableitung konzeptueller Grundbedingungen. Anschließend werden die strukturellen und prozeduralen Merkmale dieser spezifischen Interaktion herausgearbeitet und dazu benutzt, die generellen Attribute von Kooperation zwischen Fahrer und Fahrzeug zu identifizieren. Dafür wurden nachfolgend solche Indikatoren abgeleitet, vermittels derer der unterstellte Gewinn infolge der Kooperation von Fahrer und Fahrzeug kontrolliert und bewertet werden kann.Im Rahmen mehrerer Voruntersuchungen wurden Fahrsituationen identifiziert, die am meisten von einer kooperativen Interaktion zwischen Fahrer und Fahrzeug profitieren würden. Im Ergebnis wurden für die zwei Hauptuntersuchungen das „Überholen auf der Autobahn“ und das „Linksabbiegen auf innerstädtischen Straßen und Landstraßen mit Gegenverkehr“ als Fahrszenarien ausgewählt, die in jeweils einem eigenständigen Experiment mit alternativen Systemvarianten verglichen worden sind. Die Prüfung spezifischer Hypothesen wurde dabei in die prototypische Umgebung eines Fahrsimulators eingebettet. Abschließend werden in dieser Arbeit die Möglichkeiten zur Etablierung und Einbettung dieses Interaktionskonzeptes in den übergreifenden sozio-technischen Kontext aufgezeigt und zukünftige Perspektiven diskutiert. / The aim of this dissertation is to systematically develop a continuative concept of driver-automobile cooperation, to evaluate its suitability on the basis of empirical data, and to value its provable potential in relation to existing approaches.Assumptions and premises regarding the human-machine interaction constitute the starting point of this work. The decisive altercation and notional differentiation of cooperation are explained in just this context, leading logically to a definitional demarcation of existing approaches, the demand of a specific role understanding of the interaction as well as the derivation of conceptual basic conditions. The structural and procedural characteristics of this specific interaction are then elaborated upon and used to identify the general attributes of cooperation between driver and automobile. In the following, such indicators are derived by which the implied profit as a result of cooperation between driver and automobile can be controlled and valued. Within the framework of several preliminary investigations, those driving situations were identified that would profit most from a cooperative interaction between driver and automobile. As a result, the two driving scenarios "Overtaking on Highways" and "Turning Left on Urban and Country Roads with Oncoming Traffic" were utilized in the experiments. Both single scenarios have been compared in independent experiments with regard to alternative system variants. The prove of specific hypotheses was embedded in the prototypical surroundings of a driving simulator. Finally, the possibility of establishing and embedding this interaction concept into the overall socio-technical context will be presented, and future perspectives will be discussed. Vertrauen Akzeptanz Kooperative Automatisierung Funktionsverteilung Fahrerassistenzsystem Fahrsituation Fahrsimulator Überholen auf Autobahnen Linksabbiegen über Gegenverkehr Physiologische Parameter Cooperative Automation Function allocation Acceptance Trust Driver assistance system Driving situation Driving simulator Overtaking on highways Turning left with oncoming traffic physiological parameters 150 Psychologie 11 Psychologie ddc:150
2	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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Cooperative automation in automobiles

Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms