In-Vehicle Cabin Scene Estimation via Sensor Fusion
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Solution Overview
Problem
Autonomous vehicles lack the ability to effectively sense and understand the interior environment, which is crucial for maintenance needs and emergency situations without a human driver, highlighting a gap in current systems for monitoring the vehicle cabin.
Innovation Solution
A system comprising multiple sensors and a processing system that determines attributes of the cabin interior by receiving sensor signals, combining data from local and remote sensors to estimate complex attributes such as air quality, passenger conditions, and object presence, with a virtual assistant triggering actions based on these estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are deployed to monitor the cabin interior, then the ability to detect interior events and understand the vehicle state is improved, but the device complexity and cost increase
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor units, each responsible for detecting specific interior events (e.g., motion sensors for passenger presence, temperature sensors for climate monitoring, light sensors for cabin illumination). This segmentation allows the complex monitoring task to be divided into manageable components, improving reliability while keeping individual sensor units simple and cost-effective.
Solution Approach 2:
The sensor system is designed with multi-functionality where sensors can serve multiple purposes. For example, cameras can detect both passenger presence and facial expressions, microphones can monitor both speech for voice commands and ambient sounds for emergency detection. This universality reduces the total number of sensors needed, lowering complexity while maintaining comprehensive monitoring capability.
2Measurement precision
If comprehensive sensor data is collected and processed to determine multiple cabin attributes, then the accuracy of interior scene estimation is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary processing of sensor data by pre-defining detection algorithms and thresholds for various interior events. For example, motion detection thresholds, temperature alarm levels, and facial expression recognition models are pre-configured. This preliminary action allows the system to quickly compare incoming sensor data against predetermined criteria, improving scene estimation accuracy while minimizing real-time processing time.
Solution Approach 2:
An intermediary processing layer is introduced between raw sensor data and final scene estimation. This intermediary layer includes data fusion modules that aggregate information from multiple sensors, feature extraction algorithms that identify relevant patterns, and filtering mechanisms that remove noise. This intermediary processing improves measurement precision by synthesizing comprehensive sensor data while managing computational load through optimized algorithms.
3Adaptability or versatility
If the system monitors multiple attributes simultaneously (air quality, passenger conditions, object presence), then the versatility of the monitoring system is improved, but the device complexity increases
Solution Approach 1:
The system merges multiple monitoring functions into an integrated cabin monitoring platform. Different sensor types (air quality sensors, motion sensors, cameras, microphones) are combined with a central processing unit that handles multiple attribute detection simultaneously. This merging approach improves versatility by providing comprehensive monitoring coverage while managing complexity through a unified system architecture rather than separate independent systems.
Solution Approach 2:
The monitoring system incorporates self-service capabilities where the system automatically configures and adjusts its monitoring parameters based on detected conditions. For example, the system can automatically activate specific monitoring modes (e.g., emergency detection, climate control, passenger comfort) based on sensor inputs without requiring manual intervention. This self-service feature expands monitoring versatility while reducing the complexity of system configuration and management.
Data Source
AI summary
An in-vehicle system and method are disclosed for monitoring or estimating a scene inside a cabin of the vehicle. 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.