Complex self-driving behaviors emerging from affordance competition in layered control architectures

The deployment of autonomous driving technology is hindered by “corner cases”: unusual nuanced conditions that the self-driving software cannot understand and act fully. We argue that some corner cases originate from a “narrow AI” approach, which lacks the general knowledge that humans exploit when dealing with these cases. We propose an alternative that can be seen as a step toward features of Artificial General Intelligence. We exploit the biological principle of affordance competition in layered control architectures to create an artificial agent that realizes emergent, adaptive, and logical behaviors without programming case-specific rules or algorithms. We give six different examples of simple and complex emergent behaviors. For the case study of merge scenarios, we contrast the approach of this paper with an algorithmic solution of the literature. The ideas presented here (if not the whole agent’s sensorimotor organization) could be used to improve the robustness and flexibility of self-driving technology