Understanding PedestrianVehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis 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 otheru0027s motion. In order to create robust methods to reason about pedestrian behavior and to design interfaces of communication between selfdriving cars and pedestrians we need to better understand such interactions. In this paper, we present a datadriven approach to implicitly model pedestriansu0027 interactions with vehicles, to better predict pedestrian behavior. We propose a LSTM model that takes as input the past trajectories of the pedestrian and egovehicle, and pedestrian head orientation, and predicts the future positions of the pedestrian. Our experiments based on a realworld, 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.

Forecasting Pedestrian Trajectory with MachineAnnotated Training Data Reliable anticipation of pedestrian trajectory is imperative for the operation of autonomous vehicles and can significantly enhance the functionality of advanced driver assistance systems. While significant progress has been made in the field of pedestrian detection, forecasting pedestrian trajectories remains a challenging problem due to the unpredictable nature of pedestrians and the huge space of potentially useful features. In this work, we present a deep learning approach for pedestrian trajectory forecasting using a single vehiclemounted camera. Deep learning models that have revolutionized other areas in computer vision have seen limited application to trajectory forecasting, in part due to the lack of richly annotated training data. We address the lack of training data by introducing a scalable machine annotation scheme that enables our model to be trained using a large dataset without human annotation. In addition, we propose Dynamic Trajectory Predictor (DTP), a model for forecasting pedestrian trajectory up to one second into the future. DTP is trained using both human and machineannotated data, and anticipates dynamic motion that is not captured by linear models. Experimental evaluation confirms the benefits of the proposed model.