Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCube

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCub

Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub

An Innovative Telepathology Solution For Developing Countries

INTRODUCTION / BACKGROUND: The increasing incidence of pathologies like tumors and infections is a significant public health burden in developing countries. The ability to provide early diagnosis, treatments, follow-up care has a strong impact on the survival. Telemedicine is of great utility in countries lacking appropriate healthcare facilities by allowing for the performance of good level healthcare practices. Sub-Saharan African Countries suffer a dramatic shortage of medical pathologists (in the range of 1 to 10 pathologists per 10 million people) and are also victims of digital divide. Vittorio Tison Association (Tison), IRST research cancer hospital and Patologi Oltre Frontiera NGO (APOF) cooperate in the sanitary mission founded in 1999 in Bugando Medical Centre (BMC), a hospital located in Mwanza, Tanzania. In that mission, during 2011 we started the development of our telemedicine project. The project utilizes a novel telematic platform oriented to several sanitary branches with a special focus in pathology and oncology. AIMS: The main project goals are: to provide ICT and TLC services between healthcare facilities in developed and developing countries; to allow for simultaneous telepathology counselling sharing microscopy and radiology images, conference calling, remote diagnosis, double-blind evaluation, second opinion and the remote control of medical instrumentation; to perform e-learning and remote quality control; to carry out GCP clinical trials through data collection, monitoring and evaluation; to encourage and support scientific research; to reduce the knowledge gaps inherent to the digital divide. METHODS: APOF has been developing the BMC pathology lab from 2000 to 2008. In the meantime Tison took care of training in Oncology of local medical doctors, opened the BMC Oncology Department in 2010 and patronized the building of a new clinic dedicated to the Oncologic Institute. We started the development of the project in 2011 with a general assessment of the needs and lacks in local working procedures, related to the possible improvements in ICT. Our first step involved the Internet connections activation and the implementation of the project informatic core in the IT room of the BMC Oncologic Institute. The telematic link between IRST’s Italian site and BMC has been realized during the early pilot phase. We carried out several experimental sessions to investigate the compatibility of the main third-parts digital pathology products with our platform, choosing digital microscope Menarini D-SIGHT in association with D-SIGHT+ telepathology web-based application. Finally, on June 2015 we launched the BMC Telepathology Facility performing a complete demo of the system during the AORTIC East African Regional Meeting. RESULTS: We validated the system in a wide range of conditions. Experimental data indicate an improvement of a factor up to 100 in the overall images transmission rate in comparison to the previous models. The pathology images remotely viewed are fully compliant with the diagnostic requirements in terms of definition and magnification. The platform is easy-to-use, all sanitary operators involved in the testing found it friendly and effective. The images browsing on the screen is very fast and precise, professional operators evaluated this solution equivalent to the use of the microscope. Our project is characterized by a high level of innovation which increases efficiency and efficacy of health practices and can boost the use of telepathology in developing countries

Reinforcement learning helps slam: Learning to build maps

In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments

Towards continuous control for mobile robot navigation: A reinforcement learning and slam based approach

We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot

CURIOSITY-DRIVEN REINFORCEMENT LEARNING AGENT FOR MAPPING UNKNOWN INDOOR ENVIRONMENTS

Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinforcement learning agent is in charge of selecting the commands for steering the mobile robot, while a SLAM algorithm estimates the robot pose and maps the environments. The agent, to select optimal actions, is trained to be curious about the world. This concept translates into the introduction of a curiosity-driven reward function that encourages the agent to steer the mobile robot towards unknown and unseen areas of the world and the map. We test our approach in explorations challenges in different indoor environments. The agent trained with the proposed reward function outperforms the agents trained with reward functions commonly used in the literature for solving such tasks