Vehicle Occupant Classification Using Weight and Presence Sensing
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Solution Overview
Problem
Current vehicle occupant classification systems face challenges in accurately differentiating between children and relatively small adults, particularly in varying environmental conditions, which can lead to inappropriate airbag deployment during collisions.
Innovation Solution
A system comprising occupant weight sensors, presence sensors, and logic devices that communicate to determine estimated occupant weights and presence responses, incorporating environmental conditions to provide reliable and accurate occupant classification statuses, enabling appropriate airbag deployment adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If weight-based classification is used to control airbag deployment, then airbag deployment can be adjusted for relatively small adults, but young children cannot be reliably differentiated from small adults despite having similar weights
Solution Approach 1:
The classification system is segmented into multiple independent sensing dimensions: weight measurement, presence detection, and environmental condition monitoring. Each sensor type provides a separate measurement that is processed independently before being combined for final classification, allowing the system to distinguish between children and small adults who may have similar weights but differ in other characteristics.
Solution Approach 2:
The system transitions from one-dimensional weight-based classification to multi-dimensional classification by incorporating presence sensor data and environmental condition data. This dimensional expansion allows the system to create a more nuanced occupant profile that can reliably differentiate between children and small adults who would be indistinguishable using weight alone.
2Measurement precision
If multiple sensors and environmental compensations are added to improve classification accuracy, then occupant differentiation improves, but system complexity increases
Solution Approach 1:
The logic device is designed as a universal processor that handles multiple sensor types (weight sensors, presence sensors) and performs multiple functions (data acquisition, environmental compensation, classification determination). This multi-functional approach consolidates what could be separate complex subsystems into a single integrated unit, improving accuracy while controlling overall system complexity.
Solution Approach 2:
Multiple sensing functions and processing operations are merged into the logic device. The weight sensor signals, presence sensor signals, and environmental condition data are all processed together in a unified classification algorithm, reducing the need for separate dedicated hardware for each function and simplifying the overall system architecture.
3Reliability
If environmental conditions are compensated for in sensor readings, then classification reliability improves under varying conditions, but processing requirements and system complexity increase
Solution Approach 1:
Environmental compensation is performed as a preliminary processing step before final classification determination. The logic device first acquires environmental condition data, then applies compensation adjustments to the weight and presence sensor readings based on these conditions, and finally performs classification on the compensated data. This preliminary action ensures reliable classification across varying environmental conditions while organizing the processing workflow efficiently.
Data Source
AI summary
Techniques are disclosed for systems and methods to detect and/or classify a vehicle occupant, such as a passenger seated within the cockpit of a vehicle. An occupant classification system includes an occupant weight sensor, an occupant presence sensor, and a logic device configured to communicate with the occupant weight sensor and the occupant presence sensor. The logic device is configured to receive occupant weight sensor signals from the occupant weight sensor and occupant presence sensor signals from the occupant presence sensor, determine an estimated occupant weight and an occupant presence response based, at least in part, on the occupant weight sensor signals and the occupant presence sensor signals, and determine an occupant classification status corresponding to the passenger seat based, at least in part, on the estimated occupant weight and/or the occupant presence response.