Abstract:

System parameters of autonomous vehicles need to be identified and learned during operation to solve the problem that autonomous vehicles encounter many uncertainties that change with time, operating conditions, and environments. By capturing system behavior in a closed-loop setting and using data to learn the
related parameters, system reliability and robustness can be quantitatively established. This paper focuses on a basic scenario of an autonomous vehicle following its front vehicle. By integrating control actions with vehicle dynamics, a learning algorithm using operational data and confidence ellipsoids was employed to support robustness and reliability. A simulation case study was used to illustrate the strategies. The results show the proposed method can estimate the vehicle’s parameters accurately.

Key words: vehicle control , autonomous vehicle , identification of parameters, l earning, robustness