Actual deviation correction based on weight improvement for 10-unit Dolph–Chebyshev array antennas

Li, Wei; Xu, Chunguang; He, Yanlin; Chen, Liguo; Sun, Lining; Guoqiang, Fang

doi:10.1007/s12652-017-0589-y

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…In terms of vehicle autonomous driving decision-making, many scholars worldwide have carried out related research, focusing on three parts: task-driven rule limitation methods [8][9][10][11], data-driven deep learning methods [12,13], and experience-driven reinforcement methods [14][15][16][17]. The task-driven rule definition method is to artificially construct a driving behavior rule library according to traffic laws and then to select driving behaviors according to environmental information, which has good interpretability and stability.…”

Section: Introductionmentioning

confidence: 99%

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

Lin

Zhang

et al. 2022

Machines

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To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based on deep reinforcement learning. The ideas of target attraction and obstacle rejection of the artificial potential field method are introduced into the distributed proximal policy optimization algorithm, and the APF-DPPO learning model is established. To solve the range repulsion problem of the artificial potential field method, which affects the optimal driving strategy, this paper proposes a directional penalty function method that combines collision penalty and yaw penalty to convert the range penalty of obstacles into a single directional penalty, and establishes the vehicle motion collision model. Finally, the APF-DPPO learning model is selected to train the driving strategy for the virtual vehicle, and the transfer learning method is selected to verify the comparison experiment. The simulation results show that the completion rate of the virtual vehicle in the obstacle environment that generates penalty feedback is as high as 96.3%, which is 3.8% higher than the completion rate in the environment that does not generate penalty feedback. Under different reward functions, the method in this paper obtains the highest cumulative reward value within 500 s, which improves 69 points compared with the reward function method based on the artificial potential field method, and has higher adaptability and robustness in different environments. The experimental results show that this method can effectively improve the efficiency of autonomous driving strategy learning and control the virtual vehicle for autonomous driving behavior decisions, and provide reliable theoretical and technical support for real vehicles in autonomous driving decision-making.

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Section: Introductionmentioning

confidence: 99%

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

Lin

Zhang

et al. 2022

Machines

View full text Add to dashboard Cite

show abstract

“…Zhang et al (Yong et al 2017) proposed a gas leakage source localization algorithm using distributed maximum likelihood estimation method for mobile sensor network to improve the lower performance with static sensor network. In order to achieve higher gains and lower side lobe in antenna design, array antenna is conventionally adopted by Li et al (Wei et al 2017). Zheng et al (2018) present a Parallel Gated Poll (PGP) access mechanism that exploits the PCF defined in the IEEE 802.11.…”

mentioning

confidence: 99%

Special issue on the intelligent sensing on mobile computing and wireless communications

Liu

Yen

2018

J Ambient Intell Human Comput

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A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

Koppula¹,

Rao²,

Nabi³

et al. 2022

Wireless Pers Commun

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Actual deviation correction based on weight improvement for 10-unit Dolph–Chebyshev array antennas

Cited by 8 publications

References 13 publications

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

Special issue on the intelligent sensing on mobile computing and wireless communications

A Novel Optimized Recurrent Network-Based Automatic System for Speech Emotion Identification

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