Decision-making algorithm for self-driving vehicles Using diagnostics and prognostics for shortterm fault handling

Sammanfattning: A problem in self-driving vehicle (SDV) development is replacing human intuition in the diagnostic process. Some fundamental interactions between driver, service personnel, and system developer are hard to replace by onboard systems and processes. One solution to this problem is to have a staffed control tower that supports the vehicle’s decision-making. In this thesis, a decision-making process for short-term fault avoidance and uptime maximization was developed. A system architecture was proposed and implemented on the SVEA platform. By integrating the onboard system with a control tower, an increase in safe operation was achieved when the vehicle lacked knowledge. In addition, some critical interactions between SDV and control tower were tested: Diagnosis verification and plan correction. By communicating onboard data such as system warnings, symptoms, speed, and location, the vehicle could support the control tower in its decision-making. One conclusion from the thesis was that the SDV with a control tower lowered the threshold for vehicle autonomy. Also, it was shown that both vehicle safety and uptime could be considered in the route planning of SDV:s. In the future, the diagnostic and prognostic algorithms employed in the proposed architecture could be integrated with machine learning tools to update degradation models online. This could make their outputs more reliable and accurate and ultimately make the whole system more safe and reliable.