Vehicle Cabin Air Intake Control Using Predicted Roadside Pollution
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Solution Overview
Problem
Existing methods for controlling vehicle interior air quality do not effectively predict and adjust outside air supply based on future pollutant levels, leading to potential exposure of occupants to high pollutant concentrations.
Innovation Solution
A method that utilizes a vehicle-mounted pollutant sensor and visual detection unit to predict future pollutant levels by analyzing visual information and using machine learning to train a time-delayed model, which adjusts the outside air supply to minimize pollutant entry into the vehicle interior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If outside air is continuously supplied to the vehicle interior for ventilation, then air freshness and oxygen supply are improved, but pollutant exposure of occupants increases when external pollutant levels are high
Solution Approach 1:
The system performs preliminary detection of external pollutant levels using sensors and cameras before pollutants can significantly enter the vehicle interior. Based on this advance detection, the ventilation system proactively adjusts outside air supply to prevent pollutant accumulation, rather than reacting after pollutants have already contaminated the interior air.
Solution Approach 2:
The system continuously monitors external air quality parameters (pollutant concentrations, visual pollution indicators) and uses this feedback to dynamically adjust the ventilation system's outside air supply. This closed-loop control ensures that ventilation maintains air freshness while preventing pollutant exposure by reducing outside air intake when external quality deteriorates.
2Reliability
If pollutant detection and response systems are enhanced to predict future pollutant levels, then occupant health protection is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system trains machine learning models in advance using historical sensor data and visual information to create predictive capabilities. Once trained, these models can quickly predict future pollutant levels without requiring complex real-time computations, enabling proactive health protection while maintaining manageable system complexity during vehicle operation.
Solution Approach 2:
The system uses visual detection units (cameras) to capture optical copies of the external environment and compares these visual representations against trained models to identify pollutant sources. This copying approach allows the system to detect pollution conditions through image processing rather than requiring multiple complex physical sensors for every pollutant type.
3Measurement precision
If visual detection units and machine learning models are added to predict pollutant levels, then prediction accuracy is improved, but device complexity and energy consumption increase
Solution Approach 1:
The system uses visual detection units and machine learning models selectively based on predicted pollution risk levels. During low-risk conditions, the system reduces the frequency of visual analysis and model inference operations, consuming less energy. When high pollution risk is detected or predicted, the system intensifies monitoring and prediction activities to maintain high measurement precision when it matters most.
Data Source
Figure 1
AI summary
The invention relates to a method for adjusting a supply of external air into an interior compartment of a vehicle (1), wherein an interior compartment pollutant load is ascertained continuously during driving operation of the vehicle (1) on the basis of signals detected by a pollutant sensor (2) arranged in the interior compartment. According to the invention, provision is made for a pollutant load of external air on a route section ahead of the vehicle (1) to be predicted, wherein the supply of external air is automatically controlled in closed-loop fashion in a manner dependent on the predicted pollutant load of the external air.