About Session
Advanced driver assistance systems are evolving rapidly towards increasingly automated driving functions. Combining cameras, radar, lidar and ultrasonic sensors creates growing demands on high-performance vehicle controllers, which must process massive data streams within milliseconds to support reliable perception and driving decisions.
As more development and validation activities move from the road into the laboratory, real-world data remain essential for validating models, refining algorithms and assessing system behaviour. Engineers need flexible tools that support data acquisition and analysis across the development lifecycle—from early prototypes to series production and software updates after the start of production. These tools must accommodate data rates ranging from a few megabytes to several gigabytes per second while fitting into collaborative workflows between OEMs and suppliers.
At VCARSYSTEM, we address these challenges with a modular, scalable approach connecting in-vehicle measurement with laboratory testing. We show how PCIe-based architectures enable high-bandwidth data acquisition and how the same measurement hardware can be reused for data replay and integrated into hardware-in-the-loop environments.
With this approach, we support AI-assisted development workflows by making recorded sensor and vehicle data available for training-data preparation, model evaluation and systematic error analysis. Through targeted data injection and repeatable replay, we enable engineers to compare AI model versions under consistent input conditions, investigate challenging scenarios and assess how model changes affect the vehicle controller’s behaviour. Combined with automated testing and HIL integration, we connect data-driven development with validation on the target hardware, establishing a repeatable feedback process from real-world observations to model improvement and system-level assessment.
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