DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
Name
1911.12736.pdf
Description
Accepted version
Size
2.91 MB
Format
Adobe PDF
Checksum (MD5)
5d003fc06232b046bf43be68a44ae307
Author(s)
Huang, Xin
McGill, Stephen G
DeCastro, Jonathan A
Fletcher, Luke
Leonard, John J
Williams, Brian C
Rosman, Guy
Date Issued
2020
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 2016 IEEE. Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it-a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We first extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We then sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state-of-the-art prediction performance, while providing improved coverage of the space of predicted trajectory semantics.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2020.3005369
