In the present scenario of Automotive Test Data Analytics, a wide variety of software platforms are available that help Users perform the various processes required to analyze their Data. Solutions are available for a range of use cases and problem statements. However, one functionality which still remains unaddressed, is the ability to effectively collaborate with your team while working on the test data. Users generally end up using multiple different software to generate analysis, and conventional channels such as email, shared drives and chats to communicate their findings with their colleagues and concerned approving authorities. Anyone who has been through this knows that it is a time-consuming and inefficient way to collaborate. Naturally, at DriveTech, we decided to change this.

DriveTech’s Data Analytics platform StellarAi is built with ‘Collaboration’ at its core; along with ‘Data Consolidation’ and ‘Automation’. Data Analysis done in StellarAi is segregated in individual Projects. All team members working on a Project would be assigned the said Project in StellarAi. The Test Data collected from various sources under a project is accessible to all the people working on it. Further, in StellarAi Users are able to work in two environments – a private space where they can generate analysis by processing the shared files, and a common environment shared with fellow project team. This common environment is known as Dashboard. A collaborative space where Users can share their work with their peers and colleagues and get their feedback on it. Concerned team leaders can also record their approval / dis-approval of the analysis shared by any User. 

Benchmarking stands as a conventional practice in the automotive industry, serving as a vital tool for OEMs to discern how their products measure up against competitors and to gain critical insights on market dynamics. Traditionally, the evaluation of vehicles leaned heavily on the mechanical aspects, but with the increasing software dependence of vehicles, the electronic and electrical (E/E) systems too have become important while benchmarking. 

This, however, poses a different set of challenges for the OEM. Achieving a meaningful comparison requires the meticulous evaluation of two vehicles under identical test conditions—a process known for its inherent time-consuming and labor-intensive nature. Adding to the complexity, contemporary software platforms frequently lack dedicated benchmarking capabilities. This makes it difficult for test engineers to effectively analyze the performance of their proto. Vehicles against benchmarking vehicles.  

DriveTech’s in-house data analytics platform, StellarAi provides an integrated ‘Compare’ feature that addresses the above challenges. This feature serves as a catalyst for engineers, enabling them to conduct efficient comparisons of parameters between the prototype vehicle and its corresponding benchmarks.   

Users benefit from the ability to overlay and slide parameter curves, effectively eliminating irrelevant data and facilitating the simultaneous comparison of multiple parameter sets. Further, StellarAi is the only platform that allows Users to merge and compare data from multiple test files for both the proto vehicle and benchmark vehicle(s). Users can choose to visualize parameters with either a pair of single ‘Multiple Y-axis' plots or multiple pairs of ‘Single Y-axis' plots. An additional noteworthy capability of StellarAi is its capacity to accommodate the comparison of data from up to four vehicles concurrently.  

The above functionality, combined with its standard automation capabilities, makes StellarAi the most suitable platform for analysis of benchmarking data. Its ‘Merge & Plot’ functionality allows for huge datasets to be combined and compared; whereas the ‘Automation’ feature helps users auto-generate comparison visualization from live tests.   

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. 

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.