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Keywords
In deep learning, in order to improve learning performance, preprocessing and ingenuity to combine a plurality of discriminators are performed. It can be inferred that it has elements exceeding the set of learning. Therefore, a configuration to combine multiple recognition elements with low loss will be studied. The advance category classification method is expected to narrow the scope of learning in the next stage. Combining elements specialized for FalsePositive/FalseNegative removal after the positive/negative determination is considered to be effective if the accuracy of the subsequent stage is high. We conducted a license plate recognition experiment by combining these and achieved the best performance for Caltech data.
Deep Learning-Based Multi-scale Multi-object Detection and Classification for Autonomous Driving
(2019)
Autonomous driving vehicles need to perceive their immediate environment in order to detect other traffic participants such as vehicles or pedestrians. Vision based functionality using camera images have been widely investigated because of the low sensor price and the detailed information they provide. Conventional computer vision techniques are based on hand-engineered features. Due to the very complex environmental conditions this limited feature representations fail to uniquely identify a specific object. Thanks to the rapid development of processing power (especially GPUs), advanced software frameworks and the availability of large image datasets, Convolutional Neural Networks (CNN) have distinguished themselves by scoring the best on populthis information, the boundingar object detection benchmarks in the research community. Using deep architectures of CNN with many layers, they are able to extract both low-level and high-level features from images by skipping the feature design procedures of conventional computer vision approaches. In this work, an end-to-end learning pipeline for multi-object detection based on one existing CNN architecture, namely Single Shot MultiBox Detector (SSD) [1], with real-time capability, is first reviewed. The SSD detector predicts the object’s position based on feature maps of different resolution together with a default set of bounding boxes. Using the SSD architecture as a starting point, this work focuses on training a single CNN to achieve high detection accuracy for vehicles and pedestrians computed in real time. Since vehicles and pedestrians have different sizes, shapes and poses, independent NNs are normally trained to perform the two detection tasks. It is thus very challenging to train one NN to learn the multi-scale detection ability. The contribution of this work can be summarized as follows:
A detailed investigation on different public datasets (e.g., KITTI [2], Caltech [3] and Udacity [4] datasets). The datasets provide annotated images from real world traffic scenarios containing objects of vehicles and pedestrians.
A data augmentation and weighting scheme is proposed to tackle the problem of class imbalance in the datasets to enable the training for both classes in a balanced manner.
Specific default bounding box design for small objects and further data augmentation techniques to balance the number of objects in different scales.
Extended SSD+ and SSD2 architectures are proposed in order to improve the detection performance and keeping the computational requirements low.
In the early phase of new vehicle system developments, it is crucial to fully define and optimize working system and functional architectures. Architecture definition and validation in turn requires a quick and accurate evaluation of a system‟s overall performance. Modeling and simulating a complete vehicle system, however, is complex and in many cases was either technically not achievable or simply has been omitted within the development process. It is the utmost challenge in system modeling and simulation to realistically reflect interaction of various electrical, mechanical, thermal, and software elements as attributed to individual system modules and their relations. State-of-the-art tools meanwhile bear this capability. In this paper we present an approach how they may effectively and efficiently be incorporated into a car system development process. To accomplish this target, we „virtualize‟ all system entities while defining and reflecting all relevant system aspects. Our proposed development flow allows simulating, evaluating, and validating complete vehicle systems and their behavior. The proposed flow will sustainably change car system development processes.
Trajectory Modelling for Autonomous Driving: Investigating the Artificial Potential Field Method
(2024)
Although the focus of autonomous driving is on maximizing safety and efficiency, comfort and familiarity will play a key role in the adoption of autonomous driving. Therefore, it is important to develop algorithms that can mimic human driving skills and adapt to individual driving styles. The potential field method (PFM) is an obstacle avoidance algorithm for autonomous driving that uses a repulsive potential field, as a environment model, to navigate the vehicle to the lowest risk potential. In this paper, the PFM is used in a overtake scenario at high speed, to test the impact of using prediction when calculating the ideal yaw rate. Analysis is done on how the potential field can be used for lane keeping while following a car and then for overtaking it. A driving simulator is used to record human driving data and compare it with automated driving using a PFM as is proposed by [3], with modifications to enable future prediction.
The following paper points out the key role of IT in the future of car development. At the moment a fundamental change in the structure of automotive IT organizations can be observed. The fact that software update cycle in automotive, about 1 year, in comparison with Apple, Google or Tesla is too much. The entertainment industry is constantly proceeding ahead much faster than the automotive industry. On top of this, new emerging platforms like Apple CarPlay and Android Auto are providing the look and the feel of a mobile phone regarding the control of the car. The vehicle itself is getting more and more as an “ultimate mobile application or app”. This shows the need of speeding up the Time-to-Market of new innovations in automotive industry.
The structure of IT departments has to support these process. No wonder that CIOs of car manufacturers are looking for new structures in their IT departments that enable faster cycle update for automotive applications taking in consideration safety and security requirements.
This only represents a particular interest, as for Apple and Google, we can see that Google has already a fleet of 23 self-driving cars in place which has already autonomously driven more than one million miles with only 12 accidents on public roads and Apple is said to work under the project name "Titan" on its own electric car.
