Autonomous Driving Control Using Driver State and HUD Gaze Adaptation
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Solution Overview
Problem
Existing vehicle control systems in autonomous driving modes do not consider individual driver preferences and characteristics, leading to limitations in recognizing HUD information, especially due to inadequate consideration of driver gaze position and illumination conditions.
Innovation Solution
A vehicle control system that collects driving situation, infrastructure, and driver state data to learn and determine a preferred autonomous driving pattern, adjusting HUD information display based on driver gaze and illumination conditions to enhance visibility and readability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional autonomous driving control is implemented without considering driver preferences, then the system operates with simpler control logic, but the driving experience does not match individual driver characteristics and preferences
Solution Approach 1:
The system performs preliminary learning of driver preferences and characteristics before actual autonomous driving control is needed. By collecting and analyzing driver behavior data in advance, the system builds a preference model that enables personalized control without adding complexity during real-time operation
Solution Approach 2:
The autonomous driving system automatically adapts to individual driver preferences through self-learning mechanisms. The system collects driver feedback and behavior patterns, then autonomously adjusts control parameters without requiring manual configuration or complex user interfaces
2Measurement precision
If HUD information is displayed without considering driver gaze position, then the display system operates with fixed positioning logic, but the driver cannot reliably recognize information depending on the situation
Solution Approach 1:
The system implements a feedback loop that continuously monitors driver gaze position and adjusts HUD information display accordingly. Eye tracking sensors detect gaze direction, and this information feeds back to dynamically reposition or adjust the content displayed on the HUD, ensuring information appears in the driver's visual field
Solution Approach 2:
The HUD display transitions from a static, fixed-position system to a dynamic system that automatically adjusts display location and characteristics based on real-time driver gaze position. The display parameters such as position, brightness, and content are continuously adapted to match driver attention patterns
3Illumination intensity
If HUD display does not consider illumination conditions, then the display system operates with fixed brightness and color settings, but visibility and readability deteriorate in varying light conditions
Solution Approach 1:
The system dynamically changes display parameters including brightness, contrast, and color temperature of the HUD based on detected illumination conditions. Sensors monitor ambient light levels and the system automatically adjusts display characteristics to maintain optimal visibility across different lighting environments from bright daylight to dark nighttime conditions
Data Source
AI summary
Provided is a system and method for controlling a vehicle. The vehicle control system includes an input unit configured to collect driving situation data and driver's state data, a memory configured to store a program for determining a driving pattern using the driving situation data and the driver's state data in the case of an autonomous driving mode, and a processor configured to execute the program. The processor learns the driving situation data and the driver's state data to determine a driver's preferred driving pattern and transmit an autonomous driving control command according to the driving pattern.