Bicycle Model

Bicycle modeling research focuses on creating accurate and computationally efficient representations of bicycle dynamics for various applications. Current efforts involve developing sophisticated models, such as the Whipple model, incorporating machine learning techniques like reinforcement learning for control and path planning, and utilizing neural networks (e.g., LSTMs) to handle high-dynamic maneuvers. These advancements are driving improvements in areas like autonomous bicycle control, bike-sharing system optimization through predictive modeling of user behavior and resource allocation, and even the digital documentation of historical bicycles.