Contributions to road safety: from abstractions and control theory to real solutions, discussion and evaluation

This manuscript aims to describe my career in the transportation domain, putting in evidence my contributions in different levels, as for example thesis advising, teaching, research animation and coordination, projects construction and participation in expert committees, among others, besides my scientific research itself. The goal, besides the HDR diploma itself, is to show very clearly, including to myself, this 'pack' of contributions in order to look for better contributions to the transportation and control communities or to other communities in the future, and also which research directions I will define to work on in the following. I obtained my PhD degree in the Laboratoire des Signaux et Systèmes - L2S 1 in collaboration with MIT, in 2001, having worked in a purely theoretical automatic control topic scarcely known in the literature - the adaptive control of systems with nonlinear parameterization problem. Arriving in 2002 as a permanent researcher to the former LCPC (Laboratoire Central des Ponts et haussées), now called IFSTTAR (Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux), I have been faced to real problems to solve in practice, and faced to the new community of transportation, with a completely different philosophy of work. I have nowadays this double vision - of the very applied transportation domain with concrete problems to be solved that touch the citizen every day, and the vision of a very rich high-level theoretical research in automatic control with powerful tools to solve the real problems, or on the other hand, with control problems that appear because of the need for new tools to solve the real problems. I consider this as an important characteristic for my future contributions. Besides the knowledge in Transportation itself, my eleven years of career in IFSTTAR gave me as well the following new features : 1. From the individual research, I have learned also how to coordinate work (in projects for example, as in the PReVAL sub-project of the European PReVENT project, in which I co-leaded one workpackage, or for research teams, as the control team of LIVIC, coordinated by myself from 2006 to 2009). I have also learned how to animate research (by coordinating research working groups or organizing scientific events and workshops - see for example the working group RSEI and the related scientific event below that I have organized in June 2012) and how to advise students. 2. Besides the double vision I have described above, the experience gave me also the acquisition of a quite multidisciplinary view of the problems in the domain. Firstly, arriving in LIVIC, in the frame of the French consortium ARCOS, I have worked for two years in close cooperation with experts in cognitive sciences (the PsyCoTech group from IRCCyN, Nantes) on designing driving assistance systems to a human driver. After this work, I have continued the collaboration with experts in human sciences within the PReVAL subproject of PReVENT on driving assistance systems evaluation and within the French ANR PARTAGE project, that I have constructed together with the PsyCoTec team of IRCCyN and leaded the IFSTTAR partner for one year. In a dition, through my participation in PReVENT at dirent levels (in two meetings of the Core Group, in PReVAL by co-leading the workpackage 3 on Technical Evaluation of ADAS - ADAS is the shortcut for Advanced Driving Assistance Systems - and in the SAFELANE subproject), I have learned many different aspects of ITS systems. I consider this as an add-on value for my 'pack of knowledge'. 3. What I call "from abstractions to real problems : coming back and forth to solve these real problems" has been matured in my mind, and I am very grateful to my students, with whom I have learned and that helped me in this maturing process. By this sentence, I mean, with a problem to solve in hands, and after building an abstraction, or a simplified view of the problem, and the design of a solution, how to apply it, and to come back again to the theory to change it and to come back to the practice, and so on. This is exactly one of the pillars of the NoE HYCON2, for making interact the theory with the application domains. 4. Considering a problem inserted into the societal context, or inserted within its related context, has been another maturing for myself that I consider very important, notably in the transportation domain, that represents a very complex context containing many different parameters, scenarios and objectives and in addition all the uncertainties linked to the human behavior. I think that it is very important to have a very large view of the context in which the specific problem we are treating is placed. Without this, one cannot say in most of the cases, from my point of view, that the problem is solved. This point will be discussed in Chapter 9.5. 5. Another point that I consider important and where I have been contributing recently is the road mapping work. The acquisition of the multidisciplinary knowledge and a larger view of the domain that I have mentioned in the preceding items, together with my theoretical knowledge in automatic control, allowed myself to start contributing to theroad mapping work in Transportation (through my participation in the imobility forum, in HYCON2 and the in the support action T-Area-SoS on Systems of Systems - all these actions to give advice to the European Commission on the priority areas to be considered in the new Calls, notably in the frame of the H2020 program). I had also the pleasure of opening again books and thesis that I had studied in my PhD work, this time now for advising students in the frame of other very different problems. The very beautiful thesis of Mikael Johansson, Lund University, on piecewise linear systems stability theory is an example. My previous study on switched systems, and the implication of switched Lyapunov functions on stability helped me also in advising my students (Post-Docs, PhD, and M.Sc. students), this time for real applications, with very interesting results blooming up from their work. I realize also that the experience that I have described in the five items above must be put in favor of students since this kind of knowledge cannot be found in the books. Concluding, in these last eleven years, from 2002 to 2013, I could bring to the scientic community and to my students a set of contributions of different kinds. I will try to make clear these contributions for the reader in the next two chapters (written in English and in French). This document is organized in the following way : Part II contains my complete curriculum vitae (in french) where all these contributions will be described in detail. Part III contains then the scientific contributions of the manuscript. What I aim in this chapter is to describe, but further, to analyze them with a distanced look and providing a critical view, announcing perspectives, and placing and discussing the obtained results in the societal context. This is in straight relation with item 4 above. Also, I prefer to adopt, as far as possible, a form comprehensible to the non-automatic control expert, with, as far as possible as well, qualitative explanations and then appropriated references containing the theorems and the definitions corresponding to the qualitative explanations will be provided. In the case it is necessary, they are provided within the text. The Part III is structured in the following chapters. Chapter 8 contains an overview of the global transportation scenario with the associated challenges and a description of the driving assistance systems context. Chapter 9 contains my scientific contributions. These include my research results, my contributions in students advising, in the coordination of research groups, and the collaborative works. It is structured in 3 sections : Section 9.1 introduces what will be the greed for a part of the main contributions, that are described in Sections 9.2 and 9.3. Section 9.1 is also dedicated to showing to the reader how theory and abstractions can be very important for solving real problems. Chapter 9.4 describes other contributions that are the result of collaborative works. A discussion from a multidisciplinary view is provided in Chapter 9.5 based on a survey paper of myself. Chapter 10 will be finally dedicated to the perspectives and the general conclusions. Then last Part contains as annexes a selection of the publications that I consider the most illustrative of my contributions described in Chapter 9. Finally, since the described work is in the intersection of two communities - the transportation and the control theory communities - I decided to write a part of the document dedicated to the non control experts readers. This is Part VI of the document whose aim is to provide some fundamental notions on control theory in a very simple qualitative description whose understanding will help the different readers to understand the contributions

