CONSIDERING SAFETY AND SECURITY IN AV FUNCTIONS

Autonomous vehicles (AVs) are coming to our streets. Due to the presence of highly complex software systems in AVs, a new hazard analysis technique is needed to meet stringent safety standards. Also, safety and security are inter-dependent and inter-related aspects of AV. They are focused on shielding the vehicles from deliberate attacks (security issue) as well as accidental failures (safety concern), that might lead to loss of lives and injuries to the occupants. So, the current research work has two key components: functional safety and cybersecurity of the autonomous systems. For the safety analysis, we have applied System Theoretic Process Analysis (STPA), which is built on Systems Theoretic Accident Modeling and Processes (STAMP). STAMP is a powerful tool that can identify, define, analyze, and mitigate hazards from the earliest conceptual stage of development to the operation of a system. Applying STPA to autonomous vehicles demonstrates STPA's applicability to preliminary hazard analysis, alternative available, developmental tests, organizational design, and functional design of each unique safety operation. This thesis describes the STPA process used to generate system design requirements for an Autonomous Emergency Braking (AEB) system using a top-down analysis approach for the system safety. The research makes the following contributions to practicing STPA for safety and security: 1. It describes the incorporation of safety and security analysis in one process and discusses the benefits of this; 2. It provides an improved, structural approach for scenario analysis, concentrating on safety and security; 3. It demonstrates the utility of STPA for gap analysis of existing designs in the automotive domain; 4. It provides lessons learned throughout the process of applying STPA and STPA-Sec. Controlling a physical process is associated with dependability requirements in a cyber-physical system (CPS). Cyberattacks can lead to the dependability requirements not being in the acceptable range. Thus, monitoring of the cyber-physical system becomes inevitable for the detection of the deviations in the system from normal operation. One of the main issues is understanding the rationale behind these variations in a reliable manner. Understanding the reason for the variation is crucial in the execution of accurate and time-based control resolution, for mitigating the cyberattacks as well as other reasons of reduced dependability. Currently, we are using evidential networks to solve the reliability issue. In the present work, we are presenting a cyber-physical system analysis where the evidential networks are used for the detection of attacks. The results obtained from the STPA analysis, which provides the technical safety requirements, can be combined with the EN analysis, which can be used efficiently to detect the quality of the used sensor to justify whether the CPS is suitable for the safe and secure design

An Inference-based Prognostic Framework for Health Management of Automotive Systems

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids

Topics in Machining with Industrial Robot Manipulators and Optimal Motion Control

Two main topics are considered in this thesis: Machining with industrial robot manipulators and optimal motion control of robots and vehicles. The motivation for research on the first subject is the need for flexible and accurate production processes employing industrial robots as their main component. The challenge to overcome here is to achieve high-accuracy machining solutions, in spite of the strong process forces required for the task. Because of the process forces, the nonlinear dynamics of the manipulator, such as the joint compliance and backlash, may significantly degrade the achieved machining accuracy of the manufactured part. In this thesis, a macro/micro-manipulator configuration is considered to the purpose of increasing the milling accuracy. In particular, a model-based control architecture is developed for control of the macro/micro-manipulator setup. The considered approach is validated by experimental results from extensive milling experiments in aluminium and steel. Related to the problem of high-accuracy milling is the topic of robot modeling. To this purpose, two different approaches are considered; modeling of the quasi-static joint dynamics and dynamic compliance modeling. The first problem is approached by an identification method for determining the joint stiffness and backlash. The second problem is approached by using gray-box identification based on subspace-identification methods. Both identification algorithms are evaluated experimentally. Finally, online state estimation is considered as a means to determine the workspace position and orientation of the robot tool. Kalman Filters and Rao-Blackwellized Particle Filters are employed to the purpose of sensor fusion of internal robot measurements and measurements from an inertial measurement unit for estimation of the desired states. The approaches considered are fully implemented and evaluated on experimental data. The second part of the thesis discusses optimal motion control applied to robot manipulators and road vehicles. A control architecture for online control of a robot manipulator in high-performance path tracking is developed, and the architecture is evaluated in extensive simulations. The main characteristic of the control strategy is that it combines coordinated feedback control along both the tangential and transversal directions of the path; this separation is achieved in the framework of natural coordinates. One motivation for research on optimal control of road vehicles in time-critical maneuvers is the desire to develop improved vehicle-safety systems. In this thesis, a method for solving optimal maneuvering problems using nonlinear optimization is discussed. More specifically, vehicle and tire modeling and the optimization formulations required to get useful solutions to these problems are investigated. The considered method is evaluated on different combinations of chassis and tire models, in maneuvers under different road conditions, and for investigation of optimal maneuvers in systems for electronic stability control. The obtained optimization results in simulations are evaluated and compared

