A machine learning based personalized system for driving state recognition

Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of advanced functions such as driver safety monitoring systems and adaptive driving assistance systems. In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognition system by learning from non-intrusive, easily accessible vehicle related measurements and its validation using real-world driving data. Compared to conventional approaches, this paper first highlights the necessities of adopting a personalized system by analysing feature distribution of individual driver’s data and all drivers’ data via advanced data visualization and statistical analysis. If significant differences are identified, a dedicated personalized model is learnt to predict the driver’s driving state. Spearman distance is also drawn to evaluate the differences between individual driver’s data and all drivers’ data in a quantitative manner. In addition, five categories of classifiers are tested and compared to identify a suitable one for classification, where random forest with Bayesian parameter optimization outperforms others and therefore is adopted in this paper. A recently collected dataset from real-world driving experiments is adopted to evaluate the proposed system. Comparative experimental results indicate that the personalized learning system with road information significantly outperforms conventional approaches without considering personalized characteristics or road information, where the overall accuracy increases from 81.3% to 91.6%. It is believed that the newly developed personalized learning system can find a wide range of applications where diverse behaviours exist

Dependability for declarative mechanisms: neural networks in autonomous vehicles decision making.

Despite being introduced in 1958, neural networks appeared in numerous applications of different fields in the last decade. This change was possible thanks to the reduced costs of computing power required for deep neural networks, and increasing available data that provide examples for training sets. The 2012 ImageNet image classification competition is often used as a example to describe how neural networks became at this time good candidates for applications: during this competition a neural network based solution won for the first time. In the following editions, all winning solutions were based on neural networks. Since then, neural networks have shown great results in several non critical applications (image recognition, sound recognition, text analysis, etc...). There is a growing interest to use them in critical applications as their ability to generalize makes them good candidates for applications such as autonomous vehicles, but standards do not allow that yet. Autonomous driving functions are currently researched by the industry with the final objective of producing in the near future fully autonomous vehicles, as defined by the fifth level of the SAE international (Society of Automotive Engineers) classification. Autonomous driving process is usually decomposed into four different parts: the where sensors get information from the environment, the where the data from the different sensors is merged into one representation of the environment, the that uses the representation of the environment to decide what should be the vehicles behavior and the commands to send to the actuators and finally the part that implements these commands. In this thesis, following the interest of the company Stellantis, we will focus on the decision part of this process, considering neural network based solution. Automotive being a safety critical application, it is required to implement and ensure the dependability of the systems, and this is why neural networks use is not allowed at the moment: their lack of safety forbid their use in such applications. Dependability methods for classical software systems are well known, but neural networks do not have yet similar dependable mechanisms to guarantee their trust. This problem is due to several reasons, among them the difficulty to test applications with a quasi-infinite operational domain and whose functions are hard to define exhaustively in the specifications. Here we can find the motivation of this thesis: how can we ensure the dependability of neural networks in the context of decision for autonomous vehicles? Research is now being conducted on the topic of dependability and safety of neural networks with several approaches being considered and our research is motivated by the great potential in safety critical applications mentioned above. In this thesis, we will focus on one category of method that seems to be a good candidate to ensure the dependability of neural networks by solving some of the problems of testing: the formal verification for neural networks. These methods aim to prove that a neural network respects a safety property on an entire range of its input and output domains. Formal verification is already used in other domains and is seen as a trusted method to give confidence in a system, but it remains for the moment a research topic for neural networks with currently no industrial applications. The main contributions of this thesis are the following: a proposal of a characterization of neural network from a software development perspective, and a corresponding classification of their faults, errors and failures, the identification of a potential threat to the use of formal verification. This threat is the erroneous neural network model problem, that may lead to trust a formally validated safety property that does not hold in real life, the realization of an experiment that implements a formal verification for neural networks in an autonomous driving application that is to the best of our knowledge the closest to industrial use. For this application, we chose to work with an ACC (Adaptive Cruise Control) function, which is an autonomous driving function that performs the longitudinal control of a vehicle. The experiment is conducted with the use of a simulator and a neural network formal verification tool. The other contributions of the thesis are the following: theoretical example of the erroneous neural network model problem and a practical example in our autonomous driving experiment, a proposal of detection and recovery mechanisms as a solution to the erroneous model problem mentioned above, an implementation of these detection and recovery mechanisms in our autonomous driving experiment and a discussion about difficulties and possible processes for the implementation of formal verification for neural networks that we developed during our experiments

