A Proxemic Potential Field Approach for Modeling Interactions Between Autonomous Vehicles with Pedestrians and Cyclists

Autonomous vehicles (AVs) are designed to drive in urban areas with a high volume of people on the street. In these locations, the safety of people around and inside the vehicle relies on its capacity to react to possible collisions. Typically, the motion of the obstacles involved follows a static or well-known trajectory, but the people instead can change freely their movement with time, therefore, AVs should consider them as dynamic obstacles. In this work a proxemics-based model is developed to represent in real time the interaction zones, between AVs and pedestrians or cyclists, through elliptic regions depending on instantaneous motion parameters. The model was implemented in simulations involving pedestrians and cyclists walking in street-based scenarios. The results indicate that the proposal allows to detect and break a vehicle in response to a possible collision.