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Path following for Autonomous Ground Vehicle Using DDPG Algorithm: A Reinforcement Learning Approach

Cao, Yu ; Ni, Kan ; Jiang, Xiongwen ; Kuroiwa, Taiga ; Zhang, Haohao ; Kawaguchi, Takahiro ; Hashimoto, Seiji ; Jiang, Wei

Applied sciences, 2023-06, Vol.13 (11), p.6847 [Periódico revisado por pares]

Basel: MDPI AG

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Título:
Path following for Autonomous Ground Vehicle Using DDPG Algorithm: A Reinforcement Learning Approach
Autor: Cao, Yu ; Ni, Kan ; Jiang, Xiongwen ; Kuroiwa, Taiga ; Zhang, Haohao ; Kawaguchi, Takahiro ; Hashimoto, Seiji ; Jiang, Wei
Assuntos: Algorithms ; autonomous driving ; DDPG ; Effectiveness ; ground vehicle ; Machine learning ; Methods ; path following ; Reagents ; Reinforcement ; Robotics ; Robots ; Steering ; System dynamics ; Technology ; Trajectory planning ; Transportation industry ; Unmanned ground vehicles ; Vehicles
É parte de: Applied sciences, 2023-06, Vol.13 (11), p.6847
Descrição: The potential of autonomous driving technology to revolutionize the transportation industry has attracted significant attention. Path following, a fundamental task in autonomous driving, involves accurately and safely guiding a vehicle along a specified path. Conventional path-following methods often rely on rule-based or parameter-tuning aspects, which may not be adaptable to complex and dynamic scenarios. Reinforcement learning (RL) has emerged as a promising approach that can learn effective control policies from experience without prior knowledge of system dynamics. This paper investigates the effectiveness of the Deep Deterministic Policy Gradient (DDPG) algorithm for steering control in ground vehicle path following. The algorithm quickly converges and the trained agent achieves stable and fast path following, outperforming three baseline methods. Additionally, the agent achieves smooth control without excessive actions. These results validate the proposed approach’s effectiveness, which could contribute to the development of autonomous driving technology.
Editor: Basel: MDPI AG
Idioma: Inglês

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Path following for Autonomous Ground Vehicle Using DDPG Algorithm: A Reinforcement Learning Approach

Cao, Yu ; Ni, Kan ; Jiang, Xiongwen ; Kuroiwa, Taiga ; Zhang, Haohao ; Kawaguchi, Takahiro ; Hashimoto, Seiji ; Jiang, Wei

Basel: MDPI AG

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