Driver Preparedness Tracking for Autonomous-Manual Vehicle Handover
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Solution Overview
Problem
Existing systems fail to effectively determine the preparedness of vehicle operators to transition between autonomous and manual driving modes, posing safety risks due to inadequate assessment of operator readiness.
Innovation Solution
A system comprising sensors, processors, and computer program components that capture and analyze various types of information to gauge operator preparedness, present challenges, and adjust vehicle operation modes based on operator responsiveness and confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system implements real-time assessment of operator preparedness using multiple sensors and challenge-response mechanisms, then the safety and reliability of mode transitions improve, but the device complexity and computational requirements increase
Solution Approach 1:
The system divides the operator assessment into multiple independent challenge types (visual, auditory, tactile) and separate sensor modules, allowing the complex assessment task to be broken down into manageable segments that can be processed independently and combined for overall preparedness determination
Solution Approach 2:
The system introduces challenge-response mechanisms as intermediary elements between the operator and the autonomous system, using these challenges as mediators to indirectly assess operator state through behavioral responses rather than directly measuring complex physiological parameters
2Measurement precision
If the system continuously monitors operator state using multiple sensors, then the measurement precision of operator preparedness improves, but the energy consumption and computational load increase
Solution Approach 1:
The system implements periodic challenge-response sequences rather than continuous monitoring, presenting challenges at intervals and using these periodic assessments to gauge operator state, thereby reducing continuous energy consumption while maintaining measurement precision through strategic sampling
Solution Approach 2:
The system changes the parameters of challenges dynamically based on operator performance and context, adjusting challenge difficulty and frequency to optimize the balance between measurement precision and energy consumption, using less intensive monitoring when operator state is stable and more intensive assessment when transitions are anticipated
Data Source
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AI summary
This disclosure relates to a system and method for determining vehicle operator preparedness for vehicles that support both autonomous operation and manual operation. The system includes sensors configured to generate output signals conveying information related to vehicles and their operation. During autonomous vehicle operation, the system gauges the level of responsiveness of an individual vehicle operator through challenges and corresponding responses. Based on the level of responsiveness, a preparedness metric is determined for each vehicle operator individually.