In this paper, we show how the 4D/RCS architecture incorporates and integrates multiple types of disparate knowledge representation techniques into a common, unifying framework. 4D/RCS is based on the supposition that different knowledge representation techniques offer different advantages, and 4D/RCS is designed in such a way as to combine the strengths of all of these techniques into a common unifying architecture in order to exploit the advantages of each. 4D/RCS allow for the capability to capture knowledge in formalisms and at levels of abstraction that are suitable for the way that it is expected to be used. In the context of applying the architecture to the control of autonomous vehicles, we describe the procedural and declarative types of knowledge that has been developed and applied, and value that each brings to the achieving the ultimate goal of autonomous navigation. We also look at symbolic vs. iconic knowledge representation, and show how 4D/RCS accommodates both of these types of representations and uses the strengths of each to strive towards achieving human-level intelligence in autonomous systems.