Obstacle Point Cloud Direction Output for Vehicle Type Distinction
Solution Overview
Problem
Existing obstacle detection methods for autonomous vehicles lack effectiveness in accurately determining the direction and type of obstacles, particularly in distinguishing between motor vehicles and non-motor vehicles, which hampers safe navigation and route planning.
Innovation Solution
A method and apparatus that calculate a target value for each candidate direction of a target obstacle point cloud based on its area, width-to-length ratio, and symmetry point count, and output the direction information corresponding to the minimum target value, enhancing the accuracy of obstacle information output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing obstacle detection methods are used to determine obstacle type and mass center, then the detection process is simple, but the accuracy of obstacle direction determination and type distinction is insufficient
Solution Approach 1:
The patent segments the obstacle detection process into multiple independent calculation components: smallest circumscribing rectangle calculation, area computation, width-to-length ratio calculation, and symmetry point count calculation. Each component processes a specific aspect of the point cloud data, and their results are combined to determine the final obstacle direction. This segmentation allows for improved accuracy through comprehensive analysis while maintaining manageable computational complexity by organizing the process into modular steps.
Solution Approach 2:
The patent introduces a new dimensional approach by calculating the symmetry point count of the smallest circumscribing rectangle, which adds a geometric dimension to the analysis. Instead of relying solely on traditional mass center determination, the method evaluates the distribution of symmetry points along the rectangle's axes, creating an additional dimension of analysis that improves obstacle type distinction, particularly between motor vehicles and non-motor vehicles.
2Reliability
If traditional obstacle detection methods are used, then the processing speed is fast, but the ability to distinguish between motor vehicles and non-motor vehicles is weak
Solution Approach 1:
The patent replaces traditional mechanical or simple geometric obstacle detection methods with a computational geometry-based system. Instead of using basic mass center calculation alone, the system employs algorithms that compute the smallest circumscribing rectangle, calculate area and width-to-length ratios, and count symmetry points. This substitution of computational methods enhances the reliability of obstacle type distinction while maintaining processing efficiency through algorithmic optimization.
Solution Approach 2:
The patent changes multiple parameters simultaneously to improve obstacle type distinction: it calculates the area of the smallest circumscribing rectangle, computes the width-to-length ratio, and determines the symmetry point count. By analyzing multiple parameters rather than a single metric, the system achieves more reliable classification of obstacle types. Motor vehicles typically exhibit different parameter combinations compared to non-motor vehicles, enabling more accurate distinction.
3Loss of information
If only mass center information is output for obstacles, then the information output is simple, but the richness of obstacle information is insufficient for effective navigation and route planning
Solution Approach 1:
The patent creates a multi-functional obstacle information output system that serves multiple purposes simultaneously. The calculated parameters (smallest circumscribing rectangle, area, width-to-length ratio, symmetry point count) are not only used for determining obstacle direction but also for distinguishing obstacle types and providing comprehensive information for navigation and route planning. This universal approach ensures that a single set of calculations supports multiple functions, reducing information loss without proportionally increasing processing complexity.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for outputting obstacle information. A specific embodiment of the method comprises: determining a candidate direction information set of a target obstacle point cloud; determining, for each piece of candidate direction information in the candidate direction information set, a target value of the target obstacle point cloud in a direction indicated by the candidate direction information based on the target obstacle point cloud and a smallest circumscribing rectangle of the target obstacle point cloud in the direction indicated by the candidate direction information; defining candidate direction information having a minimum target value in the candidate direction information set as direction information corresponding to the target obstacle cloud point; and outputting the direction information corresponding to the target obstacle cloud point, thereby improving the abundance of content of outputted obstacle information.