System of Systems conceptual design methodology for space exploration

The scope of the research is to identify and develop a design methodology for System-of-System (a set of elements and sub-elements able to interact and cooperate in order to complete a mission), based on models, methods and tools, to support the decision makers during the space exploration scenarios design and evaluation activity in line with the concurrent design philosophy. Considering all combinations of system parameters (such as crew size, orbits, launchers, spacecraft, ground and space infrastructures), a large number of mission concept options are possible, even though not all of them are optimal or even feasible. The design methodology is particularly useful in the first phases of the design process (Phase 0 and A) to choose rationally and objectively the best mission concepts that ensure the higher probability of mission success in compliance with the high level requirements deriving from the “user needs”. The first phases of the project are particularly critical for the success of the entire mission because the results of this activity are the starting point of the more costly detailed design phases. Thus, any criticality in the baseline design will involve inevitably into undesirable and costly radical system redesigns during the advanced design phases. For this reason, it is important to develop reliable mathematical models that allow prediction of the system performances notwithstanding the poorly defined environment of very high complexity. In conjunction with the development of the design methodology for system-of-systems and in support of it, a software tool has been developed. The tool has been developed into Matlab environment and provides users with a useful graphical interface. The tool integrates the model of the mission concept, the models of the space elements at system and subsystem level, the cost-effectiveness model or value, the sensitivity and multi-objective optimization analysis. The tool supports users to find a system design solution in compliance with requirements and constraints, such as mass budgets and costs, and provides them with information about cost-effectiveness of the mission. The developed methodology has been applied for the design of several space elements (Man Tended Free Flyer, Cargo Logistic Vehicle, Rover Locomotion System) and several mission scenarios (Moon surface infrastructure support, Cis-Lunar infrastructure delivering, Cis-Lunar infrastructure logistic support), in order to assess advantages and disadvantages of the proposed method. The results of the design activity have been discussed and accepted by the European Space Agency (ESA) and have also been compared and presented to the scientific community. Finally, in a particular case, the study of the locomotion system of a lunar rover, the results of the methodology have been verified through the production and testing of the same system

Runtime observable and adaptable UML state machine-based software components generation and verification: [email protected] approach

