Haoxin Wang
Papers
1
Total Citations
6
H-Index
1
About
Haoxin Wang is a rising researcher at the forefront of intelligent vehicular networks and autonomous driving systems. His primary research focuses on optimizing data delivery in highly dynamic vehicular environments, particularly through the integration of named data networking (NDN) and reinforcement learning. In his most cited work, "DSORL: Data Source Optimization With Reinforcement Learning Scheme for Vehicular Named Data Networks" (2023, 6 citations), Wang tackles a critical bottleneck for autonomous driving: the efficient transmission of high-definition (HD) maps. Recognizing that HD maps contain rich, multidimensional information essential for fine-grained environmental awareness and precise localization, he proposed a novel reinforcement learning framework to intelligently select optimal data sources, dramatically reducing latency and network congestion. This contribution directly addresses the challenge of handling substantial HD map data volumes in real-time, a key enabler for safe autonomous navigation. Wang’s work bridges the gap between advanced networking protocols and practical autonomous driving needs, positioning him as an emerging authority in vehicular communication systems. His innovative approach to data source optimization promises to accelerate the deployment of reliable, connected autonomous vehicles.
Research Focus
Key Achievements
Top Papers
- 1