Multi-Sensor Gesture Interface for Vehicle Safety
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Solution Overview
Problem
Existing user interface designs for computing systems in vehicles are not intuitive and can be distracting, leading to safety concerns due to the need for drivers to search for on-screen or physical buttons, especially in varying lighting conditions.
Innovation Solution
A multi-sensor system combining short-range radar, depth camera, and optical sensors for dynamic hand-gesture recognition, using convolutional deep neural networks to classify gestures and reduce power consumption through selective sensor operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional on-screen or physical button interfaces are used, then users can interact with computing systems, but users experience distraction and safety concerns due to the need to search for controls
Solution Approach 1:
The patent replaces traditional mechanical button interfaces with a gesture recognition system that uses radar, depth, and optical sensors to detect and interpret hand movements. This substitution eliminates the need for physical interaction with buttons or knobs, allowing drivers to control computing systems through natural hand gestures in the air, thereby reducing distraction and improving ease of operation
Solution Approach 2:
The patent introduces multiple sensors (radar, depth, optical) as intermediaries between the driver and the computing system. These sensors capture hand gesture data from the driver and transmit it to a processing element that interprets the gestures and executes corresponding commands, providing a natural and intuitive interface that reduces the cognitive load and distraction associated with traditional controls
2Measurement precision
If multiple sensors are used for gesture recognition, then accuracy and robustness to lighting conditions improve, but power consumption increases
Solution Approach 1:
The patent implements selective and periodic operation of multiple sensors based on detected motion levels. The radar sensor operates continuously at low power to detect motion, and when motion exceeds a threshold, the higher-power depth and optical sensors are activated for detailed gesture recognition. This periodic activation strategy maintains high measurement precision while significantly reducing overall power consumption compared to continuous operation of all sensors
Solution Approach 2:
The patent dynamically adjusts sensor operation based on real-time conditions. The system monitors motion detection data from the radar sensor and dynamically activates or deactivates the depth and optical sensors according to the detected motion level. This dynamic adaptation allows the system to maintain high gesture recognition accuracy when needed while minimizing power consumption during periods of low activity
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
The system allows for intuitive and natural interaction with computing devices in vehicles, reducing distraction and improving safety by accurately recognizing gestures under various lighting conditions while minimizing power consumption.
Implementation Method 1
a radar sensor, a depth sensor, and an optical sensor coupled to a processing element, wherein the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection
Implementation Method 2
a depth sensor... configured for short range gesture detection
Data Source
AI summary
An apparatus and method for gesture detection and recognition. The apparatus includes a processing element, a radar sensor, a depth sensor, and an optical sensor. The radar sensor, the depth sensor, and the optical sensor are coupled to the processing element, and the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection and recognition. The processing element is further configured to detect and recognize a hand gesture based on data acquired with the radar sensor, the depth sensor, and the optical sensor.