Deep‐learning‐based vehicle trajectory prediction: a review

Simple item page
dc.contributor.authorYin, Chenhui
dc.contributor.authorCecotti, Marco
dc.contributor.authorAuger, Daniel J.
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorJiang, Haobin
dc.date.accessioned2025-02-24T13:50:26Z
dc.date.available2025-02-24T13:50:26Z
dc.date.freetoread2025-02-24
dc.date.issued2025-01-01
dc.date.pubOnline2025-02-09
dc.description.abstractVehicle trajectory prediction enables autonomous vehicles to better reason about fast‐changing driving scenarios and thus perform well‐informed decision‐making tasks. Among different prediction approaches, deep learning‐based (DL‐based) methodologies stand out because of their capabilities to efficiently summarise historical data, infer nonlinear behavioural patterns from human driving data, and perform long‐horizon prediction. This work reviews the DL‐based methods that have shown promising results, organising them in terms of usage of the input data, separating the encodings of the target vehicle's historical data, surrounding vehicle's historical data, and road layout data. In particular, this paper explores the relationships between the scope of the prediction components and the input data formats, as well as the connections with other elements in the same prediction framework, including vehicle interaction and road scene mining. This information is crucial to understand complex architectural decisions and to provide guidance for the design of improved solutions. This work also compares the performance of the most successful prediction models, establishing that appropriate encodings of vehicle interactions and road scenes improve trajectory prediction accuracy, with the best performance achieved by attention mechanism and Transformer‐based models. Finally, this work discusses future research directions, including considerations for real‐time applications.
dc.description.journalNameIET Intelligent Transport Systems
dc.description.sponsorshipChina Scholarship Council
dc.description.sponsorshipThis work was supported in part by the China Scholarship Council under Grant No. 202108690001 and the Graduate Research and Innovation Projects of Jiangsu Province under Grant KYCX21_3334.
dc.identifier.citationYin C, Cecotti M, Auger DJ, et al., (2025) Deep‐learning‐based vehicle trajectory prediction: a review. IET Intelligent Transport Systems, Volume 19, 2025, Article number e70001
dc.identifier.eissn1751-9578
dc.identifier.elementsID564648
dc.identifier.issn1751-956X
dc.identifier.paperNoe70001
dc.identifier.urihttps://doi.org/10.1049/itr2.70001
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23508
dc.identifier.volumeNo19
dc.languageEnglish
dc.language.isoen
dc.publisherInstitution of Engineering and Technology (IET)
dc.publisher.urihttps://ietresearch.onlinelibrary.wiley.com/doi/10.1049/itr2.70001
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectartificial intelligence
dc.subjectautonomous driving
dc.subjectintelligent transportation systems
dc.subjectmotion estimation
dc.subjecttransportation
dc.subject4005 Civil Engineering
dc.subject40 Engineering
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectLogistics & Transportation
dc.subject4008 Electrical engineering
dc.titleDeep‐learning‐based vehicle trajectory prediction: a review
dc.typeArticle
dc.type.subtypeReview
dcterms.dateAccepted2025-01-24

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Deep‐learning‐based_vehicle_trajectory_prediction-2025.pdf
Size:
9.37 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Plain Text
Description:
Download

Collections

Staff publications (SATM)