Cyber-Physical Systems (CPSs) are embedded computing systems in which computation interacts closely with the physical world through sensors and actuators. CPSs are used to control context aware systems. These types of systems are complex systems that will have different configurations and their control strategy can be configured depending the environmental data and current situation of the context. Therefore, in current industrial environments, the software of embedded and Cyber-Physical systems have to cope with increasing complexity, uncertain scenarios and safe requirements at runtime. The UML State Machine is a powerful formalism to model the logical behaviour of these types of systems, and in Model Driven Engineering (MDE) we can generate code automatically from these models. MDE aims to overcome the complexity of software construction by allowing developers to work at the high-level models of software systems instead of low-level codes. However, determining and evaluating the runtime behaviour and performance of models of CPSs using commercial MDE tools is a challenging task. Such tools provide little support to observe at model-level the execution of the code generated from the model, and to collect the runtime information necessary to, for example, check whether defined safe properties are met or not. One solution to address these requirements is having the software components information in model terms at runtime ([email protected]). Work on [email protected] seeks to extend the applicability of models produced in MDE approaches to the runtime environment. Having the model at runtime is the first step towards the runtime verification. Runtime verification can be performed using the information of model elements (current state, event, next state,etc.) This thesis aims at advancing the current practice on generating automatically Unified Modeling Language - State Machine (UML-SM) based software components that are able to provide their internal information in model terms at runtime. Regarding automation, we propose a tool supported methodology to automatically generate these software components. As for runtime monitoring, verification and adaptation, we propose an externalized runtime module that is able to monitor and verify the correctness of the software components based on their internal status in model terms at component and system level. In addition, if an error is detected, the runtime adaptation module is activated and the safe adaptation process starts in the involved software components. All things considered, the overall safe level of the software components and CPSs is enhanced.Sistema Ziber-Fisikoak, konputazio sistema txertatuez osatuta daude. Konputazio sistema txertatu hauek, mundu birtuala mundu fisikoarekin uztartzeko gaitasuna eskaintzen dute. Sistema ziberfisikoak orokorrean sistema konplexuak izan ohi dira eta inguruan gertazen denaren araberako konfigurazio desberdinak izan ohi dituzte. Gaur egungo industria ingurunetan, sistema hauek daramaten kontroleko softwarea asko handitu da eta beren konplexutasunak ere gorakada handia izan du: aurrez ezagunak ez diren baldintza eta inguruetan lan egin beharra dute askotan, denbora errealeko eskakizunak eta segurtasun eskakizunak ere beteaz. UML State Machine formalismoa, goian aipaturiko sistema mota horien portaera logikoa modelizatzeko erabiltzen den formalismo indartsu bat da. Formalismo honen baitan eta Model Driven Engineering (MDE) enfokea jarraituaz, sistema modelatzeko erabilitako grafikoetatik sisteman txertatua izango den kodea automatikoki sor genezake. MDEk softwarea sortzeko orduan izan genezakeen konplexutasuna gainditu nahi du, garatzailei software-sistemen goi-mailako ereduetan lan egiteko aukera emanez. Hala ere, MDE-an oinarrituriko tresna komertzialak erabiliaz, zaila izaten da berauen bidez sorturiko kodearen errendimendua eta portaera sistema exekuzioan dagoenean ebaluatzea. Tresna horiek laguntza gutxi eskaintzen dute modelotatik sortutako kodea exekutatzen ari denean sisteman zer gertatzen ari denaren informazioa modeloaren terminoetan jasotzeko. Beraz, exekuzio denboran, oso zaila izaten da sistemaren portaera egokia den edo ez aztertzea modelo mailako informazio hori erabiliaz. Eskakizun horiek kudeatzeko modu bat, software modeloaren informazioa denbora errealean izatea da ([email protected] enfokea). [email protected] enfokearen helburu nagusietako bat, MDE enfokearekin garapen fasean sortutako modeloak exekuzio denboran (runtime-en) erabilgarri izatean datza. Exekuzio denboran egiaztapen edo testing-a egin ahal izateko lehen urratsa, testeatu nahi den software horren modeloa exekuzio denboran eskuragarri izatea da. Honela, exekuzio denborako egiaztapen edo berifikazioak softwarea modelatzeko erabili ditugun elementu berberak erabiliaz egin daitke (egungo egoera, gertaera, hurrengo egoera, eta abar). Tesi honen helburutako bat UML-State Machine modeloetan oinarritutako eta exekuzio denboran beren barne egoeraren informazioa modeloko elementu bidez probestu ahalko duten software osagaiak modu automatikoan sortzea da. Automatizazioari dagokionez, lehenik eta behin, software-osagai horiek automatikoki sortzen dituzten tresnak eskaintzen dituen metodologia proposatzen dugu. Bigarrenik, UMLSM oinarritutako software osagaiak automatikoki sortuko dituen herraminta bera proposatzen dugu. Exekuzio denboran eguneraketen jarraipenari, egiaztatzeari eta egokitzeari dagokionez, barne egoera UML-SM modelo terminoetan eskaintzen duten software osagaiak egiaztatzeko eta egokitzeko gai den kanpo exekuzio modulo bat proposatzen dugu. Honela, errore bat detektatzen bada, exekuzio garaian egokitze modulua aktibatuko da egokitzapen prozesu segurua martxan jarriaz. Honek, dagokion software osagaiari abixua bidaliko dio egokitzapena egin dezan. Gauza guztiak kontuan hartuta, software osagaien eta CPSen segurtasun maila orokorra hobetua izango da.Los sistemas cyber-físicos (CPSs) son sistemas de computación embebidos en los que la computación interactúa estrechamente con el mundo físico a través de sensores y actuadores. Los CPS se utilizan para controlar sistemas que proveen conocimiento del contexto. Este tipo de sistemas son sistemas complejos que suelen tener diferentes configuraciones y su estrategia de control puede configurarse en función de los datos del entorno y de la situación actual del contexto. Por lo tanto, en los entornos industriales actuales, el software de los sistemas embebidos tiene que hacer frente a la creciente complejidad, los escenarios inciertos y los requisitos de seguridad en tiempo de ejecución. Las máquinas de estado UML son un formalismo muy utilizado en industria para modelar el comportamiento lógico de este tipo de sistemas, y siguiendo el enfoque Model Driven Engineering (MDE) podemos generar código automáticamente a partir de estos modelos. El objetivo de MDE es superar la complejidad de la construcción de software permitiendo a los desarrolladores trabajar en los modelos de alto nivel de los sistemas de software en lugar de tener que codificar el control mediante lenguajes de programación de bajo nivel. Sin embargo, determinar y evaluar el comportamiento y el rendimiento en tiempo de ejecución de estos modelos generados mediante herramientas comerciales de MDE es una tarea difícil. Estas herramientas proporcionan poco apoyo para observar a nivel de modelo la ejecución del código generado a partir del modelo. Por lo tanto, no son muy adecuadas para poder recopilar la información de tiempo de ejecución necesaria para, por ejemplo, comprobar si se cumplen o no las restricciones definidas. Un enfoque para gestionar estos requisitos, es tener la información de los componentes de software en términos de modelo en tiempo de ejecución ([email protected]). El trabajo en [email protected] busca ampliar la aplicabilidad de los modelos producidos en fase de desarrollo mediante el enfoque MDE al entorno de tiempo de ejecución. Tener el modelo en tiempo de ejecución es el primer paso para poder llevar a cabo la verificación en tiempo de ejecución. Así, esta verificación se podrá realizar utilizando la información de los elementos del modelo (estado actual, evento, siguiente estado, etc.). El objetivo de esta tesis es avanzar en la práctica actual de generar automáticamente componentes software basados en Unified Modeling Language - State Machine (UML-SM) que sean capaces de proporcionar información interna en términos de modelos en tiempo de ejecución. En cuanto a la automatización, en primer lugar, proponemos una metodología soportada por herramientas para generar automáticamente estos componentes de software. En segundo lugar, proponemos un marco de trabajo de generación de componentes de software basado en UML-SM. En cuanto a la monitorización, verificación y adaptación en tiempo de ejecución, proponemos un módulo de tiempo de ejecución externalizado que es capaz de monitorizar y verificar la validez de los componentes del software en función de su estado interno en términos de modelo. Además, si se detecta un error, se activa el módulo de adaptación en tiempo de ejecución y se inicia el proceso de adaptación seguro en el componente de software correspondiente. Teniendo en cuenta todo esto, el nivel de seguridad global de los componentes del software y de los CPS se ve mejorado

