Pedestrian 3D Bounding Box Prediction

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained predictions (human body keypoints predicti...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Saadatnejad, Saeed, Yi Zhou Ju, Alahi, Alexandre
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 28.06.2022
Subjects
Coders
Encoders-Decoders
Pedestrians
Recurrent neural networks
Three dimensional models
Online AccessGet full text

Cover

More Information
Summary:Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained predictions (human body keypoints prediction), we focus on 3D bounding boxes, which are reasonable estimates of humans without modeling complex motion details for autonomous vehicles. This gives the flexibility to predict in longer horizons in real-world settings. We suggest this new problem and present a simple yet effective model for pedestrians' 3D bounding box prediction. This method follows an encoder-decoder architecture based on recurrent neural networks, and our experiments show its effectiveness in both the synthetic (JTA) and real-world (NuScenes) datasets. The learned representation has useful information to enhance the performance of other tasks, such as action anticipation. Our code is available online: https://github.com/vita-epfl/bounding-box-prediction
ISSN:2331-8422