Drivetech - Blog #Data Sampling

Integrating Test Management and Data Analytics for Automotive Testing

Fri, 24 Nov 2023 18:36:41 +0530

Introduction:

In the ever-evolving landscape of automotive development, engineers often grapple with numerous challenges, from analyzing test data to managing resources and ensuring seamless inter-departmental coordination. These hurdles can result in delays during the vehicle development cycle.

DriveTech, recognizing these pain points, offers a comprehensive solution – the 'Test Management Suite' (TMS). The TMS is a versatile cross-platform solution tailored to suit the unique structure and workflow of each organization. It seamlessly deploys on iOS, Android, and existing systems, fitting into any stage of the workflow. TMS is essentially a highly customizable platform that organizations can use to bring their test management online, in a very short amount of time.

TMS simplifies test scheduling, resource allocation, rig and operator management, lab announcements, notifications, downtime tracking, and daily dashboards. However, the true standout feature of TMS is its integration with DriveTech's data analytics platform, StellarAi. StellarAi revolutionizes testing by automating both the testing process and post-processing of test data. The StellarAi + TMS combination centralizes test data, allowing it to be merged, searched for specific events, and leveraged for automated data analysis. It also offers centralized DBC management, Message to Signal conversions, and benchmarking.

Collaboration is essential in any test program. StellarAi facilitates seamless & secure data sharing among concerned team members, ensuring that users can collaborate efficiently while maintaining the confidentiality of sensitive test data. The platform also streamlines report generation, automating many manual processes. With StellarAi, users can generate professional reports with just a few clicks. The solution is particularly well-suited for repetitive data analysis tasks, offering pre-built Analytical Templates that simplify report generation. Additionally, StellarAi can integrate with test benches to automatically fetch data after each test.

Conclusion:

The TMS + StellarAi solution empowers users to achieve efficient test management, resource allocation (both human and machine), test data consolidation, automated data analysis, collaboration, and automated report generation. TMS + StellarAi brings a wealth of value-added features, making it an indispensable tool in the automotive testing industry. To explore these features further, visit the link below.

Streamlining Network Analysis with StellarAi

Fri, 24 Nov 2023 18:36:41 +0530

Introduction:

Analysis and managing of post recorded CAN / CAN-FD message logs isn't everyone's cup of tea and requires skilled professionals. CAN / CAN-FD Log Analysis, whether at the ECU development level or the prototype vehicle level, involves delving into ECU architecture, testing, and validation. Still, most of the engineers are only able to do the analysis once the message files are converted into signal files. Currently, in the automotive market the analysis can be done only on single files, or you must concatenate all the files into one and then do the analysis of that file using special scripting. The time consumed in all these activities is too long and hence the solution to the problem is delayed. This also depends on how your PC is set up to run different software. At the end it is the OEM who suffers.

Solution:

Recognizing the need for a more efficient approach, we turned to our in-house Data Analytics platform, StellarAi. StellarAi empowers users to assess data quality before embarking on their analysis. It also promptly notifies users of any data loss within a file, which may occur due to bus or network issues. Upon uploading the file into the database, StellarAi conducts a quality check, enabling users to have confidence in their data before commencing analysis. Users can gain insights into channel-specific statistics, including message counts, unique IDs, and bus utilization. Further, StellarAi notifies the user regarding the various message types found in the file, such as Diagnostic messages, Negative responses, error frames, response and transmit messages.

Once the user has a comprehensive understanding of their data, they are better equipped to initiate their analysis, ultimately saving time and enabling them to determine where to commence or whether to discard certain data entirely. Interestingly, StellarAi allows users to manage several messages at once and do complex searches to identify particular criteria in a variety of files. Message to Signal conversion is also managed seamlessly with StellarAi. The Data Base Container Management function allows for centralized management of DBCs and allows conversion of messages into signals using the DBC(s), allowing for further analysis of the data.

Conclusion:

The range of value-added features built into StellarAi offer an end-to-end solution to the Users for CAN / CAN-FD Data Analysis as well as other kinds of automotive test data analytics. The precise nature of the solution resolves the actual pain-points faced by the User; saving time and improving efficiency.

Centralized DBC Management

Fri, 24 Nov 2023 18:36:41 +0530

The contemporary landscape of software-defined vehicles has given rise to increasingly complex vehicle E/E architectures. Such an environment makes it critical to ensure that the database container (DBC) files for each architecture are managed, made accessible and also safeguarded at the same time. The DBC files are the IP of the OEM and need to be secured. However, it is also necessary that the concerned teams within the OEM’s organization and their industry partners are able to access the DBCs and use them effectively to interpret network message files. In short, it is necessary to ensure the following:

Systematic management of DBCs (.dbc / .arxml).
Centralized control on the release / withdrawal of DBCs from circulation within the organization.
Concerned user groups are able to effectively use the DBCs to interpret message files.
Proprietary information contained within the DBCs is secured and shared only on a need-to-know basis.

