Probabilistic Approach for Road-Users Detection

Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians and vehicles. One of the major challenges in state-of-the-art deep-learning based object detection is false positive which occurrences with overconfident scores. This is highly undesirable in autonomous driving and other critical robotic-perception domains because of safety concerns. This paper proposes an approach to alleviate the problem of overconfident predictions by introducing a novel probabilistic layer to deep object detection networks in testing. The suggested approach avoids the traditional Sigmoid or Softmax prediction layer which often produces overconfident predictions. It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives. The approach is validated on the 2D-KITTI objection detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed approach enables enabling interpretable probabilistic predictions without the requirement of re-training the network and therefore is very practical.Comment: This work has been submitted to IEEE T-ITS for review and possible publicatio

AUTOCITS – Regulation study for interoperability in the adoption of autonomous driving in European urban nodes

International audienceThe technological advances of autonomous and connected road vehicles have been shown an accelerating pace in the recent years. On the other hand, the regulations for autonomous, or driverless, road vehicles across Europe still deserve much attention and discussion. In this paper, we introduce the AUTOCITS project which has the main goals of conducting studies on the regulations for the adoption of autonomous cars in Europe, and also to carry out C-ITS Pilots in Madrid, Paris and Lisbon. AUTOCITS aims to contribute, directly or indirectly, to European related policy and reference documents on vehicle automation, regulations, connected and automated driving, and related road infrastructure issues due to the trend towards higher levels of connectivity and automation, where information provided via C-ITS can be truly catalyst for connected and autonomous driving. The project will specially focus on the communication links performance and connectivity between automated vehicles using C-ITS applications connectivity and automation ;, in particular, applications increase surrounding environment awareness in relation to infrastructure and ensure both road and driver safety requirements issues and using the regulation framework. AUTOCITS is an innovation project (CEF Program) that aims to facilitate the deployment of autonomous vehicles in urban nodes by developing intelligent transport services based on cooperative systems (C-ITS) that will enable vehicles, users and infrastructures to communicate, exchange, and share information

