Vehicle Feature Assumption Validation Through Driver Feedback
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Solution Overview
Problem
Existing technologies face challenges in accurately assessing and verifying assumptions related to driver behavior in vehicle systems, which can impact safety and efficiency in assisted and automated driving features.
Innovation Solution
A method and system for monitoring driver behavior and calculating a confidence level associated with predefined assumptions about driver responses to in-vehicle stimuli, allowing for adaptive adjustments in vehicle features based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If assumptions about driver behavior are used to control vehicle features, then device complexity is reduced and ease of operation is improved, but reliability deteriorates due to potential invalid assumptions about driver responses
Solution Approach 1:
The system continuously monitors actual driver behavior and compares it against stored assumptions about driver responses. When discrepancies are detected, the system adjusts control parameters or notifies the driver, creating a feedback loop that validates and refines behavioral assumptions over time, thereby maintaining reliability while preserving ease of operation
Solution Approach 2:
The system pre-stores multiple assumptions about driver behavior patterns and timing information before operation. During vehicle operation, it selectively applies these pre-validating assumptions to control features, allowing the system to operate with simplified logic while relying on pre-validated behavioral models to ensure reliability
2Measurement precision
If behavior monitoring of multiple drivers is implemented, then measurement precision of driver behavior improves, but device complexity increases due to additional monitoring and calculation requirements
Solution Approach 1:
The system combines monitoring data from multiple drivers into aggregated behavioral profiles. By merging individual behavior patterns into composite models, the system achieves higher measurement precision through larger data samples while reducing device complexity by processing unified datasets rather than maintaining separate complex monitoring systems for each driver
Solution Approach 2:
The monitoring system is designed to universally track multiple drivers using the same sensors and processing logic. This multi-functional approach allows a single monitoring infrastructure to serve multiple purposes: individual driver profiling, comparative analysis, and aggregated statistics, thereby improving measurement precision without proportionally increasing device complexity
Data Source
AI summary
Disclosed are techniques for vehicle feature assumption evaluation. In an aspect, an assumption associated a vehicle feature defines (i) at least one response by an attentive driver to at least one in-vehicle human-machine communications interface stimulus, (ii) timing information associated with at least one in-vehicle driver action, or (iii) a combination thereof. Behavior of attentive driver(s) is monitored, and used to calculate a confidence level associated with the assumption being valid based on the monitoring.