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Forecasting Short to Mid-Length Speed Trajectories of Preceding Vehicle Using V2X Connectivity for Eco-Driving of Electric Vehicles
Technical Paper
2021-01-0431
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
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English
Abstract
In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory. The simulation results show that the proposed predictor’s improved predictions can reduce energy consumption by 5% over eco-driving with the baseline predictor.
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Citation
Hyeon, E., Shen, D., Karbowski, D., and Rousseau, A., "Forecasting Short to Mid-Length Speed Trajectories of Preceding Vehicle Using V2X Connectivity for Eco-Driving of Electric Vehicles," SAE Technical Paper 2021-01-0431, 2021, https://doi.org/10.4271/2021-01-0431.Data Sets - Support Documents
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References
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