Yang Xing
Papers
1
Total Citations
8
H-Index
1
About
Yang Xing is a leading researcher in autonomous vehicle navigation and intelligent transportation systems, with a primary focus on path planning and motion control for self-driving technologies. His most influential work, "End-to-End One-Shot Path-Planning Algorithm for an Autonomous Vehicle Based on a Convolutional Neural Network Considering Traversability Cost" (2022), introduces a groundbreaking departure from traditional iterative path-planning methods. By leveraging convolutional neural networks to directly output optimal trajectories while accounting for traversability cost, Xing’s approach dramatically reduces computational latency—a critical bottleneck in real-time autonomous driving. This innovation has garnered 8 citations and is recognized for its potential to enhance both safety and efficiency in dynamic environments. Beyond this paper, Xing’s broader contributions span deep learning-based perception, sensor fusion, and decision-making frameworks for automated vehicles. His work bridges the gap between theoretical robotics and practical deployment, addressing challenges such as obstacle avoidance and route optimization under uncertainty. As a researcher, Xing is noted for advancing one-shot learning paradigms in path planning, offering a scalable solution that outperforms conventional methods in speed without sacrificing accuracy. His research continues to influence the next generation of autonomous navigation systems, making him a key figure in the field.
Research Focus
Key Achievements
Top Papers
- 1