Sensor Time Threshold Tuning for Point Cloud Obstacle Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual adjustment of parameters in lidar data collection for autonomous driving is time-consuming and labor-intensive, requiring efficient automation for improved adaptability and accuracy.
Innovation Solution
A method and apparatus for automatically determining a target time threshold by analyzing obstacle data from point cloud and image frames, identifying similarities, and processing obstacles to adjust time thresholds, thereby reducing manual intervention and enhancing data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment of parameters is used in lidar data collection, then adaptability to different scenarios can be achieved, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs self-adjustment of the time threshold parameter by automatically analyzing point cloud data and image data to determine optimal values. The processor autonomously identifies obstacles in both data types, calculates time intervals, and adjusts the time threshold without requiring manual intervention, thereby resolving the contradiction between adaptability and time consumption
Solution Approach 2:
The patent dynamically changes the time threshold parameter based on actual driving conditions and sensor data characteristics. By automatically adjusting this critical parameter according to environmental factors and data quality, the system maintains optimal performance across different scenarios without manual reconfiguration
2Measurement precision
If multiple parameters are manually adjusted for different scenarios, then detection accuracy can be improved, but the workload increases significantly
Solution Approach 1:
The system automatically determines the time threshold parameter by processing point cloud and image data through the processor. This self-service mechanism eliminates the need for operators to manually adjust multiple parameters while maintaining high detection accuracy, as the system autonomously optimizes parameters based on real-time data characteristics
Solution Approach 2:
The patent replaces manual mechanical parameter adjustment with an automated computational system. The processor uses algorithms to analyze sensor data and automatically set optimal parameters, substituting human operational effort with automated information processing and decision-making
3Productivity
If automated processing of point cloud and image data is implemented, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent merges the processing of point cloud data and image data into a unified automated workflow. The processor integrates both data types and processes them simultaneously to determine the time threshold, improving efficiency by eliminating separate manual processing steps while managing complexity through integrated architecture
Solution Approach 2:
The processor performs multiple functions including obstacle identification in point cloud data, obstacle identification in image data, time interval calculation, and parameter adjustment. This multi-functional approach improves productivity by consolidating various processing tasks into a single automated system rather than requiring separate specialized systems
Data Source
AI summary
Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method may include: acquiring point cloud data and image data collected by a vehicle during a driving process; determining a plurality of time thresholds based on a preset time threshold value range; executing following processing for each time threshold: identifying obstacles included in each point cloud frame and each image frame respectively; determining a similarity between the obstacles; determining, in response to the similarity being greater than a preset similarity threshold, whether a time interval between the point cloud frame and the image frame corresponding to two similar obstacles is less than the time threshold; and processing recognized obstacles based on a determining result, to determine the number of obstacles; and determining, based on a plurality of numbers, and outputting a target time threshold.