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The use of design of experiment (DoE) and data-driven simulation has become state-of-the-art in engine development and base calibration to cope with the drastically increased complexity of today's engine ECUs (electronic control units). Based on the representation of the engine behavior with a virtual plant model, offline optimizers can be used to find the optimal calibration settings for the engine controller, e.g. with respect to fuel consumption and exhaust gas emissions. This increases the efficiency of the calibration process and reduces the need for expensive test stand runs. The present paper describes the application of Gaussian process regression, a statistical modeling approach with practical benefits in terms of achievable model accuracy and usability. The implementation of the algorithm in a commercial tool framework enables a broad use in series engine calibration.

The way to autonomous driving is closely connected to the capability of verifying and validating Advanced Driver Assistance Systems (ADAS), as it is one of the main challenges to achieve secure, reliable and thereby socially accepted self-driving cars. Hardware-in-the-Loop (HiL) based testing methods offer the great advantage of validating components and systems in an early stage of the development cycle, and they are established in automotive industry. When validating ADAS using HiL test benches, engineers face different barriers and conceptual difficulties: How to pipe simulated signals into multiple sensors including radar, ultrasonic, video, or lidar? How to combine classical physical simulations, e.g. vehicle dynamics, with sophisticated three-dimensional, GPU-based environmental simulations? In this article, we present current approaches of how to master these challenges and provide guidance by showing the advantages and drawbacks of each approach.