A Comprehensive Overview of Autonomous Vehicle Ethical Decision-Making Using Stacking Fusion Mechanism

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A Comprehensive Overview of Autonomous Vehicle Ethical Decision-Making Using Stacking Fusion Mechanism

Introduction

Autonomous vehicles (AVs) have made significant advancements in path planning and driving control, yet they continue to face challenges in making ethical decisions during moral dilemmas. These dilemmas often involve scenarios where the vehicle must choose between two or more undesirable outcomes, such as deciding whether to collide with pedestrians or swerve into another lane, potentially harming passengers. The inability of AVs to make transparent, consistent, and morally justifiable decisions in such situations has raised concerns about their safety and reliability.

To address these challenges, researchers have explored various ethical frameworks, including utilitarianism, deontology, and virtue ethics. However, relying on a single ethical principle often leads to limitations. For instance, utilitarianism focuses on minimizing harm but may disregard fairness, while deontology enforces rigid moral rules without considering consequences. Virtue ethics, though appealing, lacks transparency in decision-making.

This paper introduces an ethical decision-making model for autonomous vehicles based on a stacking fusion mechanism, which integrates machine learning and deep learning techniques. The model leverages multiple base learners—Attribute Correlation Naive Bayes (ACNB), Weighted Average First-Order Bayes (WADOE), and Adaptive Fuzzy Decision (AFD)—to generate preliminary decisions. These decisions are then combined using a weighted average approach and fed into a meta-learner, a multi-scale hybrid attention-based convolutional neural network (CNN), to produce a final ethical decision.

Background and Motivation

Ethical Dilemmas in Autonomous Driving

Autonomous vehicles frequently encounter ethical dilemmas similar to the classic trolley problem, where a choice must be made between two harmful outcomes. For example:
• Scenario 1: The AV must decide between hitting pedestrians crossing legally or swerving into another lane, potentially harming passengers.

• Scenario 2: The AV must choose between colliding with a group of elderly pedestrians or a child.

These scenarios require AVs to weigh multiple factors, including:
• Human life preservation (minimizing fatalities).

• Legal compliance (following traffic rules).

• Moral fairness (avoiding discrimination based on age, social status, etc.).

• Self-interest (protecting passengers).

Existing ethical decision-making models often struggle with:

  1. Lack of transparency: Deep learning models, while powerful, are often “black boxes” with unclear reasoning.
  2. Over-reliance on a single principle: Models based solely on utilitarianism or deontology may produce morally questionable outcomes.
  3. Limited generalization: Some models perform well in specific scenarios but fail in novel situations.

The Need for a Hybrid Approach

To overcome these limitations, this paper proposes a stacking fusion mechanism that combines:
• Machine learning models (ACNB, WADOE, AFD): These provide interpretable, rule-based decision-making.

• Deep learning (CNN with attention mechanisms): This enhances generalization by learning complex patterns from data.

The fusion of these approaches ensures that decisions are both explainable (due to machine learning) and adaptive (due to deep learning).

Methodology

Overview of the Stacking Fusion Mechanism

The proposed model follows a two-stage decision-making process:

  1. Base Learners (First Stage)
    • ACNB (Attribute Correlation Naive Bayes): Extends traditional Naive Bayes by considering correlations between features (e.g., age and social status).

    • WADOE (Weighted Average First-Order Bayes): Assigns weights to features based on their importance in decision-making.

    • AFD (Adaptive Fuzzy Decision): Uses fuzzy logic to handle uncertainty in ethical dilemmas.

    These models independently analyze the dilemma and produce preliminary decisions.

  2. Meta-Learner (Second Stage)
    • A multi-scale hybrid attention-based CNN processes the combined outputs from the base learners.

    • The CNN extracts high-level features from the dilemma scenario (e.g., pedestrian positions, traffic signals) and refines the final decision.

Detailed Explanation of Base Learners

  1. ACNB: Attribute Correlation Naive Bayes

Traditional Naive Bayes assumes feature independence, which is unrealistic in ethical dilemmas (e.g., age and vulnerability are often related). ACNB improves this by incorporating feature correlations.

• Key Idea: If two features (e.g., “age” and “traffic rule compliance”) both support the same decision, their joint influence is considered.

• Implementation:

• Calculate correlation coefficients between features using training data.

• Modify probability calculations to account for dependencies.

  1. WADOE: Weighted Average First-Order Bayes

WADOE enhances decision-making by assigning dynamic weights to features based on their relevance.

• Key Idea: Not all features are equally important. For example, “number of pedestrians” may carry more weight than “gender.”

• Implementation:

• Use mutual information to determine feature weights.

• Compute a weighted average of probabilities for final decision-making.

  1. AFD: Adaptive Fuzzy Decision

Ethical dilemmas often involve ambiguity (e.g., “Is it worse to harm an elderly person or a child?”). Fuzzy logic handles such uncertainty.

• Key Idea: Assign membership degrees to decisions (e.g., 70% “swerve,” 30% “stay”).

• Implementation:

• Define fuzzy sets for decision outcomes.

• Continuously update decision boundaries based on new data.

Meta-Learner: Multi-Scale Hybrid Attention CNN

The base learners’ outputs are fed into a CNN with attention mechanisms to refine the decision.

• Multi-Scale Feature Extraction:

• The CNN uses different kernel sizes to capture both fine-grained (e.g., pedestrian details) and high-level (e.g., lane positions) features.

• Attention Mechanism:

• Focuses on critical regions (e.g., pedestrians in danger zones).

• Combines channel attention (prioritizing important features) and spatial attention (highlighting key image regions).

Experimental Validation

Dataset and Training

• Training Set: 463 ethical dilemma scenarios.

• Validation Set: Used to evaluate model performance.

Performance Metrics

  1. Average Loss Rate: Measures prediction errors.
    • Lower values indicate better performance.

  2. Accuracy: Proportion of correct decisions.

  3. Correctness Rate: Consistency with human moral judgments.

Results

  1. Deep Learning Model (Baseline):
    • Average loss: 0.64

    • Accuracy: 0.70

    • Correctness: 0.61

  2. Stacking Fusion Model:
    • Average loss: 0.35 (45% improvement)

    • Accuracy: 0.90 (29% improvement)

    • Correctness: 0.75 (23% improvement)

Key Findings

• The stacking fusion model outperforms standalone deep learning in all metrics.

• The hybrid approach balances transparency (from machine learning) and adaptability (from deep learning).

Discussion

Advantages of the Stacking Fusion Mechanism

  1. Improved Decision Quality:
    • Combining multiple models reduces biases inherent in single approaches.

  2. Explainability:
    • Base learners provide interpretable intermediate decisions.

  3. Scalability:
    • The framework can incorporate additional ethical principles or new data.

Limitations and Future Work

  1. Correctness Rate:
    • The model achieves 75% correctness, indicating room for improvement.

  2. Training Data:
    • Larger datasets could enhance generalization.

  3. Real-World Deployment:
    • Further testing in dynamic environments is needed.

Conclusion

This paper presents a stacking fusion-based ethical decision-making model for autonomous vehicles, addressing key challenges in transparency, fairness, and adaptability. By integrating machine learning and deep learning, the model achieves higher accuracy and correctness than traditional approaches. Future work will focus on refining the model with larger datasets and real-world testing.

doi.org/10.19734/j.issn.1001-3695.2024.07.0280

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