Multi-Sensor Object Detection for Autonomous Driving

Thesis submitted to the Department of Electrical and Computer Engineering of the Faculty of Science and Technology of the University of Coimbra in partial fulfillment of the requirements for the Degree of Doctor of PhilosophyNesta tese é proposto um novo sistema multissensorial de detecção de obstáculos e objetos usando um LIDAR-3D, uma câmara monocular a cores e um sistema de posicionamento baseado em sensores inerciais e GPS, com aplicação a sistemas de condução autónoma. Em primeiro lugar, propõe-se a criação de um sistema de deteção de obstáculos, que incorpora dados 4D (3D espacial + tempo) e é composto por dois módulos principais: (i) uma estimativa do perfil do chão através de uma aproximação planar por partes e (ii) um modelo baseado numa grelha de voxels para a deteção de obstáculos estáticos e dinâmicos recorrendo à informação do próprio movimento do veículo. As funcionalidade do systemo foram posteriormente aumentado para permitir a Deteção e Seguimento de Objetos Móveis (DATMO) permitindo a percepção ao nível do objeto em cenas dinâmicas. De seguida procede-se à fusão dos dados obtidos pelo LIDAR-3D com os dados obtidos por uma câmara para melhorar o desempenho da função de seguimento do sistema DATMO. Em segundo lugar, é proposto um sistema de deteção de objetos baseado nos paradigmas de geração e verificação de hipóteses, usando dados obtidos pelo LIDAR-3D, recorrendo à utilização de redes neurais convolucionais (ConvNets). A geração de hipóteses é realizada aplicando um agrupamento de dados ao nível da nuvem de pontos. Na fase de verificação de hipóteses, é gerado um mapa de profundidade a partir dos dados do LIDAR-3D, sendo que esse mapa é inserido numa ConvNet para a deteção de objetos. Finalmente, é proposta uma detecção multimodal de objetos usando uma rede neuronal híbrida, composta por Deep ConvNets e uma rede neural do tipo Multi-Layer Perceptron (MLP). As modalidades sensoriais consideradas são: mapas de profundidade, mapas de reflectância geradas a partir do LIDAR-3D e imagens a cores. São definidos três detetores de objetos que individualmente, em cada modalidade, recorrendo a uma ConvNet detetam as bounding boxes do objeto. As deteções em cada uma das modalidades são depois consideradas em conjunto e fundidas por uma estratégia de fusão baseada em MLP. O propósito desta fusão é reduzir a taxa de erro na deteção de cada modalidade, o que leva a uma deteção mais precisa. Foram realizadas avaliações quantitativas e qualitativas dos métodos propostos, utilizando conjuntos de dados obtidos a partir dos datasets "Avaliação de Detecção de Objetos" e "Avaliação de Rastreamento de Objetos" do KITTI Vision Benchmark Suite. Os resultados obtidos demonstram a aplicabilidade e a eficiência da abordagem proposta para a deteção de obstáculos e objetos em cenários urbanos.In this thesis, we propose on-board multisensor obstacle and object detection systems using a 3D-LIDAR, a monocular color camera and a GPS-aided Inertial Navigation System (INS) positioning data, with application in self-driving road vehicles. Firstly, an obstacle detection system is proposed that incorporates 4D data (3D spatial data and time), and composed by two main modules: (i) a ground surface estimation using piecewise planes, and (ii) a voxel grid model for static and moving obstacles detection using ego-motion information. An extension of the proposed obstacle detection system to a Detection And Tracking Moving Object (DATMO) system is proposed to achieve an object-level perception of dynamic scenes, followed by the fusion of 3D-LIDAR with camera data to improve the tracking function of the DATMO system. The obstacle detection we propose is to effectively model dynamic driving environment. The proposed DATMO method is able to deal with the localization error of the position sensing system when computing the motion. The proposed fusion tracking module integrates multiple sensors to improve object tracking. Secondly, an object detection system based on the hypothesis generation and verification paradigms is proposed using 3D-LIDAR data and Convolutional Neural Networks (ConvNets). Hypothesis generation is performed by applying clustering on point cloud data. In the hypothesis verification phase, a depth map is generated using 3D-LIDAR data, and the depth map values are inputted to a ConvNet for object detection. Finally, a multimodal object detection is proposed using a hybrid neural network, composed by deep ConvNets and a Multi-Layer Perceptron (MLP) neural network. Three modalities, depth and reflectance maps (both generated from 3D-LIDAR data) and a color image, are used as inputs. Three deep ConvNet-based object detectors run individually on each modality to detect the object bounding boxes. Detections on each one of the modalities are jointly learned and fused by an MLP-based late-fusion strategy. The purpose of the multimodal detection fusion is to reduce the misdetection rate from each modality, which leads to a more accurate detection. Quantitative and qualitative evaluations were performed using ‘Object Detection Evaluation’ dataset and ‘Object Tracking Evaluation’ based derived datasets from the KITTI Vision Benchmark Suite. Reported results demonstrate the applicability and efficiency of the proposed obstacle and object detection approaches in urban scenarios

Digital Twin Driven Smart Home: A Feasibility Study

International audienceWe aim to facilitate the daily-life activities of frail or elderly people in collaboration with mobile assistive robots through the means of a digital twin-powered smart home. Being able to quickly and efficiently produce a digital twin of the human user's environment, can help to further develop personalized assistive solutions. As our first investigation toward this goal, we describe our proof-of-concept "digital twindriven smart home" implementation. It consists of a virtual representation, robot navigation and environment semantics using open-source software. The initial obtained results on the building process of the digital twin are encouraging and suggest the possibility of integration of digital twin for smart spaces

Bone Surface Reconstruction and Clinical Features Estimation from Sparse Landmarks and Statistical Shape Models: A feasibility study on the femur

International audienceIn this study, we investigated a method allowing the determination of the femur bone surface as well as its mechanical axis from some easy-to-identify bony landmarks. The reconstruction of the whole femur is therefore performed from these landmarks using a Statistical Shape Model (SSM). The aim of this research is therefore to assess the impact of the number, the position, and the accuracy of the landmarks for the reconstruction of the femur and the determination of its related mechanical axis, an important clinical parameter to consider for the lower limb analysis. Two statistical femur models were created from our in-house dataset and a publicly available dataset. Both were evaluated in terms of average point-to-point surface distance error and through the mechanical axis of the femur. Furthermore, the clinical impact of using landmarks on the skin in replacement of bony landmarks is investigated. The predicted proximal femurs from bony landmarks were more accurate compared to on-skin landmarks while both had less than 3.5 • degrees mechanical axis angle deviation error. The results regarding the non-invasive determination of the mechanical axis are very encouraging and could open very interesting clinical perspectives for the analysis of the lower limb either for orthopedics or functional rehabilitation

