Deyi Xue

Papers

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About

Deyi Xue is a leading researcher in intelligent vehicle control and autonomous driving systems, with a particular focus on self-localization and parameter calibration for dead-reckoning navigation. His most-cited work, "A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization" (2022, 6 citations), introduces a novel calibration approach that eliminates the need for specially designed data-collection paths. By leveraging an adaptive quantum-inspired evolutionary algorithm, Xue’s method optimizes dead-reckoning parameters in real time, significantly improving localization accuracy for autonomous vehicles. This contribution addresses a critical bottleneck in robotics control, enabling more robust and cost-effective self-localization without complex infrastructure. Xue’s research bridges evolutionary computation and vehicular control, offering practical solutions for real-world autonomous driving challenges. His work has been recognized for its innovative integration of quantum-inspired optimization with traditional dead-reckoning techniques, marking a notable step forward in intelligent vehicle reliability. For students and researchers in autonomous systems, Xue’s approach exemplifies how advanced algorithms can solve fundamental calibration problems, paving the way for safer and more efficient self-driving technologies.