US20250121843
2025-04-17
Performing operations; transporting
B60W50/12
The patent application describes a system for automatically determining a person's level of impairment, which is crucial for tasks requiring full mental and physical capability. The system employs a light and camera-based approach to detect signs of impairment such as gaze nystagmus, an involuntary eye movement indicative of inebriation. By simulating light motion and capturing eye region images, the system uses neural networks to analyze user behavior and determine impairment levels. Based on this analysis, the system can either permit or block the individual from performing tasks like vehicle operation if impairment exceeds allowable limits.
Ensuring that human operators are not impaired is vital in scenarios like vehicle navigation and robotic control. Traditional methods involved manual tests for detecting nystagmus, often conducted by police officers using a pen to track eye movements. These manual tests can be impractical, imprecise, and inconsistent due to varying levels of experience among testers. The automated system aims to address these issues by providing a consistent and objective method for determining impairment.
The invention is applicable across various systems and industries, including non-autonomous and semi-autonomous vehicles, robotics, augmented reality, security, and cloud computing. It supports diverse applications such as machine control, simulation, deep learning, and digital twinning. Systems can range from automotive control systems to aerial and medical systems, leveraging edge devices or cloud resources for operations like synthetic data generation and light transport simulation.
The system automates the assessment of a person's capacity or impairment relevant to task performance. It can detect inebriation in environments like vehicles using a light bar that simulates motion patterns. Cameras capture eye movements to infer impairment through neural network analysis. This automated approach prevents impaired individuals from operating vehicles by analyzing factors such as eye motion smoothness and nystagmus presence.
An example implementation involves a vehicle with a light bar positioned on the driver-side visor. The light bar activates LEDs in sequences that simulate motion patterns for the user's eyes to follow. A camera captures eye region data to assess impairment levels. The system ensures accurate data capture by accounting for head movement and providing notifications if excessive movement occurs. This setup allows objective determination of whether an individual can safely operate the vehicle based on detected impairments.