ParallelNet: Multi-mode Trajectory Prediction by Multi-mode Trajectory Fusion

Level 5 Autonomous Driving, a technology that a fully automated vehicle (AV) requires no human intervention, has raised serious concerns on safety and stability before widespread use. The capability of understanding and predicting future motion trajectory of road objects can help AV plan a path that...

Full description

Saved in:
Bibliographic Details
Main Authors Wu, Fei, Chen, Luoyu, Lu, Hao
Format Journal Article
LanguageEnglish
Published 20.12.2022
Subjects
Computer Science - Computer Vision and Pattern Recognition
Online AccessGet full text
DOI10.48550/arxiv.2212.10203

Cover

More Information
Summary:Level 5 Autonomous Driving, a technology that a fully automated vehicle (AV) requires no human intervention, has raised serious concerns on safety and stability before widespread use. The capability of understanding and predicting future motion trajectory of road objects can help AV plan a path that is safe and easy to control. In this paper, we propose a network architecture that parallelizes multiple convolutional neural network backbones and fuses features to make multi-mode trajectory prediction. In the 2020 ICRA Nuscene Prediction challenge, our model ranks 15th on the leaderboard across all teams.
DOI:10.48550/arxiv.2212.10203