Eichenlaub, Tobias ; Rinderknecht, Stephan (2021)
Anticipatory Longitudinal Vehicle Control using a LSTM Prediction Model.
doi: 10.1109/ITSC48978.2021.9564787
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this paper, an approach for longitudinal vehicle control is proposed that integrates a Machine Learning model for the speed prediction of the leading vehicle in order to improve the energy efficiency of the control in a city driving environment. The prediction is employed in an additional control mode that extends a conventional state-of-the-art speed and headway control. The approach aims to reduce the vehicle consumption through a more anticipatory driving style. The prediction model uses an encoder-decoder LSTM network with additional information from Vehicle-2-X communication and is trained through supervised learning with training data generated from simulation. A co-simulation environment comprising of an ego vehicle simulation and a microscopic traffic simulation is used for the generation of training data and the assessment of the control approach. The proposed control is compared to a benchmark Adaptive Cruise Control scheme for three battery electric vehicles with different powertrain specifications on multiple routes in a simulated city-driving environment of Darmstadt, Germany. The results show that the consumption of the vehicles can be reduced for all vehicles by 3–5 % while still maintaining similar mean speeds on different routes through the city.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Eichenlaub, Tobias ; Rinderknecht, Stephan |
| Art des Eintrags: | Bibliographie |
| Titel: | Anticipatory Longitudinal Vehicle Control using a LSTM Prediction Model |
| Sprache: | Englisch |
| Publikationsjahr: | 19 September 2021 |
| Ort: | New York |
| Verlag: | IEEE |
| Buchtitel: | 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 19-22 Sept. 2021, Indianapolis, IN, USA |
| DOI: | 10.1109/ITSC48978.2021.9564787 |
| URL / URN: | https://ieeexplore.ieee.org/document/9564787 |
| Kurzbeschreibung (Abstract): | In this paper, an approach for longitudinal vehicle control is proposed that integrates a Machine Learning model for the speed prediction of the leading vehicle in order to improve the energy efficiency of the control in a city driving environment. The prediction is employed in an additional control mode that extends a conventional state-of-the-art speed and headway control. The approach aims to reduce the vehicle consumption through a more anticipatory driving style. The prediction model uses an encoder-decoder LSTM network with additional information from Vehicle-2-X communication and is trained through supervised learning with training data generated from simulation. A co-simulation environment comprising of an ego vehicle simulation and a microscopic traffic simulation is used for the generation of training data and the assessment of the control approach. The proposed control is compared to a benchmark Adaptive Cruise Control scheme for three battery electric vehicles with different powertrain specifications on multiple routes in a simulated city-driving environment of Darmstadt, Germany. The results show that the consumption of the vehicles can be reduced for all vehicles by 3–5 % while still maintaining similar mean speeds on different routes through the city. |
| Freie Schlagworte: | Adaptation models, Analytical models, Microscopy, Urban areas, Training data, Machine learning, Predictive models |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Mechatronische Systeme im Maschinenbau (IMS) |
| Hinterlegungsdatum: | 31 Jan 2022 06:32 |
| Letzte Änderung: | 31 Jan 2022 06:32 |
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