Autonomous Vehicle Control Authority Allocation Based on Driver State
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Solution Overview
Problem
Current autonomous driving technologies fail to adaptively shift vehicle control authority based on road conditions, weather, and driver proficiency levels, particularly in levels 3 to 5 of autonomous driving, and do not account for individual driver differences.
Innovation Solution
A method and system that determine a driver's physical, mental, and driving proficiency levels using sensors and AI, allocating control authority between the driver and the vehicle based on risk assessments from weather, road, and driving patterns, with AI-driven sensors and databases managing control authority distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous driving control is implemented at levels 3 to 5, then automation extent is improved, but adaptability of control authority allocation deteriorates
Solution Approach 1:
The patent implements dynamic control authority allocation that adapts to changing driving conditions, driver states, and environmental factors. The system continuously monitors multiple parameters and adjusts control authority in real-time, transitioning between autonomous and manual control modes based on assessed risk levels and driver proficiency, thereby resolving the contradiction between high automation and adaptability
Solution Approach 2:
The system changes multiple parameters simultaneously including driver proficiency level, physical state, mental state, environmental conditions, road conditions, and weather conditions to determine optimal control authority allocation. By monitoring and responding to changes in these parameters, the system maintains adaptability while operating at high autonomous driving levels
2Extent of automation
If control authority is fixed to autonomous vehicle system, then extent of automation is improved, but reliability deteriorates due to inability to respond to driver conditions and environmental changes
Solution Approach 1:
The patent implements comprehensive feedback mechanisms that continuously monitor driver physical state, mental state, environmental conditions, road conditions, and weather conditions. This feedback loop enables the system to assess risk levels and adjust control authority allocation dynamically, ensuring reliable and safe operation by responding to changing conditions while maintaining high automation
Solution Approach 2:
The system performs preliminary assessment of driver proficiency, physical state, and environmental conditions before allocating control authority. By evaluating multiple parameters in advance and preparing appropriate control modes, the system ensures reliable and safe autonomous driving operation
Data Source
AI summary
A method for changing a control authority of an autonomous vehicle in consideration of an external environment includes determining a first risk level of a physical condition of a driver who drives the autonomous vehicle, determining a second risk level in response to one of a mental condition or a conscious condition of the driver, determining a driver proficiency level of a driver, and allocating a control authority of the autonomous vehicle to the driver or to the autonomous vehicle according to a result of a determination of the first risk level, the second risk level, and the driver proficiency level.