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Algorithm Research & Explore
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462-468

Self-driving ethical decision-making model based on stacking fusion mechanism

Liu Guoman1
Sheng Jing1
Luo Yufeng2
1. Jiangxi Province Key Laboratory of Precision Drive & Equipment, Nanchang Institute of Technology, Nanchang 330099, China
2. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Abstract

Although autonomous vehicles(AV) have made great progress in line planning and driving controlling, it is still difficult to make certain and reasonable decision-making in ethical dilemmas, which causes people's doubts and worry about AV's safety. Therefore, it is necessary to design a self-driving ethical decision-making model and mechanism to make reasonable decision-makings in ethical dilemmas for AV. For them, this paper provided an ethical decision-making model based on stacking fusion mechanism, in which machine learning and deep learning were deeply integrated. Firstly, naive Bayes model based on attribute correlation(ACNB), weighted average first-order Bayes model(WADOE) and adaptive fuzzy model(AFD) were constructed into basis learners of stacking fusion mechanism. According to the accuracy in previous verification, their weights were set in decision-making, and the decision-making results in all dilemmas should be calculated by weighted average method. Then the decision-making data by the basis learner was regarded as training set for the meta-learner in the stacking fusion mechanism to construct stacking fusion decision-making model. Lastly, all dilemmas in verification set were used to verify deep learning model and stacking fusion mechanism respectively. The effect of stacking fusion mechanism was evaluated and compared based on the average loss rate and accuracy in the verification and the correction rate in the testing. The results show that the average loss rate is more 0.64, the maximum average accuracy is 0.7, and the highest correction rate is 0.61 for the deep learning model. However, the lowest average loss rate is 0.35, the maximum average accuracy is 0.9, and the highest correction rate is 0.75 for stacking fusion mechanism, which indicates the stacking fusion mechanism is greatly improved compared with the deep learning model.

Foundation Support

江西省教育厅自然科学项目(GJJ201911)
江西省科技厅科技项目(20232BAA10027)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0280
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 462-468
Serial Number: 1001-3695(2025)02-019-0462-07

Publish History

[2025-02-05] Printed Article

Cite This Article

刘国满, 盛敬, 罗玉峰. 基于stacking融合机制的自动驾驶伦理决策模型 [J]. 计算机应用研究, 2025, 42 (2): 462-468. (Liu Guoman, Sheng Jing, Luo Yufeng. Self-driving ethical decision-making model based on stacking fusion mechanism [J]. Application Research of Computers, 2025, 42 (2): 462-468. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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