Ultrasonic Near-Object Detection Using Derived Range Parameters
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Solution Overview
Problem
Existing ultrasonic sensors in vehicles are unable to accurately detect objects in near-object ranges, such as those closer than 0.15 meters, due to physical limitations and multiple echoes, leading to false range values and potential collisions.
Innovation Solution
A system utilizing ultrasonic sensors that monitors parameters like average, slope, and variation of range measurements to determine whether an object is in a near-object range, allowing the system to operate in a near-object or non-near-object state, thereby enhancing detection accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultrasonic sensors are used for object detection in vehicle-parking-assist functions, then object detection capability is provided, but detection accuracy deteriorates in near-object ranges (closer than 0.15 meters) due to physical limitations of the transducer and multiple echoes
Solution Approach 1:
The system changes the processing parameters by computing derived measurements (average, slope, variation) from multiple raw range measurements. This transforms the raw data into processed parameters that can distinguish near-object echoes from valid reflections, resolving the contradiction between providing detection coverage and maintaining accuracy in near-object ranges
Solution Approach 2:
The system introduces an intermediary processing layer that computes derived measurements as intermediates between raw sensor data and final detection decisions. These intermediate calculations (average, slope, variation) serve as mediators to filter out false near-object detections while preserving valid detections
2Productivity
If the system uses raw range measurements directly for detection, then the detection process is simple and fast, but false readings occur in near-object ranges due to multiple echoes
Solution Approach 1:
The system performs partial processing by computing only essential derived measurements (average, slope, variation) from the raw data rather than exhaustive analysis. This partial action maintains detection speed while sufficiently eliminating false readings in near-object ranges
Solution Approach 2:
The system uses feedback from multiple measurements by comparing derived measurements across successive readings. The variation calculation provides feedback about measurement consistency, allowing the system to filter false readings while maintaining fast detection response
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
This approach improves the accuracy of near-object detection, reducing false readings and enhancing safe driving by using derived parameters to differentiate between near-object and non-near-object ranges, thus aiding in preventing collisions.
Implementation Method 1
Automotive manufacturers often use ultrasonic sensors for object detection in vehicle-parking-assist functions
Implementation Method 2
an object-detection system of the vehicle can utilize raw range measurements and various parameters derived from the raw range measurements
Data Source
AI summary
This document describes near-object detection using ultrasonic sensors. Specifically, when an object is in a near-object range or distance from a vehicle, an object-detection system of the vehicle can utilize raw range measurements and various parameters derived from the raw range measurements. The various parameters may include an average, a slope, and a variation of the range. In the near-object range, using the parameters derived from the raw range measurements may lead to increases in the accuracy and performance of a vehicle-based object-detection system. The increased accuracy in near-object detection capability enhances safe driving.