Validation of vehicles with automated features through the detection of behavioral differences between humans and machines

The development of automated vehicles has not achieved the desired success over the past decade, despite numerous technological and methodological successes. Currently, no production-ready autonomous vehicle on the market can operate without a human driver in a wide range of regions and environmental conditions. Due to the high complexity of the systems and the varied traffic scenarios, development, and validation are challenging and time-consuming. With the aid of simulation technology, good validation in a scalable form can be performed during development. However, the current standards for traffic safety validation require a final validation under real conditions. In this work, a framework called Shadow System (SS) is presented, which independently captures the control commands of the human driver and the vehicle. This unique approach enables the detection of problems in automated systems by identifying behavioral differences. Up to a defined safety range allows the human driver and the vehicle to execute control commands without affecting each other. This makes it possible, for example, to determine how a human driver steers the vehicle compared to the automated system in a given traffic situation. The developed method enables safe use under real conditions, as control can be taken over directly by the human driver at any time. Scenarios identified as critical will provide information about the safety of the system during the validation process.