Drivetech - Blog by DrivetechDrivetech - Blog by Drivetechhttps://www.drivetech-ai.in/blogs/author/DrivetechThu, 07 May 2026 16:16:17 +0530http://zoho.com/sites/<![CDATA[How StellarAi's Advanced 'Event-based Search' helped an OEM locate a crucial problem from their big data.]]>https://www.drivetech-ai.in/blogs/post/StellarAi-Event-based-SearchThe Problem   While working closely with one of our OEM customers, DriveTech was tasked diagnosing a critical issue they were facing with thei ]]>

The Problem 
While working closely with one of our OEM customers, DriveTech was tasked diagnosing a critical issue they were facing with their proto vehicle fleet. Essentially the requirement was to collect network data from affected vehicles, analyze it and come up with the correct diagnosis in a week.  

 

The Solution 

DriveTech installed data logger devices and recorded the CAN network data from the vehicles and their multiple networks. This data acquisition was done for a week in hope to catch a replication of the failure event on the CAN log. During this time the vehicles were routinely driven for 100 to 150 kms every day, generating large amounts of data. The devices captured multiple failure events, and now the data had to be analyzed.  This case was very critical and demanded urgent resolution as the failure was adversely affecting the test program. DriveTech was left with ~ 100+ GBs of data (500+ files) which had to be analyzed. Each file would take around 45 minutes for analysis and analyzing 500+ files would mean spending a month or more just isolating the events. 

The Turning Point 
     To specifically address this, we decided to leverage our in-house Data Analytics platform, StellarAi. This platform excels at handling vast amounts of data with minimal processing time. The standout feature that proved invaluable in this case was the Advanced ‘Event-based’ Search functionality. We were looking for specific events within this huge amount of CAN data, and the advanced event-based search needed only a 1-line query to locate the files containing failure events in less than a minute.      

We were able to quickly visualize the data from these files, which helped us pinpoint the precise instance when failure occurred. We were able to visualize multiple such files simultaneously which helped us narrow down the potential causes.                

Collective analysis of files from all the affected vehicles enabled us to precisely identify the cause of failure and come up with a diagnosis. We were also able to recreate the failure on a bench set up, validating our diagnosis. 

Conclusion  

After some more on-road testing & inspection, we were able to definitively conclude the reason for failure and notified the Automaker with potential insights. DriveTech was able to use StellarAi’s value added features and converge the analysis quickly. Without features such as event based Advanced Search it would have taken anyone a very long time to resolve the issue; while losing valuable time in getting new models into the market. 

]]>Sat, 04 Nov 2023 13:41:00 +0530<![CDATA[Digitizing Vehicle End-of-Line Activities For Enterprise Level Management]]>https://www.drivetech-ai.in/blogs/post/digitizing-vehicle-end-of-line-activities-for-enterprise-level-managementBackground:       DriveTech specializes in providing Managed Services to automotive OEMs for their Vehicle End-of-Line (EoL) st ]]>

Background:     

DriveTech specializes in providing Managed Services to automotive OEMs for their Vehicle End-of-Line (EoL) stabilization activities. Our dedicated teams work on-site at the client's manufacturing plants, where they conduct software validations for both prototype and pre-SOP electric and internal combustion engine (ICE) vehicles at the various EoL process stages. 


A critical challenge we identified was that most of the EoL data was maintained manually, primarily communicated through channels such as WhatsApp, Teams Chats, or emails. The lack of data consolidation hindered the OEMs from achieving traceability, a crucial aspect of data collection, management, and analysis. 

The Solution:  

Recognizing the need for a more efficient approach, we turned to our in-house Data Analytics platform, StellarAi. However, a key prerequisite was to digitize and structure the EoL data into a machine-readable format suitable for processing by StellarAi. To achieve this, DriveTech developed the End-of-Line Management Suite (EMS), a customized data entry application accessible on various platforms (Android / iOS / web). 


The development of EMS involved meticulously mapping the workflow for each client, accounting for specific constraints and schedules on a case-by-case basis. The EMS is designed to accommodate both standardized workflows and ad-hoc activities; while ensuring ease of adoption. The central advantage of consolidating EoL data through EMS is providing near real-time visibility into each shift's status for the client. Furthermore, EMS instances are configured to generate reports tailored to the client's specifications.

The game-changer is the seamless integration of EMS with StellarAi. This transformation empowers OEMs to harness manually recorded data for invaluable insights, and for managing it on an enterprise level. Clients can now access detailed insights with a few clicks, including but not limited to: 

- Monthly average percentages of passed and failed vehicles. 

- Identification of common Diagnostic Trouble Codes (DTCs) and potential causes. 

- Average time spent by a prototype vehicle in various EoL stages. 

Remarkably, DriveTech was able to achieve this integration in an incredibly short timeframe of just two weeks (one week for development and one week for implementation) for one of our clients.  

