This article addresses the closed-loop approach to virtual AR-HUD development and examines the key benefits that automotive software engineers can get from it.
This article looks at certain use cases to better understand how machine learning (ML) and deep learning (DL) technologies are used to build advanced systems for different levels of vehicle automation.
Check out what it takes to build a highly effective digital cockpit solution and how it can benefit both car makers and Tier1 providers.
Hardware-in-the-Loop (HIL) Automation Testing Client: global manufacturer of agricultural equipment Technology SW tools: MATLAB Simulink, Vector CANoe, TwinCAT Programming scripting automation languages: Python, CAPL HIL
Touchscreen testing in the Automotive industry – the age of the robots Manual and automated testing and the role robots can play As cars’ touch-screen
