Vehicle Cabin Gas Sensing With Mobile Source Localization
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Solution Overview
Problem
Existing vehicle-based air quality sensors are inadequate in detecting and classifying non-atmospheric gases due to their slow responses and inability to provide specific information about these gases, especially in the context of driverless vehicles where robust, fast-acting, and highly selective sensing is required.
Innovation Solution
A system comprising a movable robot equipped with both fast-acting, non-selective and slow, selective gas sensors, utilizing multi-stage sensing and analysis processes, along with AI to determine the source of non-atmospheric gases within a vehicle cabin, and employing contextual information to refine the location and classification of these gases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single gas sensor is used in a vehicle, then the device complexity is reduced, but the measurement precision and response speed are insufficient for detecting non-atmospheric gases
Solution Approach 1:
The gas sensing system is segmented into multiple specialized sensors: a first gas sensor for fast detection of non-atmospheric gases and a second gas sensor for detailed classification. This segmentation allows each sensor to be optimized for its specific function, improving overall measurement precision without requiring a single complex sensor to handle all detection tasks.
Solution Approach 2:
The system dynamically switches between different sensing modes based on detection needs. The controller activates the fast-acting first gas sensor for rapid detection of gas presence, then selectively engages the second gas sensor for detailed classification when non-atmospheric gases are detected, optimizing the balance between response speed, precision, and device complexity.
2Speed
If a fast-acting non-selective gas sensor is used, then the response speed is improved, but the ability to classify specific gas types is reduced
Solution Approach 1:
The sensing function is divided into two segments: the first gas sensor provides fast, non-selective detection of non-atmospheric gases, while the second gas sensor provides slow, selective classification of specific gas types. This segmentation allows the system to achieve both rapid response and accurate classification by using each sensor for its strengths.
Solution Approach 2:
The controller acts as an intermediary that coordinates between the fast-acting first gas sensor and the selective second gas sensor. When the first sensor detects non-atmospheric gases, it triggers the second sensor to perform detailed classification, mediating between the need for speed and the need for precision in gas identification.
3Measurement precision
If a selective gas sensor is used, then the gas classification accuracy is improved, but the response time increases
Solution Approach 1:
The first gas sensor performs preliminary detection of non-atmospheric gases quickly and non-selectively. Only when non-atmospheric gases are detected does the system activate the second selective gas sensor for detailed classification. This preliminary action approach avoids the time penalty of continuous operation of the slow selective sensor, reducing overall detection response time while maintaining classification accuracy.
Solution Approach 2:
The selective second gas sensor is activated periodically or on-demand based on detections from the first sensor, rather than operating continuously. This periodic activation reduces the average response time and energy consumption while still providing accurate gas classification when needed.
4Loss of information
If multiple gas sensors are deployed throughout the vehicle cabin, then the ability to locate gas sources is improved, but the device complexity and cost increase
Solution Approach 1:
Instead of distributing multiple sensors throughout the vehicle cabin (spatial dimension), the system uses a single mobile robot that moves through the cabin (temporal dimension). The robot equipped with gas sensors maps the cabin environment over time, detecting and classifying non-atmospheric gases and locating their sources through movement and spatial sampling, thereby providing location information without requiring a complex fixed sensor network.
Data Source
AI summary
Systems and methods for gas detection within vehicles are disclosed herein. An example method includes monitoring background gas concentrations in a vehicle using a robot having a gas module having a non-selective sensor and a selective sensor, determining a concern index based on output of the gas module, determining when the concern index exceeds a threshold which indicates presence of a non-atmospheric gas, causing the robot to traverse an operating area when the concern index exceeds the threshold to search for a source of the non-atmospheric gas by measuring gas concentration gradients, classifying the non-atmospheric gas using the selective sensor of the gas module and identifying a location of the source of the non-atmospheric gas in the vehicle based on the gas concentration gradients.