Conclusion:            

Solutions like the EMS & StellarAi are helping automotive OEMs build their own ERP System for Automotive Testing; bringing their entire Vehicle testing activities online and effectively managing them on an enterprise level. Taking a cue from our experience with EoL activity digitization, DriveTech is developing multiple such solutions that can integrate with StellarAi and provide more control to the OEMs. 

]]>Thu, 26 Oct 2023 12:50:31 +0530<![CDATA[Merge and Plot Billions of Data Points; On-the-Fly, No Pre-processing]]>https://www.drivetech-ai.in/blogs/post/merge-and-plot-billions-of-data-points-on-the-fly-no-pre-processingIntroduction: DriveTech was recently working on client project which involved Durability testing for a fleet of vehicles. It generated huge amounts of ]]>
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.
]]>Fri, 20 Oct 2023 17:29:40 +0530<![CDATA[Maximizing The Value Of Automotive Test Data Through Effective Data Management ]]>https://www.drivetech-ai.in/blogs/post/Maximizing-The-Value-Of-Automotive-Test-Data-Through-Effective-Data-ManagementIntroduction Efficient data management is the linchpin to unlock the true potential of automotive test data. Without a system driven approach, centrali ]]>

Introduction

Efficient data management is the linchpin to unlock the true potential of automotive test data. Without a system driven approach, centralized management of automotive test data is heavily dependent on human intervention making it an uncertainity.

The Significance of Automotive Test Data Management

When data is scattered across multiple locations, its full potential remains untapped. OEMs invest significantly in collecting test data, and effective data management ensures this valuable resource is fully utilized. By centralizing data, OEMs gain easy access, traceability of historical data, and comprehensive analysis capabilities, enabling them to make informed decisions, optimize processes, and extract actionable insights for enhanced vehicle development.

Maximizing the Value with StellarAi

StellarAi by DriveTech focuses on Data Consolidation and Management as one of its core functions. With StellarAi, you can easily centralize project data in a secure data warehouse. The web platform also offers the ability to organize data into separate datasets and arrange files in the desired sequence. This system consolidated data can be shared exclusively with the relevant team members, eliminating the need to physically handle large amounts of data.

StellarAi also seamlessly integrates with your existing DAQ tools, allowing you to gather data from specified locations remotely. It also comes with a built-in feature for automated analysis based on this data. (Read more about it here).

Conclusion

Effective automotive test data management is crucial for leveraging the full value of data investments. StellarAi provides the tools and capabilities to systematically centralize and optimize data management, driving better outcomes in vehicle development.

]]>Wed, 11 Oct 2023 19:38:32 +0530<![CDATA[Automotive Test Data Analytics: Automate for Efficiency]]>https://www.drivetech-ai.in/blogs/post/Automotive-Test-Data-Analytics-Automate-for-EfficiencyIntroduction In the fast-paced automotive industry, the analysis of test data holds immense importance for efficient vehicle development. The need for ]]>
boosting-efficiency

Introduction


In the fast-paced automotive industry, the analysis of test data holds immense importance for efficient vehicle development. The need for automation in this domain has become increasingly critical, as manual methods struggle to handle the growing data volumes, leading to productivity challenges and hindered operational efficiency.


The Impact of Manual Methods on Productivity

Relying solely on manual analysis methods in automotive test data analytics can significantly hamper productivity. Data collection, organization, and processing become time-consuming and error-prone, resulting in delays and inefficiencies. Analysts spend substantial time on repetitive tasks, limiting their capacity for higher-value activities. This lack of automation not only slows down the analytics process but also inhibits data-driven decision-making, impeding the overall efficiency of the organization.

The Power of Automation in Test Data Analytics

Automation is the key to overcoming the limitations of manual methods in automotive test data analytics. By automating data collection and processing, OEMs can streamline workflows and free up valuable analyst time. Real-time monitoring capabilities enable prompt anomaly detection, enhancing safety and reliability. Automation improves productivity, reduces costs, and fosters standardized processes, ultimately bolstering OEMs' efficiency and competitiveness.


Introducing StellarAi: Revolutionizing Automotive Test Data Analytics


DriveTech's StellarAi web-platform steps up to address these challenges and more. It empowers users to automate their repetitive data analysis tasks effortlessly. StellarAi allows the replication of analysis preferences, expediting results generation across various file groups. Its 'Datasets' feature seamlessly links data sources with individual repositories, automatically updating analysis results upon receiving new files from the source. StellarAi comes with a host of other value added features that help the analyst reduce time spent on manual work, and converge the analysis quickly.


Conclusion

Automation has become a necessity in automotive test data analytics, as manual methods hinder productivity and efficiency. Solutions like StellarAi pave the way for streamlined processes, improved decision-making, and heightened OEM performance.

]]>Fri, 06 Oct 2023 12:06:53 +0530