Egocentric affordance fields in pedestrian steering

In this paper we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances - the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent's local space. This egocentric property allows us to efficiently compute a local space-time plan. We then use these perception fields to compute a fitness measure for every possible action, known as an affordance field. The action that has the optimal value in the affordance field is the agent's steering decision. Using our framework, we demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations.