Artificial Intelligence/Machine Learning Training Data-hungry AI Algorithms by Daniel Rödler Large-scale data refinement is key to bringing more sophisticated automated-driving functions to series production. a combined radar + camera sensor system. In the example, the correct functioning of the sensor system must be demonstrated over 300,000 km (about 186,400 mi) with a predefined mix of driving scenarios. The sensor system provides an object list that describes the vehicle's environment. To check if the sensor system object list is correct, a comparison list called "ground truth" is required. This list is generated using a reference sensor set consisting of a camera and lidar sensor. The reference sensor set is installed in the vehicle as a rooftop box in addition to the sensors to be checked. The data stream of the reference sensor set is transferred to the ground truth object list by means of an annotation process. By systematically Training the algorithms of Artificial Intelligence A practical example The following calculation example illustrates the data volumes that must be processed to validate AUTONOMOUS VEHICLE ENGINEERING dSPACE (AI)-based systems for autonomous or highly automated driving requires enormous volumes of data to be captured and processed. The algorithms must be able to master numerous challenges so that self-driving cars can detect all essential details of their environment, make the right decisions and safely take people to their destination. Why does training require this much data? AI-based systems enable quick progress, but this progress slows down after a certain point. It must be ensured that systems can also sensibly and reliably handle rare events. Bringing sophisticated AI-based driving functions to the road safely therefore requires a growing amount of ever higher-quality data. In general, AI functions in production systems must cross a very high reliability threshold before they can be used in real systems. This particularly applies to automated driving because the associated safety risks are extremely high. The tragic accidents involving Tesla and Uber drivers are admonishing reminders of this. Consequently, registration authorities require ever stricter validation measures for high-quality driving functions. Since these validation measures are often based on accumulating actual road mileage/kilometers, they require large volumes of data. Fig. 1: Learning progress over time. September 2020 13