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The ability to anticipate pedestrian motion changes is a critical capabilityfor autonomous vehicles. In urban environments, pedestrians may enter the roadarea and create a high risk for driving, and it is important to identify thesecases. Typical predictors use the trajectory history to predict future motion,however in cases of motion initiation, motion in the trajectory may only beclearly visible after a delay, which can result in the pedestrian has enteredthe road area before an accurate prediction can be made. Appearance dataincludes useful information such as changes of gait, which are early indicatorsof motion changes, and can inform trajectory prediction. This work presents acomparative evaluation of trajectory-only and appearance-based methods forpedestrian prediction, and introduces a new dataset experiment for predictionusing appearance. We create two trajectory and image datasets based on thecombination of image and trajectory sequences from the popular NuScenesdataset, and examine prediction of trajectories using observed appearance toinfluence futures. This shows some advantages over trajectory prediction alone,although problems with the dataset prevent advantages of appearance-basedmodels from being shown. We describe methods for improving the dataset andexperiment to allow benefits of appearance-based models to be captured.

Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving