Object

Title: An automated driving strategy generating method based on WGAIL-DDPG

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Group publication title:

AMCS, volume 31 (2021)

Abstract:

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). 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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Resource Identifier:

oai:zbc.uz.zgora.pl:86296

DOI:

10.34768/amcs-2021-0031

Pages:

461-470

Source:

AMCS, volume 31, number 3 (2021) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego

Objects Similar

×

Citation

Citation style:

This page uses 'cookies'. More information