The Library
Multimodal manoeuvre and trajectory prediction for automated driving on highways using transformer networks
Tools
Mozaffari, Sajjad, Sormoli, Mreza Alipour, Koufos, Konstantinos and Dianati, Mehrdad (2023) Multimodal manoeuvre and trajectory prediction for automated driving on highways using transformer networks. IEEE Robotics and Automation Letters, 8 (10). pp. 6123-6130. doi:10.1109/lra.2023.3301720 ISSN 2377-3766.
|
PDF
WRAP-multimodal-manoeuvre-trajectory-prediction-automated-driving-highways-using-transformer-networks-2023.pdf - Accepted Version - Requires a PDF viewer. Download (1134Kb) | Preview |
Official URL: https://doi.org/10.1109/lra.2023.3301720
Abstract
Predicting the behaviour (i.e., manoeuvre/trajectory) of other road users, including vehicles, is critical for the safe and efficient operation of autonomous vehicles (AVs), a.k.a., automated driving systems (ADSs). Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for a vehicle in a given driving scene. Therefore, multimodal prediction can provide richer information than single-mode prediction, enabling AVs to perform a better risk assessment. To this end, we propose a novel multimodal prediction framework that can predict multiple plausible behaviour modes and their likelihoods. The proposed framework includes a bespoke problem formulation for manoeuvre prediction, a novel transformer-based prediction model, and a tailored training method for multimodal manoeuvre and trajectory prediction. The performance of the framework is evaluated using three public highway driving datasets, namely NGSIM, highD, and exiD. The results show that our framework outperforms the state-of-the-art multimodal methods in terms of prediction error and is capable of predicting plausible manoeuvre and trajectory modes.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | T Technology > TL Motor vehicles. Aeronautics. Astronautics | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Automated vehicles -- Design and construction, Automated vehicles -- Collision avoidance systems, Automated guided vehicle systems, Embedded computer systems, Mathematical optimization -- Industrial applications, Machine learning | ||||||
Journal or Publication Title: | IEEE Robotics and Automation Letters | ||||||
Publisher: | IEEE | ||||||
ISSN: | 2377-3766 | ||||||
Official Date: | 3 August 2023 | ||||||
Dates: |
|
||||||
Volume: | 8 | ||||||
Number: | 10 | ||||||
Page Range: | pp. 6123-6130 | ||||||
DOI: | 10.1109/lra.2023.3301720 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Re-use Statement: | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 14 August 2023 | ||||||
Date of first compliant Open Access: | 15 August 2023 | ||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year