A Quantum-inspired Hybrid Swarm Intelligence and Decision-Making for Multi-Criteria ADAS Calibration
Sanjai Pathak, Ashish Mani, Amlan Chatterjee

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…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Vehicle Dynamics and Control Systems · Vehicle emissions and performance
