Comparison of Pedestrian Prediction Models from Trajectory and
  Appearance Data for Autonomous Driving
Anthony Knittel Morris Antonello John Redford Subramanian Ramamoorthy
The ability to anticipate pedestrian motion changes is a critical capability
for autonomous vehicles. In urban environments, pedestrians may enter the road
area and create a high risk for driving, and it is important to identify these
cases. Typical predictors use the trajectory history to predict future motion,
however in cases of motion initiation, motion in the trajectory may only be
clearly visible after a delay, which can result in the pedestrian has entered
the road area before an accurate prediction can be made. Appearance data
includes useful information such as changes of gait, which are early indicators
of motion changes, and can inform trajectory prediction. This work presents a
comparative evaluation of trajectory-only and appearance-based methods for
pedestrian prediction, and introduces a new dataset experiment for prediction
using appearance. We create two trajectory and image datasets based on the
combination of image and trajectory sequences from the popular NuScenes
dataset, and examine prediction of trajectories using observed appearance to
influence futures. This shows some advantages over trajectory prediction alone,
although problems with the dataset prevent advantages of appearance-based
models from being shown. We describe methods for improving the dataset and
experiment to allow benefits of appearance-based models to be captured.