  Pedestrians and vehicles often share the road in complex inner city traffic.
This leads to interactions between the vehicle and pedestrians, with each
affecting the other's motion. In order to create robust methods to reason about
pedestrian behavior and to design interfaces of communication between
self-driving cars and pedestrians we need to better understand such
interactions. In this paper, we present a data-driven approach to implicitly
model pedestrians' interactions with vehicles, to better predict pedestrian
behavior. We propose a LSTM model that takes as input the past trajectories of
the pedestrian and ego-vehicle, and pedestrian head orientation, and predicts
the future positions of the pedestrian. Our experiments based on a real-world,
inner city dataset captured with vehicle mounted cameras, show that the usage
of such cues improve pedestrian prediction when compared to a baseline that
purely uses the past trajectory of the pedestrian.
