Now that more people are traveling again, one of the headaches attached to it is the generally miserable process of returning rental cars. But rental giant Enterprise AXON is in the process of testing a system that would eliminate queuing up your car and waiting for an attendant. In fact, you could drop off the car at the entrance to the garage and walk away.
Enterprise is testing a system introduced last year by German auto supplier Bosch called Automated Valet Parking (AVP). The tests are taking place at the Detroit Smart Parking Lab located in a downtown Detroit parking garage. The lab is a project involving Bosch, Ford Motor Co. F , commercial real estate company Bedrock, which owns and operates the parking garage, the State of Michigan and operated by the Ypsilanti, Mich.-based American Center for Mobility.
Bosch has improved the scope of application of its diesel test benches allowing an even more flexible use and increased utilization. Authorized diesel experts equipped with Bosch EPS 708 and EPS 815 diesel test benches can now also test and maintain common-rail pumps by the Continental brand VDO thanks to two newly developed test kits. Depending on already existing workshop equipment, there are two different test kits available. CP VDO retrofit kit is designed for those workshops equipped with Bosch EPS 708 or EPS 815 diesel test benches and CP1, CP3, CP4 and CP third party test kits. The CP VDO Complete kit, however, only requires the Bosch CP4 test kit. Having been developed by both Bosch and VDO in close cooperation, the new test kits are approved by VDO for CR pumps.
Bosch provides the test plans including test values for VDO pumps on the TestData CD for EPS 708 and EPS 815. In order to unlock these data, an access code is required, which is only available for VDO Diesel Repair Service (DRS) Partners. This code has a validity of one year and can be ordered again once it expired. VDO provides the repair instructions as well as the spare parts required for pump repairs.
By means of the authorization, VDO assures only workshops with both the necessary equipment, the required know-how and original spare parts will test and maintain VDO pumps. Wholesalers can provide interested workshops with more information about the DRS partner concept.
Graphic summary of rapid antigen diagnostics development using cross-reactive antibodies. Step 1: generate red nanospheres and blue nanostars from gold salts; Step 2: immunization; Step 3: Confirmation of ELISA binding using monoplex lateral flow chromatography. The naming standard (e.g. 411D3) identifies the antibody name (411) and the Dengue virus serotype antigen (D3). mAb 411 recognizes D1 and D3 NS1; mAb 323 recognizes D1-D4 serotype NS1 proteins; mAb 55 recognizes DV3 and DV4 serotype NS1 proteins. Step 4: multiplexed lateral flow chromatography, creating the test signal patterns that detect and distinguish the viral antigens. Four lateral flow chromatography strips are shown, each with mAb323D3 adsorbed at the lower test area, and mAb411D3 adsorbed at the upper test area. The control area is anti-mouse IgG. For the flowed antibody conjugates, mAb323D3 was conjugated to gold nanospheres, and mAb55D3 was conjugated to blue nanostars. Step 5: The distribution of red and blue nanoparticle colors in the test areas is determined by red/green/blue (RGB) analysis (ImageJ, NIH). Step 6: The data are clustered using principal component analysis. Step 7: A confusion matrix evaluates the performance of the tests in detecting and distinguishing the four Dengue virus serotype by comparing the predicted class with the true class. The number 3 indicates the number of tests that were run, and numbers falling on the diagonal represent a perfect correlation of predicted and true classes.
Innovation in engineering is a constant lifecycle of build, test, launch, repeat. A global manufacturer like Bosch has seriously complex demands on its application development in support of engineering. Virtual machine instances help manage the workload but create storage and availability problems of their own.
In the automotive industry, hardware-in-the-loop (HIL) refers to a method of testing and validating complex software systems on specially equipped test benches that receive data inputs from physical devices such as radars and cameras.
As the automotive industry evolves toward the software-defined vehicle, where more features and functions are primarily enabled through software, automotive software developers are evolving their methods as well. The slow, incremental waterfall method has given way to continuous development (CD), continuous integration (CI) and continuous testing (CT), which speed up development, cut costs and improve the quality of the finished product.
A typical CI/CD/CT sequence consists of defining the requirements of the new software, generating code, performing software-in-the-loop (SIL) simulation testing, integrating the results into the continually evolving code base, and then conducting HIL testing and validation.
Performing manual testing is not practical, given the complexity of the software being developed. It is expensive and time-consuming to physically load software into an actual vehicle and test-drive it for the potentially hundreds of thousands of miles needed to make sure the software works in all types of driving conditions.
HIL testing entails simulating vehicle and environmental inputs for the electronic control unit (ECU) under test, causing it to believe that it is reacting to real-world driving conditions on the open road. The HIL bench contains all of the relevant vehicle components. A simulator presents inputs to actual cameras and radar systems, which in turn send signals to the system under test to see whether it responds correctly to the inputs.
For example, test scripts can create a scenario in which a vehicle traveling at 60 mph around a curve in the rain encounters an unknown object in the road or an oncoming car swerving across the center line. Cameras and radars attached to the HIL test bench send images to the ECU, and the system under test has to process that data in real time and decide the course of action to take.
Because HIL test benches are physical devices tied to a specific location, software development has historically been fragmented. Today, however, Aptiv is moving to a cloud-based, globally available architecture to enable central control of test benches remotely from anywhere in the world. Learn more in our white paper on the future of automotive software development.
Looking for a skilled V&V Engineer to work on innovative sensor solutions in an agile team and develop/execute/automate all aspects of verification and validation at the system level of software solutions, including test automation (CI/CD) and system tests.
Every change in the code must trigger a continuous integration process. This means that a CI system must be connected with a Git repository to detect when changes are pushed, so tests can be run on the latest revision.
Yes. CI/CD platforms have access to all kinds of sensitive data such as API keys, private repositories, databases, and server passwords. An improperly secured CI/CD system is a prime target for attacks and can be exploited to release compromised software or to get unauthorized access. A CI/CD platform must support mechanisms to securely manage secrets, and control access to logs and private repositories.
Testing is integral to and inseparable from CI. The main benefit teams get from CI is continuous feedback. Developers set up tests in the CI to check that their code behaves according to expectations. There would be no feedback loop to determine if the application is in a releasable state without testing.
Test-Driven Development (TDD) is a software design practice in which a developer writes tests before code. By inverting the usual order in which software is written, a developer can think of a problem in terms of inputs and outputs and write more testable (and thus more modular) code.
If TDD is about designing a thing right, Behavior-Driven Development (BDD) is about designing the right thing. Like TDD, BDD starts with a test, but the key difference is that tests in BDD are scenarios describing how a system responds to user interaction.
While writing a BDD test, developers and testers are not interested in the technical details (how a feature works), rather in behavior (what the feature does). BDD tests are used to test and discover the features that bring the most value to users.
End-to-end usually involves testing the application by using the UI to simulate user interaction. Since this requires the application to run in a complete production-like environment, end-to-end testing provides the most confidence to developers that the system is working correctly.
The confusion stems from the fact that acceptance testing implements the acceptance criteria verification with end-to-end testing. That is, an acceptance test consists of a series of end-to-end testing scenarios that replicate the conditions and behaviors expressed in the acceptance criteria.
Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power.
The availability of the large amount of data derived from the recent explosion in metagenomics and metatranscriptomics provides unique opportunities for investigation. However, it is sometimes difficult to identify informative species. Recently, machine learning algorithms have been successfully applied because they allow the identification of patterns in situations where large, multi-dimensional and heterogeneous datasets are available. 2b1af7f3a8