Lucente, Giovanni und Maarssoe, Mikkel Skov und Kahl, Iris und Schindler, Julian (2024) Deep Learning Algorithms for Longitudinal Driving Behavior Prediction: A Comparative Analysis of Convolutional Neural Network and Long–Short-Term Memory Models. SAE International Journal of Connected and Automated Vehicles, 7 (4). SAE International. doi: 10.4271/12-07-04-0025. ISSN 2574-0741.
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Kurzfassung
In the realm of transportation science, the advent of deep learning has propelled advancements in predicting longitudinal driving behavior. This study explores the application of deep neural network architectures, specifically long–short-term memory (LSTM) and convolutional neural networks (CNNs), recognized for their effectiveness in handling sequential data. Using a 3-s temporal window that includes past vehicle progress, speed, and acceleration, the proposed model, a hybrid LSTM–CNN architecture, predicts the vehicle’s speed and progress for the next 6 s. The approach achieves state-of-the-art performance, particularly within a 4 s horizon, but remains competitive even for longer-term predictions. This is achieved despite the simplicity of its input space, which does not include information about vehicles other than the target vehicle. As a result, while its performance may decrease slightly for longer-term predictions due to the lack of environmental information, it still offers reliable predictions and can be applied effectively in scenarios with partial observability. The comparative analysis of multilayer perceptron (MLP), LSTM, and one-dimensional CNN architectures highlights the challenges faced by MLP in capturing the complex nonlinearity of driving behavior. LSTM and CNN demonstrate superior performance, with model complexity influencing outcomes. No statistically significant difference is observed in the performance between LSTM and CNN models.
elib-URL des Eintrags: | https://elib.dlr.de/205199/ | ||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Titel: | Deep Learning Algorithms for Longitudinal Driving Behavior Prediction: A Comparative Analysis of Convolutional Neural Network and Long–Short-Term Memory Models | ||||||||||||||||||||
Autoren: |
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Datum: | 13 Juni 2024 | ||||||||||||||||||||
Erschienen in: | SAE International Journal of Connected and Automated Vehicles | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
Band: | 7 | ||||||||||||||||||||
DOI: | 10.4271/12-07-04-0025 | ||||||||||||||||||||
Herausgeber: |
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Verlag: | SAE International | ||||||||||||||||||||
ISSN: | 2574-0741 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Driver behavior prediction, Deep learning, LSTM, Convolutional neural network | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Verkehr | ||||||||||||||||||||
HGF - Programmthema: | Straßenverkehr | ||||||||||||||||||||
DLR - Schwerpunkt: | Verkehr | ||||||||||||||||||||
DLR - Forschungsgebiet: | V ST Straßenverkehr | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz | ||||||||||||||||||||
Standort: | Braunschweig | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Verkehrssystemtechnik > Kooperative Systeme, BS | ||||||||||||||||||||
Hinterlegt von: | Lucente, Giovanni | ||||||||||||||||||||
Hinterlegt am: | 26 Jul 2024 09:52 | ||||||||||||||||||||
Letzte Änderung: | 29 Jul 2024 11:33 |
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