Driver Classification for Vehicle Safety Optimization
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Solution Overview
Problem
Modern vehicles' safety features, such as airbag systems and seatbelt load-limiters, are typically set for an 'average' person, failing to optimize protection for individuals of varying weights and sizes, leading to suboptimal safety performance during crashes.
Innovation Solution
A system and method that classify drivers based on height and mass, determining optimal safety feature settings through design optimization analysis, categorizing drivers into specific classes, and automatically adjusting vehicle safety systems to provide personalized protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If safety features are set for an average person, then the system complexity is reduced and ease of manufacture is improved, but crash safety performance deteriorates for individuals of varying weights and sizes
Solution Approach 1:
The patent segments the driver population into distinct size classes (small, medium, large) based on height and mass percentiles. Each class has pre-calculated optimal safety feature settings, transforming a continuous optimization problem into discrete segments that can be manufactured and deployed without real-time computation
Solution Approach 2:
The patent performs design optimization analysis in advance for each driver size class during the manufacturing phase. The optimal settings for airbag parameters, seatbelt load-limiter forces, and other safety features are pre-determined and stored in lookup tables, eliminating the need for complex real-time calculations during crashes
2Reliability
If safety features are personalized for each individual driver, then crash safety performance is improved, but device complexity and measurement precision requirements increase
Solution Approach 1:
The patent changes the approach from continuous individual optimization to discrete parameter selection. By defining driver size in terms of height and mass percentiles (e.g., 5th, 50th, 95th percentiles), the system uses parameter discretization to reduce complexity while maintaining personalized protection for each driver class
3Ease of operation
If driver classification into finite classes is implemented, then ease of operation is improved and device complexity is reduced, but manufacturing precision requirements increase to accurately categorize drivers
Solution Approach 1:
The patent creates universal classification criteria based on height and mass percentiles that can be applied across different vehicle models and safety feature configurations. The same classification methodology serves multiple functions: driver categorization, safety feature optimization, and system control, reducing the need for model-specific precision requirements
Data Source
AI summary
A system and method for classifying the optimization of safety features on a vehicle for a driver of the vehicle based on the height and mass of the driver. The method includes determining a number of basic driver sizes based on driver height and mass and determining a driver's seat position for each basic driver height. The method also identifies a set of tunable design variables that are used to adjust the safety features of the vehicle and performs design optimization analysis to identify an optimal design for the vehicle safety features for each of the basic driver sizes. The method then identifies the design from the optimal designs that provides the best performance for randomly selected reference drivers and classifies all drivers into a predetermined number of classifications where each classification represents a particular optimal design.