A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Vehicle-to-vehicle communication is a solution to improve the quality of on-road traveling in terms of throughput, safety, efficiency and comfort. However, road users that do not communicate their planned activities can create dangerous situations, so prediction models are needed to foresee and anticipate their motions in the drivable space. Various prediction methods exist, either physics-based, data-based or hybrids, but they all make conservative assumptions about others’ intentions, or they are developed using unrealistic data, and it is unclear how they perform for trajectory prediction. In this work, we introduce and demonstrate an optimal hybrid framework that overcomes these limitations, by combining the predictions of several physics-based and data-based models. Using on-road measured data we show that this novel framework outperforms the individual models in both longitudinal and lateral position predictions. We also discuss the required prediction boundaries from a safety perspective and estimate the accuracies of the models in relation to automated vehicle functions. The results achieved by this method will enable increased safety, comfort and even more proactive reactions of the automated vehicles.
Originele taal-2Engels
Titel2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
UitgeverijIEEE-SMC
Pagina's444-450
Aantal pagina's7
ISBN van elektronische versie9781728185262
DOI's
StatusGepubliceerd - 14 dec 2020
Evenement2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) : Virtual event
- VIRTUAL, Toronto, Canada
Duur: 11 okt 202014 okt 2020

Congres

Congres2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) : Virtual event
LandCanada
StadToronto
Periode11/10/2014/10/20

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