A framework for IP and non-IP multicast services for vehicular networks

International audienceEnabling drivers to be connected to the Internet and/or Vehicular Ad-hoc networks, is one of the main challenges of the future networking. This enables drivers to benefit from the existing Internet services as well as emerging ITS applications based on IP or non-IP communications (e.g geonetworking). Many of ITS applications such as fleet management require multicast data delivery. Existing works on this subject tackle mainly the problems of IP multicasting inside the Internet or geocasting in VANETs. This paper presents a new framework that enables Internet-based multicast services on top of VANETs. We introduce a self-configuring multicast addressing scheme based on the geographic locations of the vehicles coupled with a simplified approach that locally manages the group membership to allow packet delivery from the Internet. Moreover, we propose an approach that selects the appropriate network-layer protocol for either geocasting or IP multicasting depending on the vehicles' context and the application requirements. Finally, we present the integration of the designed framework to the ITS reference architecture

Monitoring and Adapting the Physical State of a Camera for Autonomous Vehicles

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. Cameras must maintain proper functionality and take automatic countermeasures if necessary. However, few works examine the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a generic and interpretable self-health-maintenance framework for cameras based on data- and physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (blur, noise phenomena and most common combinations) by comparing traditional and retrained machine learning-based approaches in extensive experiments. Furthermore, we demonstrate on a real-world ground vehicle how one can adjust the camera parameters to achieve optimal whole-system capability based on experimental (non-linear and non-monotonic) input-output performance curves, using object detection, motion blur and sensor noise as examples. Our framework not only provides a practical ready-to-use solution to evaluate and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines

Editorial: Akademische Kultur und Wissenschaftsfreiheit angesichts der Digitalisierung von Lehren und Lernen

Universitäres Lernen und Lehren, Gespräche im Studium, Künstliche Intelligenz, Plagiarismus und Wissenschaftsfreiheit sind Fragen, unter denen sich die Beiträge des Hefts Veränderungen der akademischen Kultur angesichts der Digitalisierung zuwenden. Was sich verändert, hat aber nicht aufgehört, sich zu verändern. Konstruktivistisch gesprochen, wagen die Autorinnen und Autoren eine Beobachtung zweiter Ordnung. Ob wir nur in einem Durchgangsstadium sind oder schon eine andere Stufe von Entwicklung erreicht haben, ist jedoch auch davon abhängig, mit welchem Bewusstsein die Digitalisierung weiter angegangen wird. Das Heft will einen Beitrag dazu leisten, die Herausforderungen der Zukunft unter neuen Blickwinkeln kritisch zu reflektieren und die Ad-hoc-Digitalisierung während der Pandemie wissenschaftlich zu durchdringen

A Framework for IP and non-IP Multicast Services for Vehicular Networks

International audienceEnabling drivers to be connected to the Internet and/or Vehicular Ad-hoc networks, is one of the main challenges of the future networking. This enables drivers to benefit from the existing Internet services as well as emerging ITS applications based on IP or non-IP communications (e.g geonetworking). Many of ITS applications such as fleet management require multicast data delivery. Existing works on this subject tackle mainly the problems of IP multicasting inside the Internet or geocasting in VANETs. This paper presents a new framework that enables Internet-based multicast services on top of VANETs. We introduce a self-configuring multicast addressing scheme based on the geographic locations of the vehicles coupled with a simplified approach that locally manages the group membership to allow packet delivery from the Internet. Moreover, we propose an approach that selects the appropriate network-layer protocol for either geocasting or IP multicasting depending on the vehicles' context and the application requirements. Finally, we present the integration of the designed framework to the ITS reference architecture

Camera Condition Monitoring and Readjustmentby means of Noise and Blur

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. Cameras must maintain proper functionality and take automatic countermeasures if necessary. However, there is little work that examines the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a generic and interpretable self-health-maintenance framework for cameras based on data- and physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (defocus blur, motion blur, different noise phenomena and most common combinations) by comparing traditional and retrained machine learning-based approaches in extensive experiments. Furthermore, we demonstrate how one can adjust the camera parameters to achieve optimal whole-system capability based on experimental (non-linear and non-monotonic) input-output performance curves, using object detection, motion blur and sensor noise as examples. Our framework not only provides a practical ready-to-use solution to evaluate and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines

A camera self-health-maintenance system based on sensor artificial intelligence

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. However, there are versatile undesirable states cameras can encounter. Hence, cameras must maintain proper functionality and take automatic countermeasures if necessary. Currently, there is only little work that examines the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a self-health-maintenance framework for cameras with focus on blur and noise, based on artificial intelligence and the incorporation of additional physical knowledge of the sensor (Sensor AI)

Validation and evaluation of NEMO in VANET using geographic routing

International audienceThe combination of geographic-based routing protocols (GeoNetworking) and IPv6 NEtwork MObility (NEMO) into a single communication architecture (IPv6 GeoNetworking) is key in Vehicular Ad-hoc Networks (VANET). While NEMO manages Internet access and session continuity between the vehicle and the Internet, geographically based data forwarding allows an efficient dissemination of the information between vehicles and the infrastructure. In this paper, we refer to the basic scenarios that led to the design of the IPv6 GeoNetworking architecture in the context of the GeoNet project. A prototype implementation of the modules that couple these two technologies is described, in particular the adaptation of IPv6 and C2CNet, a layer that ensures the geographic capabilities. Results of a light experimental performance evaluation are reported

Real-vehicle integration of driver support application with IPv6 GeoNetworking

International audienceOne of the essential usage of Intelligent Trans- portation Systems (ITS) applications is to provide road traffic information to vehicle drivers for road safety and efficient drive. For this usage, it is necessary to integrate geographical routing mechanisms in vehicular ad hoc network (VANET) into ITS applications. In this paper, we design and implement an ITS application which relies on IPv6 GeoNetworking; a geographical addressing and routing mechanism developed in the GeoNet project. Our application supports realistic use case scenarios, therefore we integrated it into INRIA's vehicular platform. The system has publicly been demonstrated in realistic scenarios

Seamless Navigation, 3D Reconstruction, Thermographic and Semantic Mapping for Building Inspection

We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling