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https://www.nationalacademies.org/trb/events

Active-Inference Control and Sensor-Fusion Simulation for Bicycle Stabilization in Aging Rider Mobility Systems

Low-speed fall events are the leading cause of cycling injuries among older adults, generating 25,000–40,000 annual emergency-department visits and more than $50B in national medical costs. (CDC, 2022; Weiss & Elixhauser, 2012; NCBI, 2012) These incidents primarily occur during mounting, slow riding, deceleration, and stopping—contexts where traditional steering-based stability controls, passive safety devices, and prior gyroscopic concepts fail to prevent loss of balance. StaeblTECH has developed a dual–Control-Moment-Gyroscope (CMG) stabilization prototype designed to proactively prevent these falls. Its success depends on a predictive, adaptive control architecture that can learn individual rider characteristics and manage uncertainty. Active Inference (AIF), a unified probabilistic framework for perception, prediction, and action, offers capabilities not available in conventional PID or Model Predictive Control (MPC) systems. (Bagaev & de Vries, 2023) This project develops the simulation-based control and sensor-fusion foundation required to integrate AIF into StaeblTECH’s stabilization platform. Leveraging MnRI’s robotics simulation environment, the research team will: (1) build a high-fidelity digital twin of bicycle, rider, and dual-CMG dynamics; (2) implement and tune AIF controllers using message-passing variational inference; (3) integrate IMU, optical-flow, and load-sensor data to evaluate latency, noise sensitivity, and perceptual accuracy; and (4) benchmark AIF performance against PID and MPC baselines using a 7-degree-of-freedom bicycle model. All outputs including AIF controllers, sensor-fusion models, stability metrics, and digital-twin datasets directly support StaeblTECH’s NIH Direct-to-Phase II proposal by providing validated simulation results and de-risking subsequent hardware development. This project strengthens Minnesota’s leadership in artificial intelligence (AI)-assisted mobility, addresses a critical aging-transportation challenge, and positions the UMN–StaeblTECH partnership for future federal funding.