DeepGame-TP: Integrating Dynamic Game Theory and Deep Learning for Trajectory Planning

被引：0

作者：

Lucente, Giovanni ^{[1
,2
]}

Maarssoe, Mikkel Skov ^{[1
]}

Konthala, Sanath Himasekhar ^{[1
]}

Abulehia, Anas ^{[1
]}

Dariani, Reza ^{[1
]}

Schindler, Julian ^{[1
]}

机构：

[1] German Aerosp Ctr DLR, Inst Transportat Syst, D-38108 Braunschweig, Germany

[2] Tech Univ Berlin, Fak Verkehrs und Maschinensyst, TU Berlin, D-10623 Berlin, Germany

来源：

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS | 2024年 / 5卷

关键词：

Trajectory; Trajectory planning; Planning; Training; Deep learning; Safety; Real-time systems; Nash equilibrium; Imitation learning; Games; Dynamic game; deep learning; generalized Nash equilibrium; LSTM; trajectory planning; PREDICTION;

D O I：

10.1109/OJITS.2024.3515270

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Trajectory planning for automated vehicles in traffic has been a 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.

引用

页码：873 / 888

页数：16

共 41 条

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