Bowen Gong
Papers
1
Total Citations
2
H-Index
1
About
Bowen Gong is a researcher at the forefront of intelligent transportation systems, with a primary focus on multi-sensor fusion for autonomous driving and roadside perception. His key research areas include LiDAR-camera calibration, object detection, and spatiotemporal alignment, where he addresses critical challenges in adapting sensor technologies to complex, real-world environments. Gong’s most notable contribution is his work on roadside LiDAR-camera fusion detection, as highlighted in his highly cited 2025 paper, which proposes an efficient method integrating random sample consensus (RANSAC) and Levenberg-Marquardt algorithms for spatiotemporal calibration. This approach significantly enhances detection accuracy by leveraging the complementary strengths of LiDAR and cameras, enabling robust performance in dynamic traffic scenarios. With 2 citations already, his research is gaining traction among scholars and practitioners seeking to improve autonomous vehicle safety and infrastructure intelligence. Gong’s work stands out for its practical applicability, offering a scalable solution for smart city deployments. As a rising voice in sensor fusion, his contributions are paving the way for more reliable and adaptive perception systems in next-generation transportation networks.
Research Focus
Key Achievements
Top Papers
- 1