A DATA-DRIVEN APPROACH TO SUPPORTING USERS’ ADAPTATION TO SMART IN-VEHICLE SYSTEMS

The utilization of data to understand user behavior and support user needs began to develop in areas such as internet services, smartphone apps development, and the gaming industry. This bloom of data-driven services and applications forced OEMs to consider possible solutions for better in-vehicle connectivity. However, digital transformation in the automotive sector presents numerous challenges. One of those challenges is identifying and establishing the relevant user-related data that will cover current and future needs to help the automotive industry cope with the digital transformation pace. At the same time, this development should not be sporadic, without a clear purpose or vision of how newly-generated data can support engineers to create better systems for drivers. The important issue is to learn how to extract the knowledge from the immense data we possess, and to understand the extent to which this data can be used.Another challenge is the lack of established approaches towards vehicle data utilization for user-related studies. This area is relatively new to the automotive industry. Despite the positive examples from other fields that demonstrate the potential for data-driven context-aware applications, automotive practices still have gaps in capturing the driving context and driver behavior. This lack of user-related data can partially be explained by the multitasking activities that the driver performs while driving the car and the higher complexity of the automotive context compared to other domains. Thus, more research is needed to explore the capacity of vehicle data to support users in different tasks.Considering all the interrelations between the driver and in-vehicle system in the defined context of use helps to obtain more comprehensive information and better understand how the system under evaluation can be improved to meet driver needs. Tracking driver behavior with the help of vehicle data may provide developers with quick and reliable user feedback on how drivers are using the system. Compared to vehicle data, the driver’s feedback is often incomplete and perception-based since the driver cannot always correlate his behavior to complex processes of vehicle performance or clearly remember the context conditions. Thus, this research aims to demonstrate the ability of vehicle data to support product design and evaluation processes with data-driven automated user insights. This research does not disregard the driver’s qualitative input as unimportant but provides insights into how to better combine quantitative and qualitative methods for more effective results.According to the aim, the research focuses on three main aspects:•\ua0\ua0\ua0\ua0\ua0 Identifying the extent to which vehicle data can contribute to driver behavior understanding.\ua0 •\ua0\ua0\ua0\ua0\ua0 Expanding the concepts for vehicle data utilization to support drivers.•\ua0\ua0\ua0\ua0\ua0 Developing the methodology for a more effective combination of quantitative (vehicle data-based) and qualitative (based on users’ feedback) studies. Additionally, special consideration is given to describing the drawbacks and limitations, to enhance future data-driven applications

An object-oriented modelling method for evolving the hybrid vehicle design space in a systems engineering environment

A combination of environmental awareness, consumer demands and pressure from legislators has led automotive manufacturers to seek for more environmentally friendly alternatives while still meeting the quality, performance and price demands of their customers. This has led to many complex powertrain designs being developed in order to produce vehicles with reduced carbon emissions. In particular, within the last decade most of the major automotive manufactures have either developed or announced plans to develop one or more hybrid vehicle models. This means that to be competitive and o er the best HEV solutions to customers, manufacturers have to assess a multitude of complex design choices in the most e cient way possible. Even though the automotive industry is adept at dealing with the many complexities of modern vehicle development; the magnitude of design choices, the cross coupling of multiple domains, the evolving technologies and the relative lack of experience with respect to conventional vehicle development compounds the complexities within the HEV design space. In order to meet the needs of e cient and exible HEV powertrain modelling within this design space, a parallel is drawn with the development of complex software systems. This parallel is both from a programmatic viewpoint where object-oriented techniques can be used for physical model development with new equation oriented modelling environments, and from a systems methodology perspective where the development approach encourages incremental development in order to minimize risk. This Thesis proposes a modelling method that makes use of these new tools to apply OOM principles to the design and development of HEV powertrain models. Furthermore, it is argued that together with an appropriate systems engineering approach within which the model development activities will occur, the proposed method can provide a more exible and manageable manner of exploring the HEV design space.The exibility of the modelling method is shown by means of two separate case studies, where a hierarchical library of extendable and replaceable models is developed in order to model the di erent powertrains. Ultimately the proposed method leads to an intuitive manner of developing a complex system model through abstraction and incremental development of the abstracted subsystems. Having said this, the correct management of such an e ort within the automotive industry is key for ensuring the reusability of models through enforced procedures for structuring, maintaining, controlling, documenting and protecting the model development. Further, in order to integrate the new methodology into the existing systems and practices it is imperative to develop an e cient means of sharing information between all stakeholders involved. In this respect it is proposed that together with an overall systems modelling activity for tracking stakeholder involvement and providing a central point for sharing data, CAE methods can be employed in order to automate the integration of data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

Quantified vehicles: data, services, ecosystems

Advancing digitalization has shown the potential of so-called Quantified Vehicles for gathering valuable sensor data about the vehicle itself and its environment. Consequently, (vehicle) Data has become an important resource, which can pave the way to (Data-driven) Services. The (Data-driven Service) Ecosystem of actors that collaborate to ultimately generate services, has only shaped up in recent years. This cumulative dissertation summarizes the author's contributions and includes a synopsis as well as 14 peer-reviewed publications, which contribute to answer the three research questions.Die Digitalisierung hat das Potenzial für Quantified Vehicles aufgezeigt, um Sensordaten über das Fahrzeug selbst und seine Umgebung zu sammeln. Folglich sind (Fahrzeug-)Daten zu einer wichtigen Ressource der Automobilindustrie geworden, da sie auch (datengetriebene) Services ermöglichen. Es bilden sich Ökosysteme von Akteuren, die zusammenarbeiten, um letztlich Services zu generieren. Diese kumulative Dissertation fasst die Beiträge des Autors zusammen und enthält eine Synopsis sowie 14 begutachtete Veröffentlichungen, die zur Beantwortung der drei Forschungsfragen beitragen