- Tytuł:
- An automated driving strategy generating method based on WGAIL–DDPG
- Autorzy:
-
Zhang, Mingheng
Wan, Xing
Gang, Longhui
Lv, Xinfei
Wu, Zengwen
Liu, Zhaoyang - Powiązania:
- https://bibliotekanauki.pl/articles/2055167.pdf
- Data publikacji:
- 2021
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
automated driving system
deep learning
deep reinforcement learning
imitation learning
deep deterministic policy gradient
system jezdny
uczenie głębokie
uczenie przez naśladowanie - Opis:
- Reliability, efficiency and generalization are basic evaluation criteria for a vehicle automated driving system. This paper proposes an automated driving decision-making method based on the Wasserstein generative adversarial imitation learning–deep deterministic policy gradient (WGAIL–DDPG(λ)). Here the exact reward function is designed based on the requirements of a vehicle’s driving performance, i.e., safety, dynamic and ride comfort performance. The model’s training efficiency is improved through the proposed imitation learning strategy, and a gain regulator is designed to smooth the transition from imitation to reinforcement phases. Test results show that the proposed decision-making model can generate actions quickly and accurately according to the surrounding environment. Meanwhile, the imitation learning strategy based on expert experience and the gain regulator can effectively improve the training efficiency for the reinforcement learning model. Additionally, an extended test also proves its good adaptability for different driving conditions.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 461--470
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł