Durch das I-FAS der TU Chemnitz wurde im Rahmen des AKTIV-Projektes eine Probandenstudie zur Akzeptanz von Systemausprägungen einer Aktiven Gefahrenbremsung (AGB) bei PKW durchgeführt. Unter Verwendung eines stehenden Hindernisses wurden sechs Systemausprägungen verglichen, die von den AGB-Partnern in zwei Versuchsträger implementiert wurden. Die sechs Systemausprägungen werden nahezu identisch bewertet, solange Probanden keine Vergleichsmöglichkeit zu anderen Systemausprägungen haben. Wenn es zu einem Fahrereingriff kommt, ist der Eingriffszeitpunkt des Fahrers unabhängig von der gefahrenen Systemausprägung.

abstract: Driving is already a complex task that demands a varying level of cognitive and physical load. With the advancement in technology, the car has become a place for media consumption, a communications center and an interconnected workplace. The number of features in a car has also increased. As a result, the user interaction inside the car has become overcrowded and more complex. This has increased the amount of distraction while driving and has also increased the number of accidents due to distracted driving. This thesis focuses on the critical analysis of today’s in-car environment covering two main aspects, Multi Modal Interaction (MMI), and Advanced Driver Assistance Systems (ADAS), to minimize the distraction. It also provides deep market research on future trends in the smart car technology. After careful analysis, it was observed that an infotainment screen cluttered with lots of small icons, a center stack with a plethora of small buttons and a poor Voice Recognition (VR) results in high cognitive load, and these are the reasons for the increased driver distraction. Though the VR has become a standard technology, the current state of technology is focused on features oriented design and a sales driven approach. Most of the automotive manufacturers are focusing on making the VR better but attaining perfection in VR is not the answer as there are inherent challenges and limitations in respect to the in-car environment and cognitive load. Accordingly, the research proposed a novel in-car interaction design solution: Multi-Modal Interaction (MMI). The MMI is a new term when used in the context of vehicles, but it is widely used in human-human interaction. The approach offers a non-intrusive alternative to the driver to interact with the features in the car. With the focus on user-centered design, the MMI and ADAS can potentially help to reduce the distraction. To support the discussion, an experiment was conducted to benchmark a minimalist UI design. An engineering based method was used to test and measure distraction of four different UIs with varying numbers of icons and screen sizes. Lastly, in order to compete with the market, the basic features that are provided by all the other competitors cannot be eliminated, but the hard work can be done to improve the HCaI and to make driving safer. / Dissertation/Thesis / Date collected about reaction time in the experiment_Excel / Masters Thesis Computer Science 2015

abstract: The environmental impact of the fossil fuels has increased tremendously in the last decade. This impact is one of the most contributing factors of global warming. This research aims to reduce the amount of fuel consumed by vehicles through optimizing the control scheme for the future route information. Taking advantage of more degrees of freedom available within PHEV, HEV, and FCHEV “energy management” allows more margin to maximize efficiency in the propulsion systems. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevations are obtained by use of Geographic Information System (GIS) maps to optimize the controller. The optimization is then reflected on the powertrain of the vehicle.The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade to prepare the vehicle for minimizing energy consumption during an uphill and potential energy harvesting during a downhill. The control algorithm will predict future energy/power requirements of the vehicle and optimize the performance by instructing the power split between the internal combustion engine (ICE) and the electric-drive system. Allowing for more efficient operation and higher performance of the PHEV, and HEV. Implementation of different strategies, such as MPC and Dynamic Programming (DP), is considered for optimizing energy management systems. These strategies are utilized to have a low processing time. This approach allows the optimization to be integrated with ADAS applications, using current technology for implementable real time applications. The Thesis presents multiple control strategies designed, implemented, and tested using real-world road elevation data from three different routes. Initial simulation based results show significant energy savings. The savings range between 11.84% and 25.5% for both Rule Based (RB) and DP strategies on the real world tested routes. Future work will take advantage of vehicle connectivity and ADAS systems to utilize Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), traffic information, and sensor fusion to further optimize the PHEV and HEV toward more energy efficient operation. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016

