A Cognitively Inspired Framework to Support the Driving Task of Vehicles of the Future

Abstract

In this work an artificial driving agent able to adapt its behavior depending on the specific situation and to generate human-like maneuvers was developed. The agent, called co-driver, was designed with a bio-inspired architecture, with an approach that takes advantage of ideas from cognitive science. In fact the decision process is based on the affordance competition hypothesis: the agent generates a representation of all possible actions given what is perceived in the environment and then chooses the optimal maneuver after inhibiting all the dangerous ones. In the first stage of the development process, atomic actions, called motor primitives, were identified. Then the co-driver was implemented with a layered architecture, where these motor primitives were combined in the upper levels in order to obtain more complex behaviors. Thanks to this particular architecture, the development process of the final artificial driver was split in two parts: initially an advanced driver assistance system (ADAS) was implemented and tested, and finally the architecture was extended in order to have an artificial driving agent for automated driving. In the first case the system was successfully tested on public roads and was able to warn the driver in case of dangerous scenarios, such as blind intersections. In the latter case, the co-driver could manage several challenging test cases in urban scenarios, from car following to cut-in scenario and curves. Finally, it was compared with a state-of-art driver model of CarMaker simulation software used by the main car manufacturers. Due to the achieved results, this work can be considered a valid potential solution for automated driving. In fact, the artificial driver was used as starting point for a European project (“Dreams4Cars”) that is currently in progress.