Xingtao Liu

Papers

Total Citations

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About

Xingtao Liu is a researcher focused on intelligent control systems for autonomous vehicles, particularly in complex and unstructured environments. His most-cited work, "The Study on Path Tracking Control Method Based on Fuzzy-CMAC for Autonomous Vehicle in Rural Environment" (2020), addresses a critical challenge in autonomous navigation: maintaining precise path tracking in rural settings where traditional dynamic models fall short. Liu’s major contribution lies in developing a composite control method that integrates a Cerebellar Model Articulation Controller (CMAC) neural network with fuzzy logic. This hybrid approach leverages the neural network’s adaptive learning capabilities and the fuzzy controller’s robust handling of uncertainty, enabling autonomous vehicles to navigate uneven terrain and unpredictable obstacles more effectively. While his citation count is currently modest, his work represents a practical step toward bridging the gap between laboratory-perfect autonomous systems and real-world deployment in agriculture, forestry, or rural logistics. Liu’s research is particularly valuable for students and engineers seeking robust, low-cost control solutions that do not rely on highly detailed dynamic models, offering a foundation for further innovation in off-road autonomous navigation.