Bilal Hassan
Papers
1
Total Citations
10
H-Index
1
About
Bilal Hassan is a researcher at the intersection of autonomous vehicle navigation and deep learning, with a primary focus on enhancing vehicle safety and control in challenging off-road environments. His most cited work, "A Hybrid Deep Learning Approach for Vehicle Wheel Slip Prediction in Off-Road Environments" (2022, 10 citations), tackles the critical challenge of accurately predicting tire slippage on unpaved surfaces like sand, gravel, and mud—a task essential for safe trajectory planning and optimal vehicle control. By developing a hybrid deep learning model, Hassan addresses the complexities of measuring wheel slip, which is notoriously difficult due to the dynamic and unpredictable nature of off-road terrains. This contribution is particularly impactful for autonomous ground vehicles operating in agriculture, mining, or military applications, where traction loss can lead to accidents or mission failure. Though early in his career, Hassan’s work demonstrates a clear commitment to solving real-world problems in robotics and vehicle dynamics, laying a foundation for safer, more reliable autonomous systems in rugged environments.
Research Focus
Key Achievements
Top Papers
- 1