The aim of this diploma thesis is the research of building polygons for the testing of autonomous vehicles, from the point of view of road technology and also designing aspects. In the thesis are mentioned 9 most important world test polygons and their description of design parameters. There are described particular stages of automation from foreign organizations which are concerned with research and development in the automotive industry. In addition, there are described basic advanced driver assistance systems and connectivity between vehicles and infrastructure. Conclusion also contains the assessment of existing aerodrome test areas for autonomous vehicles.

abstract: Object detection is an interesting computer vision area that is concerned with the detection of object instances belonging to specific classes of interest as well as the localization of these instances in images and/or videos. Object detection serves as a vital module in many computer vision based applications. This work focuses on the development of object detection methods that exhibit increased robustness to varying illuminations and image quality. In this work, two methods for robust object detection are presented. In the context of varying illumination, this work focuses on robust generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS) under varying illumination conditions. The highlight of the first method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). It is first shown that the angular distortion in the Inverse Perspective Mapping (IPM) domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. This information is used to generate object proposals. A novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction is also proposed. In the context of image quality, this work focuses on robust multiple-class object detection using deep neural networks for images with varying quality. The use of Generative Adversarial Networks (GANs) is proposed in a novel generative framework to generate features that provide robustness for object detection on reduced quality images. The proposed GAN-based Detection of Objects (GAN-DO) framework is not restricted to any particular architecture and can be generalized to several deep neural network (DNN) based architectures. The resulting deep neural network maintains the exact architecture as the selected baseline model without adding to the model parameter complexity or inference speed. Performance results provided using GAN-DO on object detection datasets establish an improved robustness to varying image quality and a higher object detection and classification accuracy compared to the existing approaches. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

Navigation systems are one of the most commonly found electronic gadgets in modern vehicles nowadays. Alongside navigation units this technology is made readily available to individuals in everyday devices such as a mobile phone. Digital maps which come preloaded on these devices accommodate within them an extensive dataset of spatial information from around the globe which aids the driver in achieving a well guided driving experience. Apart from being essential for navigation this sensor information backs up other vehicular applications in making intelligent decisions. The quality of this information delivered is in direct relation to the underlying dataset used to produce these maps. Since we live in a highly dynamic environment with constantly changing geography, an effort is necessary to keep these maps updated with the most up to date information as frequently as possible. The digital map of interest in this study is OpenStreetMap, the underlying data of which is a combination of donated as well as crowdsourced information from the last 10 years. This extensive dataset helps in building of a detailed digital map of the world using well defined cartographic techniques. The information within OpenStreetMap is currently enhanced by a large group of volunteers who willing use donated satellite imagery, uploaded GPS tracks, field surveys etc. to correct and collect necessary data for a region of interest. Though this method helps in improving and increasing the quality and quantity of the OpenStreetMap dataset, it is very time consuming and requires a great deal of human effort. Through this thesis an effort is made to automatically enrich this dataset by preprocessing crowdsourced sensor data collected from the navigation system and driver assistance systems (Traffic Sign Recognition system and a Lane Detection System) of a driving vehicle. The kind of data that is algorithmically derived includes the calculation of the curvature of the underlying road, correction of speed limit values for individual road segments being driven and the identification of change in the geometry of existing roads due to closure of old ones or addition of new ones in the Nuremberg region of Bavaria, Germany. Except for a small percentage of speed limit information on roads segments, other information is currently not available in the OpenStreetMap database for use in safety and comfort related applications. The navigation system has the ability to deliver geographical data in form of GPS coordinates at a certain frequency. This set of GPS coordinates can grouped together to form a GPS track visualizing the actual path traversed by a driving vehicle. A large number of such GPS tracks repeatedly collected from different vehicles driving in a region of interest gives all GPS points which lie on a particular road. These points, after outlier elimination methods are used as a dataset to scientifically determine the underlying curvature of the road with the aid of curve fitting techniques. Additional information received from the lane detection system helps identify curves on a road for which the curvature must be calculated. The fusion of information from these sources helps to achieve curvature results with high accuracy. Traffic sign recognition system helps detect traffic signs while driving, the fusion of this data with geographical information from the navigation system at the instance of detection helps determine road segments for which the recognized speed limit values are valid. This thesis successfully demonstrates a method to automatically enrich OpenStreetMap data by crowdsourcing raw sensor data from multiple vehicles equipped with driver assistance systems. All OpenStreetMap attributes were 100% updated into the database and the results have proven the effectiveness our system architecture. The positive results obtained in combination with minimal errors promise a better future for assisted driving.

The demand for Advanced Driver Assistance System (ADAS) applications is increasing day by day and their development requires efficient prototyping and real time testing. ADTF (Automotive Data and Time Triggered Framework) is a software tool from Elektrobit which is used for Development, Validation and Visualization of Vision based applications, mainly for ADAS and Autonomous driving. With the help of ADTF tool, Image or Video data can be recorded and visualized and also the testing of data can be processed both on-line and off-line. The development of ADAS applications needs image and video processing and the algorithm has to be highly efficient and must satisfy Real-time requirements. The main objective of this research would be to integrate OpenCV library with ADTF cross platform. OpenCV libraries provide efficient image processing algorithms which can be used with ADTF for quick benchmarking and testing. An ADTF filter framework has been developed where the OpenCV algorithms can be directly used and the testing of the framework is carried out with .DAT and image files with a modular approach. CMake is also explained in this thesis to build the system with ease of use. The ADTF filters are developed in Microsoft Visual Studio 2010 in C++ and OpenMP API are used for Parallel programming approach.

Over the past decades, congestion and emission problems has increased remarkablywhich escalates the demands on vehicles. The advancements withinthe eld of information and communication systems gives the opportunity todeal with the aforementioned problems. The concept of platooning shows tobe an attractive way of reducing both congestion and emissions by having ashort inter-vehicle spacing. The ndings in studies show that fuel reductionpotentials of 5-20 % are viable as a result of the lowered air drag by drivingin platoon. This thesis investigates the state of the art within the areaof intelligent transport systems (ITS) along with advanced driver assistancesystems (ADAS). Furthermore, the prosecuted work results in a proposedcontrol design for a longitudinal control in a platoon of vehicles. The platoonconsists of two homogeneous radio controlled vehicles (RCV) which aremodelled by taking advantage of system identication methods. The identi-ed plant models are implemented into a Simulink model where the controlsystem is developed. Moreover, the developed control system is implementedinto a real-time demonstrator for experimental evaluation. The results showsthat the modelled dynamics corresponds reasonably well with the real dynamicsof the system. The developed control system proves to work well andagree with the expectations of its performance obtained from simulations.The performance of the proposed controller has been evaluated by means ofsimulations and real experiments. The resulting control system consists ofPID controllers for both speed and spacing control. / Under de senaste decennierna har mangden trakstockningar och problemmed utslapp okat - darmed aven kraven pa vara fordon. Samtidigt skaparframstegen inom informations- och kommunikationssystem mojligheter foratt hantera ovannamnda problem. Kolonnkorning, eller platooning har visatsig vara en eektiv metod for att minska saval trakstockningar som utslappsom en foljd av kortare avstand mellan fordon. Resultat fran studier visarhur en branslereduktion runt 5-20 % ar mojlig till foljd av det sankta luftmotstandet vid kolonnkorning. Avhandlingen undersoker teknikens standpunktinom intelligenta transportsystem (ITS) tillsammans med avancerade drivhjalpsystem(ADAS). Vidare resulterar arbetet i ett forslag till regleringsdesignfor en longitudinell kontroll i en kolonn av fordon. Kolonnen bestar av tvahomogena radiostyrda fordon (RCV) som modelleras genom att utnyttjametoder for systemidentiering. De identierade systemmodellerna implementerasi en Simulink-modell dar styrsystemet utvecklas. Dessutom implementerasdet utvecklade styrsystemet i en realtids-demonstration for experimentellutvardering. Resultaten visar att den modellerade dynamikenstammer bra overens med systemets verkliga dynamik. Det utvecklade styrsystemetvisar sig fungera bra och overensstammer med forvantningarna pa dessprestanda som erhallits genom simuleringar. Den foreslagna regulatorns prestandahar utvarderats med hjalp av simuleringar och verkliga experiment.Det resulterande styrsystemet bestar av PID regulatorer for bade hastighetsochavstandskontroll.

