Road Object Information Acquisition for Autonomous Driving Perception
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Solution Overview
Problem
Current autonomous driving technologies face challenges in accurately perceiving the driving environment due to inconsistencies in the splicing accuracy of laser point clouds, reflectance maps, and high-precision maps, which affect the reliability of vehicle navigation and object recognition.
Innovation Solution
A method and apparatus that collect characteristic information of road objects, including positions in a world coordinate system from laser point clouds and reflectance maps, and assess the preset accuracy by matching this information with pre-defined data from spliced laser point clouds, reflectance maps, and high-precision maps to determine the splicing accuracy, reflectance map accuracy, and high-precision map accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data sources (laser point cloud, reflectance map, high-precision map) are integrated for environment perception, then the comprehensiveness of perception is improved, but the difficulty of ensuring consistent accuracy across all data sources increases
Solution Approach 1:
The patent introduces a mediator system that includes a splicing module and an assessment module. The splicing module acts as an intermediary to align and integrate laser point cloud data with reflectance map and high-precision map data using control points and coordinate transformations. The assessment module then evaluates the splicing accuracy by comparing characteristic information across all data sources, ensuring consistent accuracy without requiring manual intervention in each data source.
Solution Approach 2:
The patent implements a feedback mechanism where the assessment module continuously evaluates the splicing accuracy by comparing characteristic information (laser point positions, control point positions, annotation data) from multiple data sources. The accuracy assessment results provide feedback on the quality of data integration, enabling automatic adjustment and optimization of the splicing process to maintain consistent accuracy across laser point cloud, reflectance map, and high-precision map.
2Reliability
If the accuracy assessment covers multiple data sources and parameters, then the reliability of perception is improved, but the complexity of the assessment process increases
Solution Approach 1:
The patent segments the accuracy assessment process into distinct, manageable components: (1) collecting characteristic information from each data source separately (laser point positions, control point positions, annotation data), (2) splicing the laser point cloud data with coordinate transformations, (3) comparing corresponding characteristic information from different data sources, and (4) generating accuracy assessment results for each data source independently. This segmentation reduces the complexity of the overall assessment process while maintaining comprehensive reliability evaluation.
3Measurement precision
If the splicing accuracy of laser point cloud is improved through multiple frame splicing, then the position precision is improved, but the time consumption for splicing and assessment increases
Solution Approach 1:
The patent applies preliminary action by pre-collecting and pre-splicing multiple frames of laser point cloud data before the actual accuracy assessment is needed. The splicing module performs coordinate transformations and aligns multiple frames in advance, creating a ready-to-assess spliced laser point cloud. This preliminary processing reduces the time consumption during the actual assessment phase, as the data integration work has already been completed beforehand.
Data Source
AI summary
The present disclosure discloses a method and apparatus for acquiring information. A specific embodiment of the method includes: collecting characteristic information of a road object, the characteristic information comprising: a position, in a world coordinate system, of laser points corresponding to the road object in a laser point cloud collected in an area where the road object is located, and a position in a reflectance map corresponding to a position of a control point of the road object in the world coordinate system; and determining whether a preset accuracy is acceptable based on a match between the characteristic information of the road object and a preset characteristic information of the road object, the preset accuracy comprising: a splicing accuracy of the spliced laser point cloud, an accuracy of the reflectance map, and an accuracy of the high-precision map.