Solution:

Recognizing the importance of the above, DriveTech is proud to introduce for the First time in the IVN world, ‘Centralized DBC Management and Distribution’ with its data analytics platform, StellarAi.

Our approach involves consolidating DBC management within StellarAi, allowing only a single user or administrator to release or withdraw DBCs. This enables the OEM to notify User groups with the release of new DBCs, mapped with the E/E architectures relevant to them. OEMs can also withdraw obsolete DBCs from circulation, ensuring exclusive ownership.

The DBC Management feature allows StellarAi users to apply DBCs to message files and retrieve signal data from them. Users can also view channel-wise and file-wise DBC matching percentages along with other statistics, providing a comprehensive understanding of DBC suitability. This also ensures that users do not spend time working with the wrong DBCs.

Further, the most important aspect of the feature is that StellarAi allows the use of DBCs without allowing Users to view the information contained within the DBCs or allowing them to download the DBCs. This ensures that access to DBCs is secured and DBC files themselves are not circulated physically; maintaining the sanctity of the DBC contents and enhancing the safety of the OEM’s IP.

Conclusion:

DBC Management capabilities have been offered by StellarAi for the first time in the IVN domain and the feature has no parallels anywhere in the automotive test data analytics domain. Using this feature, OEMs can ensure systematic management and control of DBCs on-par with the contemporary software version control methods. DBC Management thus enables OEMs to have absolute control on their hard-earned IPs and safeguard their business interests.

Merge and Plot Billions of Data Points; On-the-Fly, No Pre-processing

Fri, 20 Oct 2023 17:29:40 +0530

Introduction:

DriveTech was recently working on client project which involved Durability testing for a fleet of vehicles. It generated huge amounts of data on a daily basis, and at the end of each day it was expected to have an incremental and a cumulative analysis of the data till date.

The Client was essentially facing the following four challenges:

- Merging multiple signal files together.

- Prevent loss of data due to sampling.

- Prevent data duplication (caused by sampling and re-sampling of the original data).

- Sharing of files / data analysis with team members.

For this, DriveTech developed a solution that can perform all the above and more.

Our Research:

DriveTech collaborated with the Client’s teams to get a deeper understanding of the issues faced. The Client teams were running a sequence of road tests on one of their proto vehicles and were recording CAN data on the same. We noted that this was leading to generation of a lot of data files from each test. The testing team wanted to analyze all the files as a single data set. They converted the message files to signal files and tried to manually combine multiple files together. However, we discovered that this was leading their system to crash. This meant that they had to analyze each file individually. Further they were sampling the files at a standard sampling interval. We understood that whenever a different sampling interval was needed, new re-sampled files had to be painstakingly created causing duplication of data, increasing storage requirements, as also adding the time required for re-sampling the data.

To quantify this, on average, each 8-hour drive test generated ~ 20 signal files, each of size ~ 150 mb. Analyzing each file will require at least 1 man-hour. This means that analyzing 20 files would take 20 man-hours i.e., 2 to 3 working days; making individual file analysis laborious and impractical.

The Solution:

To cater to the requirements, DriveTech introduced a new feature to its automotive test data analytics platform – StellarAi; called ‘Dynamic Sampling’ (DS). Dynamic Sampling allowed users to visualize a group of files together as a single dataset. StellarAi slices the original consolidated data set to isolate the parameters to be visualized, and the time period of interest. This eliminates the need to handle the entire data set and reduces StellarAi's own computing requirements.

Dynamic Sampling helps StellarAi to intelligently set a fixed sampling interval, optimized based on the available data bandwidth and rendering muscle of the User's workstation. Users can visualize a signal parameter across the entire data set based on the fixed sampling interval. DS also enables Users to drill-down into a particular slice of the graph, as seen here below.

As the User drills down, Dynamic Sampling adjusts the view to display just that selected data window, freeing up computing resources, and effectively crop the data under study, to allow for lower sampling interval within the selected data window.

The result is an enhanced user experience where individuals can continue drilling down into specific events within the data. This means users can even reach a point where they are viewing the actual, non-sampled data. The DS approach also ensures that all users can view comprehensive overviews of massive amounts of data; reliably rendered in a deterministic period of time.

StellarAi equipped with DS, eliminated the need for manual sampling, keeping the original data intact and readily accessible. This also ensured no data duplication, no data loss due to sampling, and the ability to adjust the sampling interval on the fly to meet the User’s analytical needs.

Further, because of StellarAi’s centralized ‘Data Repository’ and its existing collaboration environment, Users were able to share data analysis with their colleagues and effectively collaborate with each other without having to physically share large amounts of data.

Conclusion:

Due to the modular architecture of StellarAi, DriveTech was able to quickly roll out the Dynamic Sampling, a new feature that effectively addressed Client’s challenges. It ensured that the Client is able to:

- Analyze multiple files together.

- Avoid manually sample / preprocess the data.

- Eliminate data duplication.

- Share their work with their colleagues easily.