A Quantum-inspired Hybrid Swarm Intelligence and Decision-Making for Multi-Criteria ADAS Calibration

TL;DR

This paper presents a quantum-inspired hybrid swarm intelligence framework for multi-criteria ADAS calibration, improving solution quality, convergence speed, and adaptability in complex optimization scenarios.

Contribution

It introduces a novel quantum-inspired hybrid swarm optimization method with decision-maker-in-the-loop for enhanced ADAS calibration.

Findings

Outperforms state-of-the-art algorithms in convergence speed.

Produces well-distributed Pareto-optimal solutions.

Demonstrates effectiveness in practical ADAS calibration scenarios.

Abstract

The tuning of Advanced Driver Assistance Systems (ADAS) involves resolving trade-offs among several competing objectives, including operational safety, system responsiveness, energy usage, and passenger comfort. This work introduces a novel optimization framework based on Quantum-Inspired Hybrid Swarm Intelligence (QiHSI), in which quantum-inspired mechanisms are embedded within a multi-objective salp swarm optimization process to strengthen global search capability and preserve population diversity in complex, high-dimensional decision spaces. In addition, a decision-maker-in-the-loop strategy is incorporated to incorporate adaptive expert guidance, enabling the optimization process to respond dynamically to changing design priorities and system constraints. The effectiveness of QiHSI is assessed using established multi-objective benchmark problems as well as a practical ADAS calibration scenario. Experimental comparisons with several state-of-the-art evolutionary and swarm-based algorithms, including MSSA, MOPSO, MOEA/D, SPEA2, NSGA-III, and RVEA, show that the proposed method consistently produces well-distributed Pareto-optimal solutions with faster convergence and improved adaptability. These findings demonstrate that QiHSI offers a reliable and scalable approach for intelligent ADAS calibration, supporting the development of more responsive, efficient, and safety-oriented autonomous driving technologies.