Rear Seat Occupant Detection Using Camera-Radar Confirmation
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Solution Overview
Problem
Conventional rear occupant alert systems in vehicles suffer from false detections due to movements similar to human activity, such as water shaking in a bottle or vehicle shaking from external shocks, leading to potential safety hazards.
Innovation Solution
An apparatus utilizing an indoor camera and indoor radar to provide a rear occupant alert, where a controller processes image and radar data to accurately identify and confirm the presence of a human in the rear seat, using digital signal processing and machine learning algorithms to differentiate between human and non-human movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional alert systems use simple motion detection, then the system is easy to operate and low cost, but false detection occurs due to movements similar to human activity (water shaking in bottles, vehicle shaking from external shocks)
Solution Approach 1:
The patent combines multiple sensing technologies (camera, radar, ultrasonic sensors, temperature sensors, humidity sensors) into an integrated detection system. The controller processes data from all these sensors simultaneously to determine whether a detected object is a human or a false target, thereby improving detection accuracy while managing system complexity through unified control.
Solution Approach 2:
The controller acts as an intermediary that processes and analyzes data from multiple sensors before making a final determination. It uses algorithms to interpret sensor signals, distinguish human movements from false targets, and coordinate the warning output, thereby resolving the contradiction between reliability and complexity.
2Reliability
If the system uses multiple sensors and complex processing, then false detection is reduced, but the device complexity and processing requirements increase
Solution Approach 1:
The system divides the detection task into multiple independent sensing components (camera for visual identification, radar for motion detection, ultrasonic sensors for proximity detection, temperature and humidity sensors for environmental context). Each sensor handles a specific aspect of detection, and the controller integrates these segmented data streams to achieve reliable human detection while keeping individual component complexity manageable.
3Measurement precision
If the system processes detailed image and radar data, then identification accuracy improves, but the processing time and computational requirements increase
Solution Approach 1:
The system processes only the necessary portion of sensor data required for human identification. The controller selectively analyzes relevant features from image and radar data rather than processing all possible information, achieving sufficient identification precision without excessive processing time. The system processes data to the extent needed to distinguish humans from false targets.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Significantly reduces false detection rates, ensuring a reliable safety function by accurately alerting drivers to the presence of humans or pets in the rear seat, thereby reducing the risk of safety accidents.
Implementation Method 1
an indoor radar having a sensing area for the vehicle interior and configured to provide radar data
Implementation Method 2
an indoor camera having a field of view for a vehicle interior and configured provide image data
Data Source
AI summary
Disclosed herein is an apparatus for rear seat detection. The apparatus including: an indoor camera having a field of view for a vehicle interior and providing image data; an indoor radar having a sensing area for the vehicle interior and providing radar data; and a controller including a first processor and a second processor, the first processor obtaining identification information on an object in a rear seat of a vehicle based on processing the image data, and a second processor obtaining motion information on the object in the rear seat of the vehicle based on processing the radar data. The controller determining whether the object in the rear seat of the vehicle is a human based on the identification information on the object in the rear seat of the vehicle and the motion information on the object when the vehicle is turned off and a door is locked, and outputting a warning notifying that there is a human in the rear seat of the vehicle based on determining that the object in the rear seat of the vehicle is the human.