Transition control based on grey, neural states

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Organic electrochemical networks for biocompatible and implantable machine learning: Organic bioelectronic beyond sensing

How can the brain be such a good computer? Part of the answer lies in the astonishing number of neurons and synapses that process electrical impulses in parallel. Part of it must be found in the ability of the nervous system to evolve in response to external stimuli and grow, sharpen, and depress synaptic connections. However, we are far from understanding even the basic mechanisms that allow us to think, be aware, recognize patterns, and imagine. The brain can do all this while consuming only around 20 Watts, out-competing any human-made processor in terms of energy-efficiency. This question is of particular interest in a historical era and technological stage where phrases like machine learning and artificial intelligence are more and more widespread, thanks to recent advances produced in the field of computer science. However, brain-inspired computation is today still relying on algorithms that run on traditional silicon-made, digital processors. Instead, the making of brain-like hardware, where the substrate itself can be used for computation and it can dynamically update its electrical pathways, is still challenging. In this work, I tried to employ organic semiconductors that work in electrolytic solutions, called organic mixed ionic-electronic conductors (OMIECs) to build hardware capable of computation. Moreover, by exploiting an electropolymerization technique, I could form conducting connections in response to electrical spikes, in analogy to how synapses evolve when the neuron fires. After demonstrating artificial synapses as a potential building block for neuromorphic chips, I shifted my attention to the implementation of such synapses in fully operational networks. In doing so, I borrowed the mathematical framework of a machine learning approach known as reservoir computing, which allows computation with random (neural) networks. I capitalized my work on demonstrating the possibility of using such networks in-vivo for the recognition and classification of dangerous and healthy heartbeats. This is the first demonstration of machine learning carried out in a biological environment with a biocompatible substrate. The implications of this technology are straightforward: a constant monitoring of biological signals and fluids accompanied by an active recognition of the presence of malign patterns may lead to a timely, targeted and early diagnosis of potentially mortal conditions. Finally, in the attempt to simulate the random neural networks, I faced difficulties in the modeling of the devices with the state-of-the-art approach. Therefore, I tried to explore a new way to describe OMIECs and OMIECs-based devices, starting from thermodynamic axioms. The results of this model shine a light on the mechanism behind the operation of the organic electrochemical transistors, revealing the importance of the entropy of mixing and suggesting new pathways for device optimization for targeted applications

Behavior-specific proprioception models for robotic force estimation: a machine learning approach

Robots that support humans in physically demanding tasks require accurate force sensing capabilities. A common way to achieve this is by monitoring the interaction with the environment directly with dedicated force sensors. Major drawbacks of such special purpose sensors are the increased costs and the reduced payload of the robot platform. Instead, this thesis investigates how the functionality of such sensors can be approximated by utilizing force estimation approaches. Most of today’s robots are equipped with rich proprioceptive sensing capabilities where even a robotic arm, e.g., the UR5, provides access to more than hundred sensor readings. Following this trend, it is getting feasible to utilize a wide variety of sensors for force estimation purposes. Human proprioception allows estimating forces such as the weight of an object by prior experience about sensory-motor patterns. Applying a similar approach to robots enables them to learn from previous demonstrations without the need of dedicated force sensors. This thesis introduces Behavior-Specific Proprioception Models (BSPMs), a novel concept for enhancing robotic behavior with estimates of the expected proprioceptive feedback. A main methodological contribution is the operationalization of the BSPM approach using data-driven machine learning techniques. During a training phase, the behavior is continuously executed while recording proprioceptive sensor readings. The training data acquired from these demonstrations represents ground truth about behavior-specific sensory-motor experiences, i.e., the influence of performed actions and environmental conditions on the proprioceptive feedback. This data acquisition procedure does not require expert knowledge about the particular robot platform, e.g., kinematic chains or mass distribution, which is a major advantage over analytical approaches. The training data is then used to learn BSPMs, e.g. using lazy learning techniques or artificial neural networks. At runtime, the BSPMs provide estimates of the proprioceptive feedback that can be compared to actual sensations. The BSPM approach thus extends classical programming by demonstrations methods where only movement data is learned and enables robots to accurately estimate forces during behavior execution