Pedestrian Intention Detection as a Resource Competition Challenge

We propose an approach to classify possible interaction types between autonomous vehicles and pedestrians based on the idea of resource competition in shared spaces. Autonomous vehicles are more challenged in urban traffic scenarios as lots of uncertainties influence the current world model. Urban environments impose very little constraints on the motion of pedestrians. This creates the demand for an approach to determine intentions for each pedestrian as far ahead as possible and to react to changes early. A motion model based on goal-driven pedestrian movement shows a set of most likely planned trajectories. These are analyzed for overlapping occupation times in road segments, thus interactions with the vehicle. The output is an early estimation which suggests most probable interaction types and places. From this estimation, current trajectory of the pedestrian is used to refine the prediction of the most probable intention of interaction place and type. In the end the algorithm combines topological and behavioral input to infer and validate long term intention of interaction type before being able to actually infer the interaction from current dynamics.In terms of a proof-of-concept, the applicability of the approach is validated on real world scenarios from the Cityscapes data set.