Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

High performance control of a multiple-DOF motion platform for driver seat vibration test in laboratory

Dynamic testing plays an important part in the vehicle seat suspension study. However, a large amount of research work on vibration control of vehicle seat suspension to date has been limited to simulations because the use of a full-size vehicle to test the device is an expensive and dangerous task. In order to decrease the product development time and cost as well as to improve the design quality, in this research, a vibration generation platform is developed for simulating the road induced vehicle vibration in laboratory. Different from existing driving simulation platforms, this research focuses on the vehicle chassis vibration simulation and the control of motion platform to make sure the platform can more accurately generate the actual vehicle vibration movement. A seven degree-of-freedom (DOF) full-vehicle model with varying road inputs is used to simulate the real vehicle vibration. Moreover, because the output vibration data of the vehicle model is all about the absolute heave, pitch and roll velocities of the sprung mass, in order to simulate the vibration in all dimensions, a Stewart multiple-DOF motion platform is designed to generate the required vibration. As a result, the whole vibration simulator becomes a hardware-in-the-loop (HIL) system. The hardware consists of a computer used to calculate the required vibration signals, a Stewart platform used to generate the real movement, and a controller used to control the movement of the platform and implemented by a National Instruments (NI) CompactRIO board. The data, which is from the vehicle model, can be converted into the length of the six legs of the Stewart platform. Therefore, the platform can transfer into the same posture as the real vehicle chassis at that moment. The success of the developed platform is demonstrated by HIL experiments of actuators. As there are six actuators installed in the motion platform, the signals from six encoders are used as the feedback signals for the control of the length of the actuators, and advanced control strategies are developed to control the movement of the platform to make sure the platform can accurately generate the required motion even in heavy load situations. Theoretical study is conducted on how to generate the reasonable vibration signals suitable for vehicle seat vibration tests in different situations and how to develop advanced control strategies for accurate control of the motion platform. Both simulation and experimental studies are conducted to validate the proposed approaches

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

The AutoDrive Challenge: Autonomous Vehicles Education and Training Issues

Automotive companies are focusing significant research and development efforts on autonomous vehicles. As they do so, they recognize the need for a large, well-trained workforce that is equipped to conduct these research and development projects, particularly in light of the projected shortages of STEM professionals in the United States. Some of these companies have found various ways to engage with professional societies and with universities to encourage the development of this workforce, and to promote themselves to STEM students while they are still in school. One such effort is the SAE / GM AutoDrive ChallengeTM, a new collegiate competition organized by SAE International in collaboration with General Motors Corporation. In this competition, eight teams are working to modify a Chevrolet Bolt to meet the requirements of a Level 4 autonomous vehicle (i.e., a vehicle that is totally capable of driving itself within a certain operational domain). Teams were selected for this competition through a proposal process, with one of the requested components of the proposal focusing on existing courses and the development of new courses at the participating university. In this paper, we will discuss the roles of students and faculty advisors at one of the participating schools, address issues related to education and training of students who want to work in the autonomous vehicle industry, and discuss the benefits of the competition to all of its stakeholders. This discussion will include the skills developed by students, the outcomes of the competition, and the value that is being created for the automotive industry. As part of this discussion, we will focus on the close ties that can be forged between the participating universities and the corporate sponsors of the AutoDrive Challenge, as well as the impact on course development at the universit

Virtual prototyping of vehicular electric steering assistance system using co-simulations

Virtual prototyping is a practical necessity in vehicle system development. From desktop simulation to track testing, several simulation approaches, such as co-simulation and hardware-in-loop (HIL) simulation, are used. However, due to interfacing problems, the consistency of testing results may not be ensured. Correspondingly, inherent inaccuracies result from numerical coupling error and non-transparent HIL interface, which involves control tracking error, delay error, and attached hardware and noise effects. This work aims to resolve these problems and provide seamless virtual prototypes for vehicle and electric power-assisted steering (EPAS) system development.The accuracy and stability of explicit parallel co-simulation and HIL simulation are investigated. The imperfect factors propagate in the simulation tools like perturbations, yield inaccuracy, and even instability according to system dynamics. Hence, reducing perturbations (coupling problem) and improving system robustness (architecture problem) are considered.In the coupling problem, a delay compensation method relying on adaptive filters is developed for real-time simulation. A novel co-simulation coupling method on H-infinity synthesis is developed to improve accuracy for a wide frequency range and achieve low computational cost. In the architecture problem, a force(torque)-velocity coupling approach is employed. The application of a force (torque) variable to a component with considerable impedance, e.g., the steering rack (EPAS motor), yields a small loop gain as well as robust co-simulation and HIL simulation. On a given EPAS HIL system, an interface algorithm is developed for virtually shifting the impedance, thus enhancing system robustness.The theoretical findings and formulated methods are tested on generic benchmarks and implemented on a vehicle-EPAS engineering case. In addition to the acceleration of simulation speed, accuracy and robustness are also improved. Consequently, consistent testing results and extended validated ranges of virtual prototypes are obtained