Cabin Scene Estimation for Autonomous Vehicle Monitoring
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Solution Overview
Problem
Autonomous vehicles lack effective systems to monitor and understand the interior state of their cabins, including identifying left-behind objects, maintenance needs, and emergency situations, due to the absence of a human driver.
Innovation Solution
A system comprising multiple sensors and a processing system that determines reference and current values for cabin attributes, comparing them to detect changes and trigger appropriate actions, such as cleaning or notifications, using sensor fusion and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are deployed to monitor cabin attributes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor units, each responsible for detecting specific cabin attributes (temperature, humidity, air quality, etc.). This segmentation allows the system to achieve comprehensive monitoring precision while maintaining modularity, reducing the complexity burden of having multiple sensors by organizing them as independent functional modules.
Solution Approach 2:
The processing system is designed with multi-functionality to handle data from various sensor types uniformly. It can process temperature data, humidity data, air quality data, and other cabin attribute data through a unified processing framework, reducing system complexity by avoiding the need for separate processing paths for each sensor type.
2Reliability
If reference values and current values are compared to detect changes, then reliability of change detection is improved, but loss of time for data processing increases
Solution Approach 1:
Reference values for cabin attributes are pre-established and stored in the processing system before actual monitoring begins. These reference values represent the normal or baseline state of the cabin. By having these values prepared in advance, the system can perform rapid comparison with current sensor readings without needing to calculate or retrieve baseline data during real-time operation, thus reducing processing time while maintaining detection reliability.
3Measurement precision
If sensor fusion techniques are used to determine composite attributes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The processing system merges data from multiple sensors to determine composite cabin attributes such as overall air quality, comfort level, or safety status. By combining temperature, humidity, CO2 concentration, and other individual sensor readings into unified composite metrics, the system achieves more accurate and comprehensive measurements while managing complexity through integrated processing algorithms.
Data Source
AI summary
An in-vehicle system and method are disclosed for identifying changes in the scene inside the cabin of the vehicle before and after usage thereof. The in-vehicle system includes a plurality of sensors that measure, capture, and/or receive data relating to attributes the interior of the cabin. The in-vehicle system includes a scene estimator that determines and/or estimates one or more attributes of the interior of the cabin based on individual sensor signals received from the sensors. The scene estimator determines additional attributes based on combinations of one or more of the attributes determined based on the sensor signals individually. The attributes determined by the scene estimator collectively comprise an estimation of the scene inside the cabin of the vehicle. The scene estimator determines changes in the scene inside the cabin based on changes in the scene estimation before and after usage thereof.