Ultrasonic Obstacle Positioning via Detector Overlap
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Solution Overview
Problem
Existing ultrasonic obstacle positioning methods for autonomous vehicles lack accuracy in determining relative coordinates between obstacles and vehicles, leading to inadequate modeling of the obstacle environment, which hinders the vehicle's ability to make reasonable driving plans.
Innovation Solution
The method involves installing at least two detectors on a vehicle with overlapping detection areas, establishing a coordinate system, and calculating the coordinates of the detectors and obstacles within these areas to achieve precise positioning, allowing for optimal identification of obstacle distribution around the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only a single ultrasonic detector is used with improved performance characteristics and installation manner, then the detection effect and efficiency is improved, but the detection results are not accurate enough to obtain relative coordinates between obstacle and vehicle
Solution Approach 1:
The patent combines multiple ultrasonic detectors (at least two) to form an integrated detection system. By merging the detection capabilities of multiple detectors with overlapping detection areas, the system achieves both improved detection efficiency and accurate obstacle positioning through cooperative detection and coordinate calculation.
Solution Approach 2:
The patent transitions from single-detector one-dimensional detection to multi-detector two-dimensional spatial detection. By establishing overlapping detection areas and using coordinate geometry calculations based on multiple detector positions, the system obtains accurate relative coordinates of obstacles in the plane, adding spatial dimensionality to the detection capability.
2Measurement precision
If multiple detectors with overlapping detection areas are installed, then accurate obstacle positioning is achieved, but the device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing the coordinate system, pre-determining detector installation positions, and pre-calculating overlapping detection areas. This preparation work is done before actual obstacle detection, allowing the system to process detection data more efficiently and reduce real-time computational complexity.
Solution Approach 2:
The patent uses coordinate geometry calculations as a mathematical model (copy) to represent the physical detection process. By creating a virtual coordinate system and using geometric calculations to determine obstacle positions, the system simplifies the complex physical measurement problem into manageable mathematical computations.
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 enables accurate obstacle positioning, enabling autonomous vehicles to make informed driving decisions based on a detailed understanding of their surroundings by optimizing the placement and number of detectors to increase overlapping detection areas.
Implementation Method 1
ultrasonic detector is increasingly used in the field of automatic driving
Implementation Method 2
By applying a distance measurement function of the ultrasonic detector
Data Source
AI summary
An obstacle positioning method, device and terminal are provided. The method includes determining installation positions of at least two detectors on a vehicle, and respective detection areas of the detectors, determining an overlapping area of the detection areas of the detectors, and if determining that an obstacle is located in the overlapping area, determining a position of the obstacle according to the installation positions of the detectors forming the overlapping area. By changing the number and positions of detectors installed on an unmanned vehicle, a plurality of overlapping areas of the detection areas of the detectors are obtained, the distribution of obstacles around the vehicle are optimally identified, so that the unmanned vehicle makes reasonable driving plans based on an accurate surrounding obstacle environment.