Another important aspect is the software running in the car itself, e.g. the software that “fuses” data from sensors into a comprehensible form: objects have to be accurately located in the environment model of the socalled ego vehicle as a basis for decisions making either by the driver himself or even by the software that can determine within a fraction of a second what the car is going to do. High definition maps also play a very important role in enabling autonomous driving, being developed and maintained by companies such as Nokia HERE, with accuracy of only a few centimeters are thought to be of strategic importance for Advanced Driver Assistance Systems and Self Driving Cars.
“We’re the engine room of the system,” says Mr. Ristevski, vice president of reality capture and processing for former Nokia’s mapping unit named HERE. To be independent from Apple and Google maps and with that from possible competitors, it is said to be the main reason why the German premium car manufacturer Audi, BMW and Daimler bought the online map service for about € 2.5 bn. This is only the first step in the restructuring of the automotive industry.
The goal of the presented work is to develop an automotive radar sensor behavioral model to outline closed loop interaction of driver and vehicle in a synthetic environment. For this interaction open simulation interface (OSI) and functional mock-up interface (FMI) standards are used. This paper describes the architecture overview, working concept of FMI, OSI and different radar functions like radar channel and digital signal processing (DSP) in detail. The description of the scenario for the verification of radar behavioral model results and development of highly automated driving (HAD) functions for the closed loop simulation is elaborated in this paper.
Der allgemeine technologische Fortschritt, insbesondere in der Halbleitertechnik, beschert allen Industriezweigen bisher ungeahnte Entwicklungsschübe, die ganz allgemein auch auf die Software-Entwicklung ausstrahlen und sich somit auch auf die Software-Entwicklungsmethoden auswirken. Die Entwicklungsmethoden für Steuergeräte-Software, befinden sich daher in dem Übergang von manueller C-Code Programmierung hin zu einem graphischen Entwurf. Diese Entwurfsmöglichkeiten bringen viele Vorteile mit sich, wie z.B. Verringerung der Komplexität, Verkürzung der Software-Entwicklungszeit, Verbesserung der Software-Qualität und Automatisierung des Software-Produktionsprozesses.
Allerdings wirft dieser Abstraktionsschritt eine wesentliche Frage auf: Wie lässt sich überprüfen und sicherstellen, dass die eingesetzten Software-Entwicklungswerkezeuge für die Transformation vom graphischen Modell zum generierten C-Code und darüber hinaus zum Objektcode fehlerfrei arbeiten? Typische Methoden, um das notwendige Vertrauen in diese Transformationen zu erhalten sind: Betriebsbewährtheit, Audit des Entwicklungsprozesses des Werkzeuges sowie das Validieren der Entwicklungswerkzeuge. Insbesondere beim Validieren der Entwicklungswerkzeuge fehlte ein Leitfaden wie ein solcher Validierungsprozess aussehen könnte und in welchem Umfang - Stichwort Testtiefe - ein Entwicklungswerkzeug zu testen wäre.
In diesem Beitrag wird gezeigt, wie diese Lücke methodisch geschlossen werden konnte: Die Anforderungen an einem Validierungsprozess wurden ganz allgemein für Entwicklungswerkzeuge hergeleitet und entsprechende Validierungssuite zur Qualifizierung von C-Codegeneratoren und Target-Compilern umgesetzt und angewendet. Die Ergebnisse fließen in die entsprechenden Entwicklungsprozesse in Form von Modellierungsrichtlinien ein. Dieses Vorgehen setzt damit den Stand der Technik.
Die Herleitungsbasis für die Anforderungen an eine Validierungssuite bildeten die Anforderungen an Software-Entwicklungswerkzeuge (Das Entwicklungswerkzeug soll die "Eigenschaft X" haben). Dazu wurden einschlägige Normen und Standards, wie DIN EN 61508, RTCA DO-178B, IEC 60880, PTB-Softwareprüfstelle und MOD EDF Std 00-55, sorgfältig analysiert und entsprechend kategorisiert. Dieser Anforderungskatalog wurde dann in einen in sich stimmigen Anforderungskatalog für eine Validierungssuite (Die Validierungssuite soll validieren, dass das Entwicklungswerkzeug die "Eigenschaft X" hat) umformuliert und veröffentlicht und können beim TÜV Nord bezogen werden.
Der Validierungsprozess ist zweistufig vorgesehen. In einem ersten Schrott wird ein Gutachten für die prinzipielle Eignung der Modellierungssprache eingeholt. Diese Vorprüfung der Eingabeelemente eines Entwicklungswerkzeuges dient dazu, das Entwicklungswerkzeug hinsichtlich all jener Anforderungen zu prüfen, deren Erfüllung von einer weitgehend automatisierten Validierungssuite nicht geprüft werden können. Auf diese Weise kann auch frühzeitig sichergestellt werden, ob grundsätzliche Abweichungen zu den Anforderungen einer Qualifizierung eines Entwicklungswerkzeuges im Wege stehen und ermöglicht somit eine frühzeitige Behebung. Die sich daran anschließende Hauptprüfung des Entwicklungswerkzeuges durch eine Validierungssuite prüft das Entwicklungswerkzeug ausführungsbasiert um die notwendige Testtiefe zu erreichen.
Im Ausblick wird diskutiert, ob und wie die Anforderungen zur prinzipiellen Eignung einer Modellierungssprache beim Entwurf neuer Modellierungssprachen für sicherheitsrelevante Entwicklungsprojekte an dem Beispiel der Modellierungssprache Modelica wegweisend sein können.