Automotive E/E architecture is getting transition from distributed ECUs to high performing Zonal architectures. Traditional diagnostic methodologies based on Unified Diagnostic Services (UDS/ISO 14229) is being followed for providing failure analysis. It is difficult when debugging complex software and to find issues where lies exactly at modular level called “stack trace”. This could provide solutions for software bugs, failures or even container-level crashes in Software-Defined Vehicle (SDV).
This paper provides a remote diagnostic framework that makes use of the ISO 17978 (SOVD) standard to visualize a “Vehicle-wide Crashlytics” as analogous to modern mobile application analytics with Firebase SDK. Also, the comparison of failure management with traditional diagnostic method to handling fault management in remote framework with both ISO 17978 (SOVD) and ISO 20077/20078 (Remote Diagnostic) is made. It also speaks how methodology can be formed for software logs capturing and traces execution triggered by function software level anomalies. Also, the constrains such as V2X bandwidth and data is monitored and controlled with Diagnostic Saliency Algorithm that helps to determine only relevant telemetry is transmitted via 5G or V2N.
This framework proposal is checked with a simple application failure scenario, which leads to the reduction in “Mean Time to Repair” (MTTR) for software bugs. The reduction in MTTR is achieved since actionable root cause of software issue is shared directly from vehicle’s run-time environment. Future development of vehicle functions (SaaS) is expected to be integrated with SOVD and cloud-native observability patterns. This is much needed towards the scalable and quick development and deployment of applications into Vehicle as Digital Hub.