Papers
2
Total Citations
27
H-Index
2
About
Kang Yue is a researcher at the forefront of intelligent transportation and human-machine interaction, with a focus on developing safer, more responsive driving systems. His major contributions lie in bridging the gap between human intent and vehicle action, particularly through his work on Advanced Driving Assistance Systems (ADAS). His most cited paper, "Dynamic obstacles avoidance based on image-based dynamic window approach for human-vehicle interaction" (2015, 18 citations), introduces the Image-based Dynamic Window Approach (IDWA), a novel method that integrates dynamic obstacle avoidance directly with human driving input to prevent common errors. This work has been foundational for researchers exploring real-time, vision-based vehicular safety. More recently, Yue has expanded into the frontier of brain-computer interfaces (BCI) for navigation, as detailed in his 2022 progress report (9 citations). This work traces the evolution of BCI from virtual environment prototypes to the realization of accurate locomotion intent in real-world settings. By connecting neural signals directly to navigation systems, his research is charting a path toward next-generation, hands-free vehicle control, demonstrating a sustained commitment to making human-vehicle interaction both more intuitive and inherently safer.
Research Focus
Key Achievements
Top Papers
- 1
- 2