Environmental sensors, such as camera, LiDAR, radar or ultrasound, are key to many functions in automated driving, and provide the information basis for critical vehicle functions. In this context, different sensor types have different strengths and challenges. However, the sensors are not only important for detecting the environment. They can also be used in the interior for detecting critical driver conditions and monitoring the interior situation for safety and comfort functions and for providing services.
Ambient sensor technology also plays an essential role in the generation of high-precision and georeferenced map data.
In addition to classical methods, modern machine learning methods, especially from deep learning, are increasingly being used for their evaluation. Due to the increasing complexity, there is a growing need for data and test methods to achieve and, in particular, prove robust evaluation.