  Autonomous systems are composed of several subsystems such as mechanical,
propulsion, perception, planning and control. These are traditionally designed
separately which makes performance optimization of the integrated system a
significant challenge. In this paper, we study the problem of using
gradient-free optimization methods to jointly optimize the multiple domains of
an autonomous system to find the set of optimal architectures for both hardware
and software. We specifically perform multi-domain, multi-parameter
optimization on an autonomous vehicle to find the best decision-making process,
motion planning and control algorithms, and the physical parameters for
autonomous racing. We detail the multi-domain optimization scheme, benchmark
with different core components, and provide insights for generalization to new
autonomous systems. In addition, this paper provides a benchmark of the
performances of six different gradient-free optimizers in three different
operating environments.
  Our approach is validated with a case study where we describe the autonomous
vehicle system architecture, optimization methods, and finally, provide an
argument on gradient-free optimization being a powerful choice to improve the
performance of autonomous systems in an integrated manner.
