Road Surface Detection Using Grid-Based Ranging Data Processing
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Solution Overview
Problem
Existing methods for detecting road surfaces in autonomous vehicles often result in fitting errors, biases, and high standard deviations, which can hinder accurate localization and resource efficiency.
Innovation Solution
A method involving the reception of ranging data, extraction of data points within a specific height range, division into grid cells, and determination of most probable ground height and median values to accurately set cell positions, reducing errors and biases in road surface detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional road surface detection methods are used, then the detection process is simple, but fitting errors and biases increase, reducing measurement precision
Solution Approach 1:
The detection space is divided into multiple grid cells, with each cell independently processed to determine local road surface characteristics. This segmentation allows for more precise local measurements while maintaining computational efficiency through parallel processing of individual cells.
Solution Approach 2:
The method transitions from traditional 2D road surface mapping to a 3D grid-based representation by incorporating height information. Each grid cell contains height data that enables accurate determination of road surface topology, including slopes and elevation changes, thereby improving measurement precision.
2Reliability
If comprehensive ranging data is processed, then detection coverage is improved, but computational time and resources increase
Solution Approach 1:
By dividing the detection space into grid cells, the system can process data in smaller, manageable units. This enables parallel computation across multiple cells, reducing overall computational time while maintaining comprehensive coverage of the entire detection area.
Solution Approach 2:
The method focuses computational resources on processing only the necessary height information within each grid cell rather than analyzing all possible features. This selective approach maintains reliable detection coverage while minimizing unnecessary computational overhead.
3Measurement precision
If height range filtering is applied, then road surface identification accuracy is improved, but data loss increases
Solution Approach 1:
The height range filtering is applied locally within each grid cell rather than globally across all data. This allows the system to adapt the filtering criteria to local road surface characteristics, maintaining high identification accuracy while preserving relevant data that might fall outside standard height ranges in different locations.
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
This approach enhances the accuracy of road surface detection, reduces fitting errors and biases, and improves computational efficiency by effectively identifying the road surface and separating it from other environmental features.
Implementation Method 1
the ranging data points are generated by a light detection and ranging (LIDAR) device
Data Source
AI summary
A method for road surface detection includes receiving ranging data including a plurality of ranging data points, extracting one or more ranging data points lying within a height range from the plurality of ranging data points, dividing the one or more ranging data points into one or more grid cells, setting a first horizontal position of a first cell point of a first grid cell of the one or more grid cells as being centered on the first grid cell, setting a first vertical position of the first cell point, and detecting the road surface based on the first vertical position and first horizontal position of the first cell point.