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Wearable Platform for Patient Monitoring during Mass Casualty Incidents

Based on physiological data, intelligent algorithms can assist with the classification and recognition of the most severely impaired victims. This dissertation presents a new sensorbased triage platform with the main proposal to join different sensor and communications technologies into a portable device. This new device must be able to assist the rescue units along with the tactical planning of the operation. This dissertation discusses the implementation and the evaluation of the platform

A Wearable Platform for Patient Monitoring during Mass Casualty Incidents

Based on physiological data, intelligent algorithms can assist with the classification and recognition of the most severely impaired victims. This book presents a new sensorbased triage platform with the main proposal to join different sensor and communications technologies into a portable device. This new device must be able to assist the rescue units along with the tactical planning of the operation. This work discusses the implementation and the evaluation of the platform

The Second Conference on Lunar Bases and Space Activities of the 21st Century, volume 2

These 92 papers comprise a peer-reviewed selection of presentations by authors from NASA, the Lunar and Planetary Institute (LPI), industry, and academia at the Second Conference on Lunar Bases and Space Activities of the 21st Century. These papers go into more technical depth than did those published from the first NASA-sponsored symposium on the topic, held in 1984. Session topics included the following: (1) design and operation of transportation systems to, in orbit around, and on the Moon; (2) lunar base site selection; (3) design, architecture, construction, and operation of lunar bases and human habitats; (4) lunar-based scientific research and experimentation in astronomy, exobiology, and lunar geology; (5) recovery and use of lunar resources; (6) environmental and human factors of and life support technology for human presence on the Moon; and (7) program management of human exploration of the Moon and space