Pedestrian´s Trajectory Forecast in Public Traffic with Artificial Neural Networks

This paper focuses on forecasting of pedestrian´s short-time trajectories up to 2.5 s for traffic safety applications. We present a self-learning approach based on artificial neural network movement models and compare it to traditional constant velocity Kalman Filter prediction and extrapolation of polynomials fitted using a least-squares error. Trajectories of uninstructed pedestrians in public traffic at a real urban intersection are acquired by a wide angle stereo camera setup in combination with a 3D head tracking framework. Results using this real-world data show that the artificial neural network significantly improves forecast quality compared to other approaches especially for critical traffic scenes including velocity changes such as starting and stopping. For those velocity changes a reduction of position estimation errors of about 21% compared to the Kalman Filter and to extrapolation of polynomials is obtained. By means of a concrete pedestrian-vehicle scenario we demonstrate the benefit of the proposed approach for an advanced driver assistant system in terms of reaction time.