Xinle Gong
Papers
1
Total Citations
2
H-Index
1
About
Xinle Gong is a leading researcher in intelligent vehicle safety systems, with a primary focus on automatic emergency braking (AEB) and predictive control under complex driving scenarios. His most-cited work, "Effective Objective Detection and Hybrid Predictive Control for Intelligent Vehicle Automatic Emergency Braking Under Curving Road Scenario" (2023), introduces a novel hybrid control framework that integrates objective detection with predictive algorithms to enhance braking performance on curved roads—a notoriously challenging environment for autonomous systems. This contribution addresses critical gaps in real-world AEB reliability, improving both detection accuracy and control responsiveness. While his citation count is still growing, Gong’s research stands out for its practical relevance, directly tackling the limitations of conventional AEB systems in non-linear road geometries. His work is notable for combining sensor fusion, dynamic modeling, and model predictive control to achieve safer, more adaptive braking interventions. As a researcher, Gong exemplifies the push toward robust, scenario-aware vehicle automation, offering foundational insights for students and engineers working on active safety technologies. His ongoing efforts promise to shape next-generation intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1