Lucente, Giovanni und Maarssoe, Mikkel Skov und Konthala, Sanath Himasekhar und Abulehia, Anas und Dariani, Reza und Schindler, Julian (2024) DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning. IEEE Open Journal of Intelligent Transportation Systems. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.36227/techrxiv.172651860.08700487/v2. ISSN 2687-7813.
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Offizielle URL: https://www.techrxiv.org/doi/full/10.36227/techrxiv.172651860.08700487/v2
Kurzfassung
Trajectory planning for automated vehicles in traffic has been challenging task and a hot topic in recent research. The need for flexibility, transparency, interpretability and predictability poses challenges in deploying data-driven approaches in this safety-critical application. This paper proposes DeepGame-TP, a game-theoretical trajectory planner that uses deep learning to model each agent's cost function and adjust it based on observed behavior. In particular, a LSTM network predicts each agent's desired speed, forming a penalizing term that reflects aggressiveness in the cost function. Experiments demonstrated significant advantages of this innovative framework, highlighting the adaptability of DeepGame-TP in intersection, overtaking, car following and merging scenarios. It effectively avoids dangerous situations that could arise from incorrect cost function estimates. The approach is suitable for real-time applications, solving the Generalized Nash Equilibrium Problem (GNEP) in scenarios with up to four vehicles in under 100 milliseconds on average.
elib-URL des Eintrags: | https://elib.dlr.de/207616/ | ||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||
Titel: | DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | Oktober 2024 | ||||||||||||||||||||||||||||
Erschienen in: | IEEE Open Journal of Intelligent Transportation Systems | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||
Gold Open Access: | Ja | ||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
DOI: | 10.36227/techrxiv.172651860.08700487/v2 | ||||||||||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||||||
ISSN: | 2687-7813 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | deep learning, generalized nash equilibrium, dynamic game, lstm, trajectory planning | ||||||||||||||||||||||||||||
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: | 25 Okt 2024 20:18 | ||||||||||||||||||||||||||||
Letzte Änderung: | 25 Okt 2024 20:18 |
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