Control Model of Automated Driving Systems Based on SOTIF Evaluation

In partially automated and conditionally automated vehicles, a part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality. Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks. In order to discover the causes of human misuse, this paper takes the trust feeling between the driver and the automated driving system as the starting point, and based on the collected data of track test, establishes the evaluation indicator -- degree of confidence to show the trust feeling between the driver and the automated system. Degree of confidence is a comprehensive interpretation of the driver's physical and psychological feelings. In the process of track test, we simultaneously collect the dynamics indicators of the vehicle. After the test, the drivers' driving feeling was evaluated by questionnaire. Then, the relationship between objective indicator and subjective score was established by machine learning method, and the development of evaluation indicator was completed. Finally, this paper optimizes the automatic driving motion planning algorithm based on this indicator, and verifies the effectiveness of the algorithm through simulation.