Effect of Attaching Standard Medical Recording Guidelines to the Patient File on Quality of Medical Students’ Skills

Introduction: Currently, medical staff of hospitals use a number of recorded files in the treatment process of patients, but we have noticed that there are insufficiencies and gaps in data of the medical recordings, some of which may be the reason behind serious problems related to treating patients. Other studies have shown some weaknesses in the medical recording systems in our country so we studied effect of attaching a standard recording guidelines sheet to patients’files as a reference for the recorder. Methods: In this study, 50 externs and 40 interns were enrolled. They were responsible for 60 patients in the general internal medicine ward of Sina hospital, University of Medical Sciences, Tabriz, Iran. This study was done during 6 months in the Sina hospital (January 2010-August 2010). Standard medical recording guidelines were attached to the patients’ files. The externs studied off note writing, and the interns studied consultation, off note and orders writing in the first day of patient hospitalization. The quality of their medical writing was assessed before and after attaching guidelines. The students were not aware of the evaluation of their work. If the writing met less than 70% of the standard format, it was not accepted. Result: The consultation sheet of the interns showed significant differences before and after the guidelines’ attachment in problem list writing (p= 0.005). Other studied aspects did not have any significant difference. Affixed guidelines, therefore, could solve the problem of list recording, but did not alter other items. Conclusion: This study showed that the interns had many problems in medical recording which would not be solved with attaching a standard medical recording checklist, and we must choose other methods to correct those errors

Kernel selection in statistical femur modeling

Proceedings en ligne dans HALhttps://hal.archives-ouvertes.fr/hal-02183893International audienceWe aim to contribute to the development, analysis, and assessment of the statistical femur model when combined with a set of different analytical kernel functions. Reported results demonstrate the superior performance of data-driven femur model (computed from a few femur examples) when combined with an anisotropic kernel. These femur models have great potential for surgery applications

Usefulness of CD45 density in the diagnosis of B-cell chronic lymphoproliferative disorders

Background: Although many B-cell chronic lymphoproliferative disorders (BCLPDs) including B-cell chronic lymphocytic leukemia (B-CLL) have characteristic clinical and biological features, the overlapping morphologic and immunophenotypic profiles of various BCLPDs, is still the main problem. Aim: Our aim was to evaluate the usefulness of CD45 expression in the immunological classification of BCLPDs. Setting and design: A prospective study was set in a university hospital to investigate the CD45 intensity, particularly in B-CLL. Materials and Methods: The expression of CD45 in 37 patients with BCLPD including typical B-CLL (Group I), atypical B-CLL and CLL/PLL (II), and hairy cell leukemia (HCL), B-prolymphocytic leukemia (B-PLL), and B-non Hodgkin′s lymphoma (B-NHL) as non-CLL BCLPDs (III) and in eight healthy age matched controls (IV) was quantitatively compared by flow cytometric CD45/RALS gating strategy. Statistical analysis: The mean, median, and peak channel scores of CD45 obtained for the four groups were compared using one-way analysis of variance test. A P value < 0.05 was to be considered statistically significant. Results: Lower CD45 density is associated highly with typical CLL and differences between typical CLL and other groups were significant (P>0.001, 0.001, and 0.001). Non-CLL cases had significantly brighter CD45 expression than atypical CLL (P=0.014). No differences were found between normal lymphocytes and non-CLL BCLPD cases. Conclusions: CD45 is a useful marker, to discriminate the typical CLL from the non-CLL BCLPD and from atypical CLL

Human resource planning guidance Planning for care : guidance to assist local authorities and partner organisations in Wales to draw up effective human resource plans for the social care sector in their area

Added t.p.in Welsh: Canllaw cynllunio adnoddau dynol : cynllunio ar gyfer gofal : canllawiau i gynorthwyo awdurdodau lleol a sefydliadau sy'n bartneriad yng Nghymru i lunio cynlluniau adnoddau dynol effeithiol ar gyfer y sector gofal cymdeithasol yn eu hardal. Parallel text in English and Welsh, printed tete-becheAvailable from British Library Document Supply Centre- DSC:m03/27797 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo