Wushuang Bai
Papers
1
Total Citations
8
H-Index
1
About
Wushuang Bai is a researcher at the forefront of intelligent transportation systems, specializing in the integration of Connected and Autonomous Vehicles (CAVs) into real-world traffic networks. Their key research areas include traffic simulation, vehicle-to-infrastructure communication, and the macroscopic impacts of autonomous driving algorithms. Bai’s most notable contribution is the development of a novel framework that bridges the gap between high-fidelity vehicle motion simulations and large-scale traffic flow models, enabling researchers to predict how individual CAV behaviors—such as platooning or adaptive cruise control—ripple through an entire road network. This work, published in 2021 and garnering 8 citations, directly addresses a critical challenge in the field: the disconnect between per-vehicle algorithm testing and network-level traffic analysis. By integrating ROS-based vehicle simulations with the AIMSUN traffic simulator, Bai’s methodology provides a powerful tool for assessing CAV impacts on congestion, safety, and efficiency. Their research is essential for policymakers and engineers seeking to validate autonomous systems before real-world deployment, marking Bai as a rising authority in the practical evaluation of smart mobility solutions.
Research Focus
Key Achievements
Top Papers
- 1