LiDAR Blind-Zone Obstruction Detection Through Echo Analysis
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Solution Overview
Problem
LiDAR systems face challenges in accurately detecting obstructions in their blind zones, which affect detection accuracy and range due to non-overlapping fields of view of the light emitter and receiver, and obstructions like dirt or weather conditions impair detection light paths.
Innovation Solution
An obstruction detection method for LiDARs that determines an obstruction time window within the blind zone and analyzes echo characteristics to identify obstructions, using parameters like pulse width, peak value, and echo count to differentiate between obstructions and weather conditions, and divides the field of view into regions for precise detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a paraxial transreceiver optics system is used, then the LiDAR can achieve compact design and simple structure, but a blind zone exists in the close range where the field of view of the light emitter and light receiver do not overlap
Solution Approach 1:
The patent performs preliminary obstruction detection in the blind zone by analyzing echo characteristics before they affect long-distance detection. The system proactively identifies obstructions (dirt, weather conditions, or objects) in the blind zone by examining echo parameters such as pulse width, peak value, and echo count, allowing for preventive maintenance or compensation before these obstructions impact the LiDAR's primary detection function.
2Adaptability or versatility
If the LiDAR emits detection light and receives echo in the blind zone, then the system can potentially detect close-range objects, but the echo is weak and difficult to identify
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring echo characteristics from the blind zone and using this information to identify obstructions. The system analyzes feedback signals (echo pulse width, peak value, echo count) to determine whether an obstruction exists, and this feedback information is then used to adjust detection parameters or trigger warnings, improving the overall measurement precision despite the weak echoes in the blind zone.
3Reliability
If obstructions exist in the blind zone or on the cover surface, then the light emitting path is blocked, but the LiDAR cannot accurately detect the obstruction using conventional methods
Solution Approach 1:
The patent uses echo characteristics as an intermediary indicator to indirectly detect obstructions in the blind zone. Instead of directly observing the obstruction (which would require the obstruction to be within the overlapping field of view), the system uses the modified echo signals passing through or reflecting from the obstruction as an intermediary signal to infer the presence and location of obstructions, enabling accurate obstruction detection without direct line-of-sight observation.
4Adaptability or versatility
If the LiDAR operates in various weather conditions or with dirty covers, then the detection range and accuracy are reduced, but the system lacks the ability to distinguish between different types of obstructions
Solution Approach 1:
The patent applies local quality analysis by examining different characteristics of echoes from different regions and sources. The system analyzes local variations in echo parameters (pulse width, peak value, echo count) to distinguish between different types of obstructions such as weather conditions (rain, snow, fog) versus physical obstructions (dirt on cover, objects in blind zone). This localized analysis of echo quality enables the system to adapt to various environmental conditions while maintaining precise obstruction type identification.
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
Enhances obstruction detection accuracy and adaptability to various environments by distinguishing between different types of obstructions and weather conditions, improving detection results without requiring hardware changes.
Implementation Method 1
The LiDAR can emit detection light and receive an echo reflected by an object to detect information
Implementation Method 2
A LiDAR can include components such as a light emitter, a light receiver, and a cover (e.g., a window). The LiDAR can be a non-contact measuring device.
Data Source
AI summary
An obstruction detection method includes: determining an obstruction time window, such that a distance corresponding to the obstruction time window is within a blind zone of the LiDAR (602); and determining whether an obstruction exists based on a characteristic parameter of an echo generated by a detection light within the obstruction time window (603,604).