This thesis consists of two projects focusing on how 5G can be used to make vehicles safer. The first project focuses on conceptualizing near-future use cases of how Advanced Driver Assistance Systems (ADAS) can be enhanced through 5G technology. Four concepts were developed in collaboration with various industry partners. These concepts were successfully demonstrated in a proof-of-concept at the 5G Automotive Association (5GAA) “The 5G Path of Vehicle-to-Everything Communication: From Local to Global” conference in Turin, Italy. This proof-of-concept was the world’s first demonstration of such a system. The second project focuses on a futuristic use case, namely remote operation of semi-autonomous vehicles (sAVs). As part of this work, it was explored if augmented reality (AR) can be used to warn remote operators of dangerous events. It was explored if such augmentations can be used to compensate during critical events. These events are defined as occurrences in which the network conditions are suboptimal, and information provided to the operator is limited. To evaluate this, a simulator environment was developed that uses eye- tracking technology to study the impact of such scenarios through user studies. The simulator establishes an extendable platform for future work. Through experiments, it was found that AR can be beneficial in spotting danger. However, it can also be used to directly affect the scanning patterns at which the operator views the scene and directly affect their visual scanning behavior. / Denna avhandling består av två projekt med fokus på hur 5G kan användas för att göra fordon säkrare. Det första projektet fokuserar på att konceptualisera användningsfall i närmaste framtid av hur Advanced Driver Assistance Systems (ADAS) kan förbättras genom 5G-teknik. Fyra koncept utvecklades i samarbete med olika branschpartner. Dessa koncept demonstrerade i ett proof-of- concept på 5G Automotive Association (5GAA) “5G Path of Vehicle to to Everything Communication: From Local to Global” -konferensen i Turin, Italien. Detta bevis-of-concept var världens första demonstration av ett sådant system. Det andra projektet fokuserar på ett långt futuristiskt användningsfall, nämligen fjärrstyrning av semi-autonoma fordon (sAVs). Som en del av detta arbete undersöktes det om augmented reality (AR) kan användas för att varna fjärroperatörer om farliga händelser. Det undersöktes om sådana förstärkningar kan användas för att kompensera under kritiska händelser. Dessa händelser definieras som händelser där nätverksförhållandena är suboptimala och information som tillhandahålls till operatören är begränsad. För att utvärdera detta utvecklades en simulatormiljö som använder ögonspårningsteknologi för att studera effekterna av sådana scenarier genom en användarstudie. Simulatorn bildar en utdragbar plattform för framtida arbete. Genom experiment fann man att AR kan vara fördelaktigt när det gäller att upptäcka fara. Men det kan också användas för att direkt påverka skanningsmönstret där operatören tittar på scenen och direkt påverka deras visuella skanningsbeteende.

Models based on micro-simulation of traffic flows have proven to be useful tools in the study of various traffic systems. Today, there is a wealth of traffic microsimulation models developed for freeway and urban street networks. The road mileage is however in many countries dominated by rural highways. Hence, there is a need for rural road traffic simulation models capable of assessing the performance of such road environments. This thesis introduces a versatile traffic micro-simulation model for the rural roads of today and of the future. The developed model system considers all common types of rural roads including effects of intersections and roundabouts on the main road traffic. The model is calibrated and validated through a simulation study comparing a two-lane highway to rural road designs with separated oncoming traffic lanes. A good general agreement between the simulation results and the field data is established. The interest in road safety and the environmental impact of traffic is growing. Recent research has indicated that traffic simulation can be of use in these areas as well as in traditional capacity and level-of-service studies. In the road safety area more attention is turning towards active safety improving countermeasures designed to improve road safety by reducing the number of driver errors and the accident risks. One important example is Advanced Driver Assistance Systems (ADAS). The potential to use traffic simulation to evaluate the road safety effects of ADAS is investigated in the last part of this thesis. A car-following model for simulation of traffic including ADAS-equipped vehicles is proposed and the developed simulation framework is used to study important properties of a traffic simulation model to be used for safety evaluation of ADAS. Driver behavior for ADAS-equipped vehicles has usually not been considered in simulation studies including ADAS-equipped vehicles. The work in this thesis does however indicate that modeling of the behavior of drivers in ADAS-equipped vehicles is essential for reliable conclusions on the road safety effects of ADAS. / <p>Report code: LiU-Tek-Lic-2005:60.</p>