Abordagem de Anotações para o Suporte da Gestão Energética de Software em Modelos AMALTHEA

The automotive industry is continuously introducing innovative software features to provide more efficient, safe, and comfortable solutions. Despite the several benefits to the consumer, the evolution of automotive software is also reflected in several challenges, presenting a growing complexity that hinders its development and integration. The adoption of standards and appropriate development methods becomes essential to meet the requirements of the industry. Furthermore, the expansion of automotive software systems is also driving a considerable growth in the number of electronic components installed in a vehicle, which has a significant impact on the electric energy consumption. Thus, the focus on non-functional energy requirements has become increasingly important. This work presents a study focused on the evolution of automotive software considering the development standards, methodologies, as well as approaches for energy requirements management. We propose an automatic and self-contained approach for the support of energy properties management, adopting the model-based open-source framework AMALTHEA. From the analysis of execution or simulation traces, the energy consumption estimation is provided at a fine-grained level and annotated in AMALTHEA models. Thus, we enable the energy analysis and management of the system throughout the entire lifecycle. Additionally, this solution is in line with the AUTOSAR Adaptive standard, allowing the development of energy management strategies for automatic, dynamic, and adaptive systems.A indústria automotiva encontra-se constantemente a introduzir funcionalidades inovadoras através de software, para oferecer soluções mais eficientes, seguras e confortáveis. Apesar dos diversos benefícios para o consumidor, a evolução do software automóvel também se reflete em diversos desafios, apresentando uma crescente complexidade que dificulta o seu desenvolvimento e integração. Desta forma, a adoção de normas e metodologias adequadas para o seu desenvolvimento torna-se essencial para cumprir os requisitos do setor. Adicionalmente, esta expansão das funcionalidades suportadas por software é fonte de um aumento considerável do número de componentes eletrónicos instalados em automóveis. Consequentemente, existe um impacto significativo no consumo de energia elétrica dos sistemas automóveis, sendo cada vez mais relevante o foco nos requisitos não-funcionais deste domínio. Este trabalho apresenta um estudo focado na evolução do software automotivo tendo em conta os padrões e metodologias de desenvolvimento desta área, bem como abordagens para a gestão de requisitos de energia. Através da adoção da ferramenta AMALTHEA, uma plataforma open-source de desenvolvimento baseado em modelos, é proposta uma abordagem automática e independente para a análise de propriedades energéticas. A partir da análise de traços de execução ou de simulação, é produzida uma estimativa pormenorizada do consumo de energia, sendo esta anotada em modelos AMALTHEA. Desta forma, torna-se possível a análise e gestão energética ao longo de todo o ciclo de vida do sistema. Salienta-se que a solução se encontra alinhada com a norma AUTOSAR Adaptive, permitindo o desenvolvimento de estratégias para a gestão energética de sistemas automáticos, dinâmicos e adaptativos

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

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

Visualization of Crash Channel Assignments in a Tabular Form

Passive safety systems try to lessen the effects of an accident. Airbags are a passive safety feature. They are designed to protect occupants of a vehicle during a crash. These systems have to be configured correctly in order to deploy airbags at the right time in case of a collision. Airbag application tools are used to simulate and interpret crashes. Some factors influence when an airbag should deploy. Based on different parameters, the logic for firing airbags is also different. Under every circumstance, an airbag has to be deployed at the right time in order to prevent injuries and fatalities. During the process of simulation, the data which is simulated is written to a database. During interpretation, this data is extracted from the database. Then, the required information can be analyzed and interpreted for further use. This data contains crash related information. For example, the type of crash, crash code and crash channel assignments. For every crash present in the airbag project, crash channels are assigned to the sensors. Each sensor present has a crash channel assigned to it. This is called the crash channel assignment. An airbag application tool is developed to show the crash channel assignments. This tool should handle the information extraction, and visualization of crash channel assignments. The final output should be in a tabular format, which includes user specific customizations

Dagstuhl-Workshop MBEES:

modellbasierte automobile Steuergeräteentwicklun

Scheduling for Mixed-criticality Hypervisor Systems in the Automotive Domain

This thesis focuses on scheduling for hypervisor systems in the automotive domain. Current practices are primarily implementation-agnostic or are limited by lack of visibility during the execution of partitions. The tasks executed within the partitions are classified as event-triggered or time-triggered. A scheduling model is developed using a pair of a deferrable server and a periodic server per partition to provide low latency for event-triggered tasks and maximising utilisation. The developed approach enforces temporal isolation between partitions and ensures that time-triggered tasks do not suffer from starvation. The scheduling model was extended to support three criticality levels with two degraded modes. The first degraded mode provides the partitions with additional capacity by trading-off low latency of event-driven tasks with lower overheads and utilisation. Both models were evaluated by forming a case study using real ECU application code. A second case study was formed inspired from the Olympus Attitude and Orbital Control System (AOCS) to further evaluate the proposed mixed-criticality model. To conclude, the contributions of this thesis are addressed with respect to the research hypothesis and possible avenues for future work are identified

Applying Hypervisor-Based Fault Tolerance Techniques to Safety-Critical Embedded Systems

This document details the work conducted through the development of this thesis, and it is structured as follows: • Chapter 1, Introduction, has briefly presented the motivation, objectives, and contributions of this thesis. • Chapter 2, Fundamentals, exposes a series of concepts that are necessary to correctly understand the information presented in the rest of the thesis, such as the concepts of virtualization, hypervisors, or software-based fault tolerance. In addition, this chapter includes an exhaustive review and comparison between the different hypervisors used in scientific studies dealing with safety-critical systems, and a brief review of some works that try to improve fault tolerance in the hypervisor itself, an area of research that is outside the scope of this work, but that complements the mechanism presented and could be established as a line of future work. • Chapter 3, Problem Statement and Related Work, explains the main reasons why the concept of Hypervisor-Based Fault Tolerance was born and reviews the main articles and research papers on the subject. This review includes both papers related to safety-critical embedded systems (such as the research carried out in this thesis) and papers related to cloud servers and cluster computing that, although not directly applicable to embedded systems, may raise useful concepts that make our solution more complete or allow us to establish future lines of work. • Chapter 4, Proposed Solution, begins with a brief comparison of the work presented in Chapter 3 to establish the requirements that our solution must meet in order to be as complete and innovative as possible. It then sets out the architecture of the proposed solution and explains in detail the two main elements of the solution: the Voter and the Health Monitoring partition. • Chapter 5, Prototype, explains in detail the prototyping of the proposed solution, including the choice of the hypervisor, the processing board, and the critical functionality to be redundant. With respect to the voter, it includes prototypes for both the software version (the voter is implemented in a virtual machine) and the hardware version (the voter is implemented as IP cores on the FPGA). • Chapter 6, Evaluation, includes the evaluation of the prototype developed in Chapter 5. As a preliminary step and given that there is no evidence in this regard, an exercise is carried out to measure the overhead involved in using the XtratuM hypervisor versus not using it. Subsequently, qualitative tests are carried out to check that Health Monitoring is working as expected and a fault injection campaign is carried out to check the error detection and correction rate of our solution. Finally, a comparison is made between the performance of the hardware and software versions of Voter. • Chapter 7, Conclusions and Future Work, is dedicated to collect the conclusions obtained and the contributions made during the research (in the form of articles in journals, conferences and contributions to projects and proposals in the industry). In addition, it establishes some lines of future work that could complete and extend the research carried out during this doctoral thesis.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Katzalin Olcoz Herrero.- Secretario: Félix García Carballeira.- Vocal: Santiago Rodríguez de la Fuent

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