Autonomous Confidence Control for Vehicle Mode Transitions
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Solution Overview
Problem
Fully and semi-autonomous vehicles face challenges in determining when to hand control back to a human operator, as they lack sufficient information to differentiate between intentional and unintentional human input, and may struggle with unclear environmental conditions, leading to potential safety issues.
Innovation Solution
The implementation of a vehicle control system that uses sensor data collectors and fuzzy logic analysis to determine operational factors such as alertness, readiness, autonomous confidence, and peril, allowing the vehicle to make informed decisions about mode transitions between autonomous, semi-autonomous, and manual control based on real-time data and historical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the vehicle computer relies solely on sensor data to determine environmental conditions, then autonomous control decisions can be made, but the system may lack sufficient information to accurately assess uncertain or ambiguous situations
Solution Approach 1:
The patent introduces an intermediary assessment mechanism that evaluates the sufficiency and reliability of sensor data before autonomous control decisions are made. This intermediary layer determines whether the available information is adequate for safe autonomous operation, effectively mediating between raw sensor data and control decisions.
Solution Approach 2:
The system implements feedback loops where the vehicle computer continuously monitors sensor data quality and environmental condition clarity. When information sufficiency drops below thresholds, the system feedbacks to request human operator intervention, creating a closed-loop information assessment system.
2Ease of operation
If the vehicle computer monitors human operator inputs to determine when to hand back control, then control transitions can be managed, but the system cannot reliably distinguish between intentional and unintentional inputs
Solution Approach 1:
The patent segments the control input monitoring into multiple independent assessment dimensions: physical input characteristics, contextual driving conditions, operator state indicators, and temporal patterns. Each dimension is evaluated separately to build a comprehensive understanding of input intent.
Solution Approach 2:
The system dynamically adjusts the thresholds and criteria for distinguishing intentional versus unintentional inputs based on contextual factors such as driving conditions, vehicle state, and operator behavior patterns. This dynamic assessment adapts to varying operational scenarios.
3Productivity
If the vehicle computer operates in fully autonomous mode, then productivity is improved, but safety risks increase when sensor data is insufficient or environmental conditions are unclear
Solution Approach 1:
The patent implements partial autonomous operation where the vehicle computer autonomously controls vehicle functions only when information sufficiency and environmental clarity meet required thresholds. When conditions are uncertain or ambiguous, the system transitions to reduced autonomous operation, allowing human operator involvement.
Solution Approach 2:
The system establishes predetermined safety thresholds and decision criteria before autonomous operation begins. These pre-established boundaries cushion against safety risks by automatically limiting autonomous control authority when environmental conditions or sensor data quality fall below acceptable levels.
Data Source
AI summary
Signals are from a plurality of sources representing aspects of the vehicle and an environment surrounding the vehicle. An autonomous confidence factor is developed based on component confidence levels for at least one of the signals. Control of the vehicle is transitioned between levels of autonomous control based at least in part on the autonomous confidence factor.