Vehicle Radar Sensitivity Control Using 3D Geographical Data
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Solution Overview
Problem
Radar devices struggle to accurately detect vehicles in varying surroundings such as tunnels and bridges due to increased noise, leading to difficulty in distinguishing between scanning targets and roadside structures, and placing a heavy load on the device, which results in missed obstruction detection at high speeds.
Innovation Solution
The system adjusts the radar sensitivity based on geographical data, changing and resetting it at specific points like tunnel entrances and exits, and uses 3-D geographical data to define detection areas, allowing for accurate detection by comparing distances to determine if an object is a vehicle or not.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the receiving sensitivity is set at a high level to detect roadside structures, then roadside structures can be detected, but noise increases in environments like tunnels and bridges
Solution Approach 1:
The patent applies dynamics by making the receiving sensitivity adjustable rather than fixed. The sensitivity is dynamically changed based on the vehicle's location data - set high when outside tunnels/bridges for detecting roadside structures, and set low when inside tunnels/bridges to reduce noise from wall reflections
Solution Approach 2:
The patent changes the receiving sensitivity parameter according to the detection environment. By referencing location data to determine whether the vehicle is in a tunnel or bridge section, the system adjusts the sensitivity parameter to appropriate levels, thereby adapting to varying detection conditions
2Object-affected harmful factors
If the receiving sensitivity is set at a low level to reduce noise, then noise is reduced, but roadside structures cannot be detected
Solution Approach 1:
The system dynamically adjusts sensitivity based on location context. When the vehicle enters a tunnel or bridge section identified from location data, sensitivity is lowered to reduce noise; when outside such sections, sensitivity is raised to enable roadside structure detection
Solution Approach 2:
The receiving sensitivity parameter is changed according to environmental conditions derived from location data. The system transitions between high sensitivity (for structure detection) and low sensitivity (for noise reduction) based on whether the vehicle is in a tunnel or bridge environment
3Measurement precision
If the device distinguishes between scanning targets and roadside structures, then detection accuracy improves, but processing load increases
Solution Approach 1:
The system performs preliminary action by pre-determining which areas contain roadside structures based on location data before radar detection. By calculating absolute locations of roadside structures in advance and comparing them with detected object locations, the system avoids complex real-time distinction processing
Solution Approach 2:
The patent extracts the distinction problem by separately handling structure detection. Instead of distinguishing all detected objects in real-time, the system extracts roadside structure detection as a separate function using location data comparison, reducing the processing burden on the main detection system
4Measurement precision
If the device processes all detected objects to distinguish targets from structures, then detection accuracy improves, but detection speed decreases
Solution Approach 1:
The system performs preliminary filtering by calculating absolute locations of roadside structures in advance using location data. This preliminary action allows the system to quickly determine whether detected objects are structures or targets without complex real-time processing, maintaining both accuracy and speed
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 detection of vehicles without distinguishing between targets and roadside structures, reduces unnecessary scanning, and improves detection in noisy environments by adjusting sensitivity dynamically and using 3-D data to define scanning areas.
Implementation Method 1
A radar device mounted in a vehicle, such as a millimeter-wave radar device, may be used to detect a preceding vehicle
Implementation Method 2
increased radio waves reflected from the wall are picked up and output on a radar screen as noise
Data Source
AI summary
A method and apparatus for detecting an object on a road around a vehicle. The method includes referring to three-dimensional geographical data, calculating a distance DA to a detecting object BLD in a 360 degree range, defining an area for the detecting target by the distance DA, measuring a distance DB to the object BLD and an object OBL in the 360 degree range when the vehicle travels, determining that the object is present on a road around the vehicle when DB<DA, and outputting the determination result.