Dergi makalesi Açık Erişim
Albaba, Berat Mert; Yildiz, Yildiray
In this work, a synergistic combination of deep reinforcement learning and hierarchical game theory is proposed as a modeling framework for behavioral predictions of drivers in highway driving scenarios. The modeling framework presented in this work can be used in a high-fidelity traffic simulator consisting of multiple human decision-makers. This simulator can reduce the time and effort spent for testing autonomous vehicles by allowing safe and quick assessment of self-driving control algorithms. To demonstrate the fidelity of the proposed modeling framework, game-theoretical driver models are compared with real human driver behavior patterns extracted from two different sets of traffic data.
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bib-c999ac33-c6d6-441c-b5e1-4195979ac209.txt
md5:2fd4b7956174e98dd4caacb26a5be1a8 |
176 Bytes | İndir |
Görüntülenme | 14 |
İndirme | 4 |
Veri hacmi | 704 Bytes |
Tekil görüntülenme | 12 |
Tekil indirme | 4 |