dc.contributor.author | Huang, Xin | |
dc.contributor.author | McGill, Stephen G | |
dc.contributor.author | DeCastro, Jonathan A | |
dc.contributor.author | Fletcher, Luke | |
dc.contributor.author | Leonard, John J | |
dc.contributor.author | Williams, Brian C | |
dc.contributor.author | Rosman, Guy | |
dc.date.accessioned | 2021-10-27T19:52:59Z | |
dc.date.available | 2021-10-27T19:52:59Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/1721.1/133463 | |
dc.description.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. | |
dc.language.iso | en | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.isversionof | 10.1109/LRA.2020.3005369 | |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
dc.source | arXiv | |
dc.title | DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.relation.journal | IEEE Robotics and Automation Letters | |
dc.eprint.version | Author's final manuscript | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2020-07-29T17:45:43Z | |
dspace.orderedauthors | Huang, X; McGill, SG; DeCastro, JA; Fletcher, L; Leonard, JJ; Williams, BC; Rosman, G | |
dspace.date.submission | 2020-07-29T17:45:46Z | |
mit.journal.volume | 5 | |
mit.journal.issue | 4 | |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | |