Teng Sun

Papers

Total Citations

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About

Teng Sun is a researcher advancing intelligent systems in robotics and autonomous driving, with a focus on pedestrian tracking and multi-robot coordination. Their most cited work, "Intelligent vehicle pedestrian tracking based on YOLOv3 and DASiamRPN" (2021, 10 citations), addresses a critical challenge in autonomous navigation: the need for manual frame selection in traditional single-target pedestrian tracking. By blending YOLOv3’s detection capabilities with DASiamRPN’s tracking algorithm, Sun proposed a method that automates designated pedestrian tracking, enhancing safety and efficiency in robot and autopilot applications. This contribution is particularly impactful for real-time systems where precision and automation are paramount. Additionally, Sun’s work on "Artificial Potential Field Method for Area Coverage of Multi Agricultural Robots" (2021, 4 citations) explores scalable solutions for agricultural robotics, using potential field theory to coordinate multiple robots for efficient area coverage. This research underscores Sun’s versatility in applying computational methods to both urban and rural autonomous systems. With a growing citation record, Sun’s contributions are shaping the future of intelligent vehicle navigation and multi-agent robotics, offering practical algorithms that bridge detection, tracking, and coordination challenges.