Have you ever wanted to visualize the data from your vehicle, whether a car, bus, boat, tractor, drone, submarine, or tank? We did too! To put it another way, if your vehicle could speak, what would it say? The challenge of listening to your vehicle and interpreting its language is no small feat.
The most significant difference between this approach and the previous flow diagram is the use of FME from Safe Software Inc., which can be acquired from the Azure Marketplace. FME allows geospatial architects to integrate various type of geospatial data which includes CAD (for Azure Maps Creator), GIS, BIM, 3D, point clouds, LIDAR, etc. There are 450+ integration options, and can speed up the creation of many data transformations through its functionality. Implementation, however, is based on the usage of a virtual machine, and has therefore limits in its scaling capabilities. The automation of FME transformations might be reached using FME API calls with the use of Azure Data Factory and/or with Azure Functions. Once the data is loaded in Azure SQL, for example, it can then be served in GeoServer and published as a Web Feature Service (vector) or Web Mapping Tile Service (raster) and visualized in Azure Maps web SDK or analyzed with QGIS for the desktop along with the other Azure Maps base maps.
From the Kibana left navigation side panel, the Visualize, Canvas, and Maps apps will all allow users to visualize data from Elasticsearch. The Visualize app provides access to standard charts and graphs, as well as Kibana Lens. Canvas allows users to create infographic-style reports and presentations backed with live data and includes the ability to use more fine-grained formatting options like custom CSS elements. Elastic Maps lets users plot their geospatial data using Elasticsearch indices as unique layers in a single view.
To answer these questions, and make better economic decisions, we support innovation accounting by using leading indicators, actionable metrics focused on measuring specific early outcomes using objective data. Leading indicators are designed to harvest the results of development and deployment of a Minimum Viable Product (MVP). These indicators may include non-standard financial metrics such as active users, hours on a website, revenue per user, net promoter score, and more.
To test the hypothesis, we would like to find out if the sensor system can detect objects and if it is indeed fast enough for our purposes before we build the entire system. We could build a single sensor and basic data capture software to validate the distance between the object and speed of vehicle control system interface. As an MVP, suppose we mounted the sensor on the front of a car and connected it to a laptop within the vehicle. Next, we place several objects on a test track and drive the vehicle toward them. We could use the software to record information from the sensor as our leading indicator. We could also use software to measure how long it takes for the message to be generated and sent to the vehicle control system interface by using a mocking framework instead of waiting for the vehicle control system to be built. This would give us an early indication of compliance with our NFR. Figure 6 describes the leading indicators for this cyber-physical system.
IXQ is an automated collaboration tool that allows warfighters and analysts to rapidly obtain critical expertise across the intelligence community. IXQ harvests analytic activity data from intelligence production systems to form a high-fidelity profile of the unique skillsets and expertise of each analyst. Users can then search, discover, geospatially visualize, and connect with peers using the same temporal, geospatial, or contextual queries they might employ for products or reports. Warfighters and analysts use dynamically created, secure collaboration rooms to discuss their objective or problem set, with the capability to post files and links in addition to text. IXQ provides analysts and tactical customers with an unparalleled ability to rapidly discover and leverage subject matter expertise.
Leidos border protection solutions are geared to meet specific border security mission needs, now and in the future. We have more than 50 products and applications that can comprise an end-to-end border security solution from prediction to resolution. Our solutions cover the entire security spectrum - from unmanned vehicles, safety and security, and software/hardware to transportation, simulation, and geospatial applications.
The WITS is software-adaptable to support the full range of tracked and wheeled logistics, utility, and light armored vehicles in service today. The WITS is easy to mount on a variety of vehicles and targets making it one of the most adaptable and flexible Multiple Integrated Laser Engagement System (MILES) target products available today.
The Xpatch toolkit is used by the Air Force Research Laboratory and Defense Advanced Research Projects Agency (DARPA) for multiple radar simulation programs. There are over 9000 active licenses across the country in both industrial and government applications using the Xpatch toolkit to produce and analyze scattering data for realistic aircraft, missiles, ships, spacecraft, and ground vehicles.
But you can also master more specific elements like account, patient, provider, beneficiary, contract, claims, projects, movie, character, airports, aircraft, vehicles, sites, and more. It all depends on the business challenges with which you want to align your data.
MDM addresses the challenges associated with disparate applications that create, capture, and access data across multiple systems, applications, and channels. This includes SAP, Marketo, Salesforce, DemandBase, web portals, shipping systems, invoicing systems, contract systems, and more. With a trusted source of reliable, current data, organizations can get a better view of their products and suppliers, drive customer engagement, and offer a consistent experience to employees as well as customers.
Since the term "EDR" can be used to cover many different types of devices, we believe it is important to define the term for purposes of this research site. When we use the term EDR in this site, we are referring to a device installed in a motor vehicle to record technical vehicle and occupant information for a brief period of time (seconds, not minutes) before, during and after a crash. For instance, EDRs may record (1) pre-crash vehicle dynamics and system status, (2) driver inputs, (3) vehicle crash signature, (4) restraint usage/deployment status, and (5) post-crash data such as the activation of an automatic collision notification (ACN) system. We are not using the term to include any type of device that either makes an audio or video record, or logs data such as hours of service for truck operators. EDRs are devices which record information related to an "event." In the context of this site the event is defined as a highway vehicle crash.
In 1997, the National Transportation Safety Board (NTSB) issued recommendations to "pursue crash information gathering using EDRs." NASA's Jet Propulsion Laboratory, in April of the same year recommended that NHTSA "study the feasibility of installing and obtaining crash data for safety analyses from crash recorders on vehicles."
Project purpose: Many light-duty motor vehicles, and increasing numbers of heavy commercial vehicles, are equipped with some form of MVEDR. These systems, which are designed and produced by individual motor vehicle manufacturers and component suppliers, are diverse in function, and proprietary in nature. The continuing implementation of MVEDR systems provides an opportunity to voluntarily standardize data output and retrieval protocols to facilitate analysis and promote compatibility of MVEDR data. Adoption of the standard will therefore make MVEDR data more accessible and useful to end users.
NHTSA Request for Comments Regarding--Federal Motor Vehicle Safety Standards -- Event Data Recorders -- Document NHTSA-2002-13546-1 10/11/02ABSTRACT: Over the past several years, NHTSA has been actively involved with Event Data Recorders (EDRs) in motor vehicles. EDRs collect vehicle and occupant based crash information. The agency's involvement has included sponsoring two working groups, using data from EDRs in crash investigations, and conducting research and development. Particularly since the two working groups have completed their work, we request comments on what future role the agency should take related to the continued development and installation of EDRs in motor vehicles.FULL DOCUMENT
ABSTRACT: This report documents the findings of the Event Data Recorder (EDR) working group established by the NHTSA's Motor Vehicle Safety Research Advisory Committee. In 1997, the National Transportation Safety Board issued recommendations to pursue vehicle crash information-gathering using event data recorders. In early 1998, NHTSA's Office of Research and Development launched a new effort to form a working group comprised of industry, academia, and governmental organizations. The members of the working group participated in the forum to study the state-of-the-art of EDRs. Meetings were held on a regular basis, culminating in this EDR findings report.The following selected findings present the highlights of the report:
The T&B EDR WG focused its findings in three areas: data elements, survivability of the EDR data, and discussion on when data should be collected. Twenty-eight data elements were highlighted for inclusion in EDRs. These were subdivided as follows: 13 Priority 1 elements, 13Priority 2 elements, and 2 optional elements. Based on input from the WG members, manufacturers should focus on collecting the Priority 1 data elements and include Priority 2 data elements only as sensors to measure these characteristics become more commonplace, or as technology develops that would make them more feasible for large vehicles. 2b1af7f3a8