Vehicle Collision Warning Timing via Driver Gaze Analysis
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Solution Overview
Problem
Conventional collision avoidance assistance systems provide collision warnings at a calculated time without considering the driver's recognition of the forward object, leading to discomfort and inefficiency.
Innovation Solution
A vehicle system that includes a distance sensor, camera, and driver recognition sensor to analyze the driver's gaze and field of view, calculating a collision warning time based on a recognition index derived from the driver's attention distribution function and visibility index, allowing for optimal timing of collision risk notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collision warning time is calculated based only on object type and distance data, then the calculation is simple and fast, but the driver comfort and recognition effectiveness deteriorate
Solution Approach 1:
The system performs preliminary analysis of driver gaze and field of view using facial data before calculating the final warning time. This allows the system to pre-determine the driver's attention distribution function and use it to adjust the warning timing, ensuring the warning is provided at an optimal moment when the driver is most likely to recognize it, thereby improving both driver comfort and recognition effectiveness
Solution Approach 2:
The system continuously monitors driver gaze direction and field of view using facial recognition sensors, and uses this feedback information to dynamically adjust the collision warning time. By incorporating real-time driver attention state into the warning calculation, the system optimizes the timing to match actual driver recognition patterns, resolving the contradiction between simple calculation and driver comfort
2Ease of operation
If driver gaze and field of view analysis is incorporated into collision warning time calculation, then driver comfort and recognition effectiveness improve, but the system complexity increases
Solution Approach 1:
The controller performs multiple functions using the same facial data: it analyzes driver gaze direction, determines field of view boundaries, calculates attention distribution function, and uses this information to adjust warning timing. By making the controller multi-functional, the system avoids adding separate dedicated hardware for each function, thereby improving driver comfort while limiting the increase in overall system complexity
Solution Approach 2:
The system introduces an attention distribution function as an intermediary mathematical model that bridges driver facial data and collision warning timing. This intermediary function simplifies the complex relationship between driver gaze patterns and optimal warning moments, allowing the system to achieve improved driver comfort through a manageable computational approach rather than complex hardware modifications
3Ease of manufacture
If warning time is fixed without considering driver recognition, then the system is simple to implement, but the accident prevention effectiveness deteriorates
Solution Approach 1:
The system dynamically changes the warning time parameter based on driver gaze analysis results. Instead of using a fixed warning time, the system adjusts the timing parameter according to the driver's actual attention state and field of view, thereby improving accident prevention effectiveness while maintaining reasonable system implementation complexity through parameter optimization rather than structural complexity
Data Source
AI summary
A vehicle, which adjusts a collision warning time based on driver's field of view and visibility of the object, is provided. The vehicle includes: a distance sensor that collects data of an object, a camera that collects image data, a driver recognition sensor that collects driver's facial data, and a controller that analyzes driver's gaze and field of view using the driver's facial data, analyzes visibility of the object using the image data, and calculates a collision warning time based on a result of analyzing the driver's gaze and field of view and the visibility of the object, and an alarm device that informs the driver of the risk of collision at the collision warning time under the control of the controller.