An autonomous vehicle is not a “simple” robot, such as a robot companion, but a robot that transports people. That implies that the people inside must feel integrated in the environment, as they would be in a driven car. They expect, as well as people in the surroundings, the cybercar to behave accordingly adhering to social and urban conventions and negotiating its path among crowded environments. This is a new challenging topic that must and will be tackled in the HIANIC project. This project is part of the “Axe : Véhicules propres, sûrs, connectés, automatisés ” of the “défi 6 – mobilité et systèmes urbains durables. The HIANIC cybercar will analyze its environment by detecting people, evaluating crowd flows, recognizing typical scenarios. It will infer the reaction of the passengers in order to navigate in a way that makes them feel comfortable. Several navigation strategies will collaborate to adapt the movement of the cybercar to the typical scenarios. For example, in a crowded environment, the cybercar will move using reactive navigation but in less cluttered environments, human-aware navigation will be used. Finally, the vehicle will communicate its intention to the passengers and pedestrians and pay attention to its environment (passengers and pedestrians), increasing its knowledge of the situation for handling emergency situations. Such a system will contribute both to urban safety and intelligent mobility in “shared spaces”. Negotiation will help to avoid frozen situations increasing the vehicle’s reactivity and optimizing the navigable space. Negotiation, Human-Aware Navigation and Communication will contribute to a better public acceptance of such autonomous systems and facilitate their penetration in the transportation landscape.

The last decade has acknowledged the emergence of a new paradigm for building large-scale distributed systems: the microservice architecture. As opposed to the classical tightly coupled monolith architectures, this paradigm promotes small loosely coupled autonomous services, so called microservices. Such software services are to be small to encapsulate a well-defined functionality and thus facilitate bug isolation and maintenance. They should be loosely coupled to support independent software updates. Finally, they need to be autonomous to enable independent delivery to production. Adopting microservices becomes a general trend and experts predict that "by 2022, 90% of all new apps will feature microservices architectures". Among companies working around microservices are infrastructure providers, including GAFAM and EOLAS, major IT players, including Netflix and Uber, but also telco operators, including Orange. They all build on the container technology to facilitate functionality encapsulation, dependency isolation and standalone deployment, as required by microservices. However, the current used orchestration solutions (e.g., Kubernetes) struggle with the scale and the heterogeneity of microservices and fail to fix the performance problems fast enough. The challenge addressed by the SCALER project is to optimize the scaling of microservice-based networked services while satisfying their stringent IT and telco requirements. The proposal targets three research aspects, namely automatic microservice characterization, identification of microservice interaction patterns and microservice smart-scaling. SCALER brings together two academical and two industrial partners, namely LIG ERODS, INRIA Spirals, Orange Innovation and EOLAS B&D.

Despite the wide use of property graphs as a flexible data model for numerous applications and use cases, the current graph processing systems lack foundations for well-defined semantics of the underlying graph query languages and mapping specifications. These are however the principal building blocks of modern graph processing and graph data integration systems. The project VeriGraph is thus motivated by two main observations. First, there is currently a shift from relational to Graph Databases that still suffer from the lack of a formal semantics. Second, graph databases need to be both queried and also transformed in a meaningful and reliable way. The project will address these issues by making decisive contributions at the interface of Graph Databases and Programming Language Theory, by (1) enriching graph databases with formal semantic information; (2) verifying and informing the design of the next generation of graph query languages; (3) defining graph transformation and schema mapping languages with a formal semantics to permit fully automated verification of enforcement of consistency constraints. The project will have a considerable impact on the design and specification of a new standard for a graph query language pursued by the International Organization for Standardization together with the key graph database vendors. The project will in addition significantly advance our understanding of property graph transformations for data integration and data curation.

Despite recent advances in robotics around autonomous machines, machines really capable of giving a real to humans, as part of everyday life, remains limited. On the other hand, the animal, especially dogs, has shown for many years the capacity to complete the man in his life in daily tasks, such as aid for service dogs, or in exceptional event such as avalanche or earthquakes intervention, or in professional life to search for drugs, for example. Nevertheless, each system, robot or dog has its own limits, for example: a step or a hole can be fatal to a robot, a cat turns "mad" a dog. The actual idea is to take best of both entities to increase the performance of the couple animal / robot. In this context, the subject requires extensive research in particular on the development of interaction, communication or cooperation between a robot and a dog. The Cochise project proposes a modest focus of this research, on a particular set of dogs: service dogs for people with mobility impairments. Service dogs assist the human in the gestures of everyday life and for this purpose the problem is clearly identified. In this context, we propose to design a robot to act on and with the service dog, to build a "couple" with high performance in regard to the problem. The machine will become a mediator robot that allows on one hand to "understand" the state of the dog and retransmit it to the man, and on the other hand to enable autonomous robot via the trigger sequences of behavior on the dog. The question underlying the Cochise project is: how far a dog he will accept to obey to a robot? and what type of robot should be designed for this?. The project Cochise is twofold: firstly to increase the knowledge on the interactions in an animal / robot system and to achieve an experimental development that could quickly become a mainstream product. For this project we bring together the skills of users and trainers of service dogs , people specialized in robotics sensors and actuators for the realization of the robot and finally reseachers specialized in human / machine interface and architectures to build an operational device for the user.

Evaluating search systems requires setting up an environment: select a paradigm, metrics, a dataset, etc. The choice of an environment is rarely motivated objectively, and the impact of its variations (choosing a dataset against another, altering one) is rarely measured. Such objectivity comes from a quantifiable understanding of the differences between datasets, documents, or test queries. In Kodicare, we generically call such difference “knowledge delta”. Evaluation of several environments, knowing their knowledge deltas, leads to measuring and qualifying “results deltas”. Online systems require continuous evaluation with a stable and meaningful environment; which guarantees the reproducibility and explainability of systems results. The environment and result deltas will be able to support such continuous evaluation, and to provide explanations. The theoretical results will be confronted to real cases defined by a French company that deploys a web search engine (Qwant). Scientific and technical challenges: To our knowledge, no such framework dedicated to real continuous evaluation of information retrieval systems exist, due to the numerous parameters that must be handled. The deltas proposed by Kodicare are then a sensible way to tackle this problem. Continuous evaluation is only possible with real cases, which are often difficult to define without the help of web search companies. The large implication of Qwant will help the project define usable proposals. Expected results: • The innovative theoretical solution explored by this project is to define in a common framework “knowledge delta” and “result deltas”, and quantify them such that: results are comparable over the time (like a regression test) and the search engine adapts to changes in user behaviour and information need. • A transversal focus of the project will explore the transparency that such a continuous evaluation system must support. • New evaluation paradigm with a potential impact on many connected communities