Dr. Peijiang Zhang is a researcher specializing in human machine collaboration, bio inspired visual perception, 3D vision, precision measurement, and multi sensor information fusion. His research bridges the fields of computer vision, cognitive neuroscience, and artificial intelligence, focusing on developing biologically inspired perception models that emulate the cognitive and visual mechanisms of the human brain. Through his work, he has advanced methodologies for image quality assessment, defect detection in mechanical manufacturing, and visual modeling inspired by human visual cognition. His studies extend to 3D LiDAR based SLAM algorithms and integrated vehicle infrastructure cooperative perception systems, contributing to intelligent transportation and autonomous systems. Dr. Zhang’s current research aims to enhance human machine collaborative perception by integrating AI and brain computer interface technologies to improve system adaptability, real time decision making, and cooperative sensing performance. His scientific contributions have been recognized through publications in high impact journals and conferences, including research on cognitive load classification using spiking neural networks and cross-modal brain activity analysis. His work is directed toward advancing intelligent, adaptive, and cooperative perception systems for autonomous vehicles and smart transportation infrastructures, contributing to the next generation of bio inspired artificial intelligence and human-centered machine collaboration.
Profile: Orcid
Featured Publications
Zhang, P., Cheng, T., Jiang, Y., Zou, X., & Chen, X. (2025). fNIRS SpikeNet: A spiking neural network framework for cognitive load classification in cooperative learning environments. IEEE Transactions on Computational Social Systems.
Zou, X., Liu, X., Wang, K., Cheng, T., & Zhang, P. (2025, November 4). Cognitive load in novice UAV pilots: A preliminary fNIRS investigation. International Journal of Human–Computer Interaction.
Zou, X., Zhang, P., Cheng, T., & Fan, J. (2024, November 15). Effects of hazard perception training on driving behavior: An fNIRS-based assessment. In Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR). IEEE.
Zhang, P., & Zou, X. (2024, November 15). Integrated approach for cross-modal brain activity classification through manual feature extraction. In Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR). IEEE.