ABSTRACT
This chapter focuses on rule-based and artificial intelligence-based decision-making and on model-based and model-free motion planning. It gives a detailed overview of the two function clusters and the respective layers of hierarchy within the clusters. The chapter introduces concrete algorithms for motion planning on the operational layer. The focus of the chapter is presenting methods that handle, in a safe manner, decisions based on uncertain and incomplete information. Based on the stochastic process, the focus is on information-based cost functions from which the underlying optimization task can be derived. Since automated vehicles have to deal with a nondeterministic, highly dynamic environment, planning, executing a plan, and revising a plan might also be tightly interwoven-especially on the lower hierarchy layers. The normative decision problem can be described mathematically such that there is a choice of various actions and the environment is presented with the set of states.
