Modeling Head-Neck Dynamics under Lateral Perturbations Using MPC to Mimic CNS postural stabilization strategy

2025-07-30

AI Summary

In autonomous vehicles, passengers lacking visual cues often exhibit head instability under lateral disturbances, leading to sensory conflict and discomfort. To address this, we propose a biomechanical head–neck posture regulation model based on model predictive control (MPC), which emulates the central nervous system’s compensatory control strategy—relying solely on vestibular and partial proprioceptive feedback in the absence of vision. The model integrates muscle dynamics with delayed, noise-contaminated proprioceptive signals while explicitly avoiding a head-pose integrator, thereby enhancing dynamic fidelity during abrupt perturbations. Validated against human experimental data, it accurately reproduces measured head–neck kinematics. Our approach advances sensory conflict and ride comfort prediction accuracy, establishing an interpretable, experimentally verifiable computational framework for assessing passenger comfort in autonomous driving systems.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Automated vehicles will allow occupants to engage in non-driving tasks, but limited visual cues will make them vulnerable to unexpected movements. These unpredictable perturbations create a "surprise factor," forcing the central nervous system to rely on compensatory postural adjustments, which are less effective, and are more likely to trigger sensory conflicts. Since the head is a key reference for sensory input (vestibular and vision), models accurately capturing head-neck postural stabilization are essential for assessing AV comfort. This study extends an existing model predictive control-based framework to simulate head-neck postural control under lateral perturbations. Experimental validation against human data demonstrates that the model can accurately reproduce dynamic responses during lateral trunk perturbations. The results show that muscle effort combined with partial somatosensory feedback provides the best overall dynamic fit without requiring corrective relative and global head orientation integrators for posture.

Problem

• Model head-neck dynamics under lateral perturbations
• Assess postural stabilization in automated vehicles
• Validate model accuracy with human experimental data

Innovation

• MPC-based head-neck dynamics modeling
• Lateral perturbation experimental validation
• Muscle effort with somatosensory feedback