Driver Gaze Tracking With Primary Preview Region Alerts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Distracted driving leads to a significant number of accidents, causing substantial loss of lives and economic harm, as drivers fail to pay attention to the road and obstacles.
Innovation Solution
A system and method for detecting distracted driving by determining a primary preview region (PPR) in a vehicle's environment and tracking the driver's gaze, generating alerts when the gaze is outside the PPR, using cameras and machine learning to identify critical objects and regions, and adjusting attention levels based on gaze focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the driver's attention is monitored continuously using gaze tracking, then driver distraction detection accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the driver monitoring task into multiple components: gaze point detection from facial images, primary preview region identification from environmental images, and attention level calculation based on gaze-PPR overlap. This segmentation allows each component to be processed independently with appropriate algorithms, reducing overall system complexity while maintaining detection accuracy.
Solution Approach 2:
The patent introduces an intermediary attention level metric that bridges gaze tracking data and distraction detection outcomes. Instead of directly classifying distraction states from raw gaze data, the system calculates an attention level based on the overlap between gaze point and primary preview region, providing a continuous intermediate measure that simplifies the decision-making process.
2Reliability
If multiple cameras and sensors are used to track driver gaze and environment, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The system employs multi-functional cameras that serve multiple purposes: capturing driver facial images for gaze detection, capturing environmental images for primary preview region identification, and potentially serving as both input and reference sources. This multi-functionality reduces the need for separate dedicated sensors for each function, lowering device complexity while maintaining detection reliability.
3Reliability
If real-time gaze tracking and alert generation is implemented, then driver safety is improved, but processing time and energy consumption increase
Solution Approach 1:
The system implements partial monitoring by focusing computational resources on the primary preview region rather than analyzing the entire field of view. By identifying and prioritizing specific regions of interest in the environment, the system reduces the computational burden of continuous image processing while maintaining safety-critical detection capabilities.
Solution Approach 2:
The attention level is updated periodically based on gaze point measurements rather than requiring continuous real-time processing at maximum resolution. This periodic updating approach, combined with the use of logistic decay function for attention level adjustment, reduces processing energy consumption while maintaining adequate detection reliability for safety applications.
Data Source
AI summary
A computer-implemented method of detecting distracted driving comprises: determining, by one or more processors, a primary preview region (PPR) in a representation of an environment; determining, by the one or more processors, a gaze point for a driver based on a sequence of images of the driver; determining, by the one or more processors, that the gaze point is outside of the PPR; based on the determined gaze point being outside of the PPR, decreasing, by the one or more processors, an attention level for the PPR; based on the attention level for the PPR, generating, by the one or more processors, an alert.