Pedestrian Prediction by Planning Using Deep Neural Networks

Abstract

Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed planning. For this, we infer a mixture density function for possible destinations. We use these destinations as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The patterns are learned by a fully convolutional network operating on maps of the environment. We show that this entire system can be modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately.