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

by   Anthony Knittel, et al.

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.


page 1

page 2

page 4


Evaluating Pedestrian Trajectory Prediction Methods for the Application in Autonomous Driving

In this paper, the state of the art in the field of pedestrian trajector...

Intentions of Vulnerable Road Users - Detection and Forecasting by Means of Machine Learning

Avoiding collisions with vulnerable road users (VRUs) using sensor-based...

Learning Pedestrian Actions to Ensure Safe Autonomous Driving

To ensure safe autonomous driving in urban environments with complex veh...

Pedestrian Motion Model Using Non-Parametric Trajectory Clustering and Discrete Transition Points

This paper presents a pedestrian motion model that includes both low lev...

Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning

Pedestrian trajectory prediction plays an important role in autonomous d...

T2FPV: Constructing High-Fidelity First-Person View Datasets From Real-World Pedestrian Trajectories

Predicting pedestrian motion is essential for developing socially-aware ...

Subjective Vertical Conflict Model with Visual Vertical: Predicting Motion Sickness on Autonomous Personal Mobility Vehicles

Passengers of level 3-5 autonomous personal mobility vehicles (APMV) can...

Please sign up or login with your details

Forgot password? Click here to reset