Eichenlaub, Tobias ; Heckelmann, Paul ; Rinderknecht, Stephan (2023)
Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds.
In: Vehicles, 5 (1)
doi: 10.3390/vehicles5010001
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Eichenlaub, Tobias ; Heckelmann, Paul ; Rinderknecht, Stephan |
| Art des Eintrags: | Bibliographie |
| Titel: | Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds |
| Sprache: | Englisch |
| Publikationsjahr: | 17 Januar 2023 |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Vehicles |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.3390/vehicles5010001 |
| Kurzbeschreibung (Abstract): | Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach. |
| Freie Schlagworte: | longitudinal control, supervised learning, speed predictions, V2X, realistic microscopic traffic simulation, urban traffic, electric vehicles |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Mechatronische Systeme im Maschinenbau (IMS) |
| Hinterlegungsdatum: | 18 Jan 2023 07:28 |
| Letzte Änderung: | 18 Jan 2023 09:59 |
| PPN: | 503871060 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
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