LIDAR Contour Shape Recognition for Cut-In Vehicle Detection
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Solution Overview
Problem
Current LIDAR-based object recognition systems face challenges in accurately identifying the shape of objects in autonomous vehicles due to the complexity of data processing and the need for precise interpretation of contour points, especially in dynamic scenarios where objects may be tilted or cut in from the side.
Innovation Solution
An object recognition apparatus and method that utilizes a LIDAR sensor to obtain contour points and a processor to identify shapes by assigning flags to specific layers based on the distribution of contour points, considering reference and non-reference line segments, angle ranges, and size criteria, allowing for the identification of various shapes such as L-shaped, I-shaped, and sL-shaped distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR systems with high angular resolution are used to precisely detect surrounding environments, then measurement precision is improved, but the amount of data acquired increases, making data processing more complex
Solution Approach 1:
The patent segments the acquired point cloud data into multiple layers based on height information, and further segments each layer into regions based on contour point distributions. This segmentation approach divides the large volume of high-resolution LIDAR data into manageable portions that can be processed independently, reducing overall data processing complexity while maintaining detection precision
Solution Approach 2:
The patent extracts only the essential features from the high-resolution LIDAR data, specifically focusing on contour points and their distributions in different layers. By extracting only the relevant geometric features rather than processing all raw point cloud data, the system maintains measurement precision while significantly reducing data processing complexity
2Reliability
If the system processes all contour points to identify object shapes, then object identification accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The patent divides the point cloud data into multiple height-based layers and further segments each layer into regions based on contour point patterns. This segmentation allows the system to process smaller subsets of data in parallel, reducing processing time while maintaining accurate object identification by examining contour point distributions in each segment
Solution Approach 2:
The patent applies partial action by focusing processing efforts only on contour points and their distributions rather than analyzing every point in the point cloud. By applying excessive action in terms of creating multiple layers and regions, the system ensures thorough analysis of relevant features while keeping individual processing tasks manageable and time-efficient
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 the accuracy of object recognition by effectively distinguishing between different shapes of objects, improving the ability to detect preceding vehicles that are tilted or cutting in, thereby reducing the risk of accidents and enhancing the reliability of autonomous driving systems.
Implementation Method 1
A distance from a LIDAR sensor to an object, for instance, may be obtained through an interval between the time when laser is transmitted by the LIDAR sensor and the time when the laser reflected by the object is received
Data Source
AI summary
An object recognition apparatus includes a LIDAR sensor and a processor. The processor may identify whether an object is a dynamic object and identify a shape of the distribution of the contour points among a first shape, a second shape and a third shape, and assign a flag corresponding to an identified shape to the specific layer. The reference line segment may include a longer line segment among a line segment connecting a peak point and a first end point which is one of two outermost endpoints among the contour points and a line segment connecting the peak point and a second end point different from the first end point, the second end point being one of the two outermost endpoints.