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Various technological challenges still prevail when it comes to the development and validation process of advanced driver-assistance systems and autonomous functions. This dissertation aims to introduce new approaches and methods that still adhere to the process flow of the V-cycle, and to address some of the existing development challenges. To satisfy these approaches,
a physical sensor model for an advanced driver assistance camera
has been created, and in the context of the proposed modeling approach, pertinent levels of abstraction and relevant optical and image sensor effects are identified. Additionally, applied concepts and employed methods for the camera model parametrization are presented as well. Moreover, this dissertation illustrates the effectiveness of hybrid-development and test strategies. For the development part, the usage of a camera model in training image-based neural network algorithms is illustrated. Besides,
concerning the test part, several classical computer vision algorithms and image-based neural network algorithms are tested and evaluated in a dedicated but generic/modular framework. Finally, co-simulation frameworks are presented for the integration and coupling process of various software components (sensor models, highly automated functions and simulation
software) with standardized interfaces like the functional mockup interface and the open simulation interface.
In the Automotive industry and especially in the ADAS domain, functions like “Vehicle Detection”, “Lane Detection” undergo a very costly and time-consuming validation process before their final deployment in the vehicle.
After the completion of the development process, image based detection algorithms usually rely on huge data sets of previously recorded data for performance testing and validation. Though vision data sets like “KITTI Vision Benchmark Dataset” and others are currently available for public use, there still lies numerous requirements that need
to be satisfied and steps that need to be followed in order to pave the way for a proper and meaningful use of the recorded data sets in the scope of image based function testing and validation. Using the publicly available recorded data may be in some cases a good starting point but as we all know sooner or later we will need a more
customized/personalized recorded data sets that capture more precise and detailed specifications like the camera’s technical specifications or even its mounting position in the car. Furthermore, depending on the image based function under investigation, recorded data should also reflect certain driving scenarios in specific environmental conditions (rain, snow, fog, at sun rise, at daytime, at night …) or specific driving parameters like, speed,acceleration, grip, car
orientation, position in lane, etc. that that may be too hard to safety due to safety, financial restrictions or even time
limitations.
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.
Development and Simulation of a Test Environment for Vehicle Dynamics, Virtual Test Track Layout
(2017)
This paper presents an overview of a hybrid test strategy for ADAS functions testing and validation. The hybrid test strategy represents a combination of real and virtual validation procedure providing a substitute to elaborate real tests in the development process. ADAS sensor – front view camera – modeling approaches are presented where the causes of relevant optical aberrations and their effects on the acquired images are investigated. In this context, distortion, blur and vignetting models are created and demonstrated. Additionally, a toolchain, a method for camera simulation and testbed setups are defined and presented as enablers for hybrid test strategies.
In this paper, we describe a new approach for synthetic image augmentation and its advantages in training Deep Neural Networks (DNNs) for object classification and localization. To address the need for a significant amount of data when training DNNs, for image-based ADAS functions, our method relies on virtually generated scenarios augmented via a physics-based camera model. The camera model implements various optical effects on ideal-synthetic images. For the scope of this paper, we illustrate the performance differences associated with the vignetting effect when training DNNs with and without image augmentation. We show that training on images altered by our camera vignetting model yield to a better performance than using ideal-synthetic images, additionally we illustrate the relationship between the network's performance results and the implemented effect (vignetting in this case). For a start, our results open the possibility for using camera models for training neural networks on synthetic data and pave the way toward further investigations on significant optical and image sensor effects to be modeled/implemented for performance enhancement during the training process. The approach is conducted and evaluated by training a DNN for car detection using the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) and Virtual KITTI (VKITTI) datasets.