Yuxiang Feng
Papers
3
Total Citations
42
H-Index
3
About
Yuxiang Feng is a researcher specializing in intelligent transportation systems, driver behavior modeling, and sensor fusion for autonomous driving. Their work focuses on understanding and classifying driving styles to improve fuel efficiency and road safety. Feng’s most cited paper, "A Support Vector Clustering Based Approach for Driving Style Classification" (2019, 21 citations), introduces a method to categorize drivers’ habitual behaviors, enabling the extraction of economical and ecological driving patterns despite intra-driver variability. Another key contribution, "Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filter" (2017, 15 citations), proposes a low-cost, accurate distance estimation technique by integrating Continental radar and dashcam data, with applications in driver modeling, accident avoidance, and autonomous driving. Feng also explored "Driving Style Modelling with Adaptive Neuro-Fuzzy Inference System and Real Driving Data" (2018, 6 citations), advancing adaptive modeling using real-world driving data. Their work has garnered over 40 citations, reflecting its relevance in the fields of driver classification and sensor fusion. Feng’s research offers practical solutions for enhancing vehicle intelligence and eco-driving, making significant strides toward safer and more efficient transportation systems.
Research Focus
Key Achievements
Top Papers
- 1A Support Vector Clustering Based Approach for Driving Style Classification21 citations · 2019
- 2Distance Estimation by Fusing Radar and Monocular Camera with Kalman Filter15 citations · 2017
- 3