📄 中文摘要
高级驾驶辅助系统（ADAS）的调校需要在多个相互竞争的目标之间进行权衡，包括操作安全性、系统响应性、能量使用和乘客舒适度。提出了一种基于量子启发混合群体智能（QiHSI）的新优化框架，该框架将量子启发机制嵌入多目标沙尔普群体优化过程中，以增强在复杂高维决策空间中的全局搜索能力并保持种群多样性。此外，结合了决策者参与的策略，以便在优化过程中动态响应变化的设计优先级和系统需求。
一种量子启发的混合群体智能与多标准ADAS校准决策方法
📄 English Summary
A Quantum-inspired Hybrid Swarm Intelligence and Decision-Making for Multi-Criteria ADAS Calibration
The tuning of Advanced Driver Assistance Systems (ADAS) requires balancing multiple competing objectives, such as operational safety, system responsiveness, energy consumption, and passenger comfort. A novel optimization framework based on Quantum-Inspired Hybrid Swarm Intelligence (QiHSI) is proposed, embedding quantum-inspired mechanisms within a multi-objective salp swarm optimization process to enhance global search capabilities and maintain population diversity in complex, high-dimensional decision spaces. Additionally, a decision-maker-in-the-loop strategy is incorporated to allow the optimization process to dynamically respond to changing design priorities and system requirements.