Lane Stripe Detection Using 3D LIDAR Point Cloud Segmentation
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Solution Overview
Problem
Existing lane stripe detection systems, particularly those vision-based and high-resolution LIDAR systems, face challenges in accuracy and cost due to lighting conditions and high data volume, respectively, necessitating a more efficient and cost-effective method for lane stripe detection in autonomous driving.
Innovation Solution
A lane stripe detecting method utilizing a low-resolution three-dimensional LIDAR sensor that transforms scan point coordinates into vehicle scan points, employing a horizontal layer judging step with point cloud intensity analysis and a vertical layer judging step with continuity analysis to identify lane stripes, independent of external lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a vision-based system (camera) is used to detect lane stripes, then the system can identify lane markings, but the detection accuracy deteriorates under varying lighting conditions and poor image quality
Solution Approach 1:
The patent replaces the vision-based optical system (camera) with a LIDAR-based active sensing system that uses laser beams to measure road surface geometry. This substitution eliminates dependence on ambient lighting conditions, as LIDAR actively emits its own light source and measures time-of-flight or phase shift, providing reliable detection regardless of external illumination.
Solution Approach 2:
The patent changes the detection parameter from optical intensity (camera-based) to geometric elevation (LIDAR-based). By measuring the vertical distance between the road surface and detected points, the system identifies lane stripes through geometric features rather than optical properties, making detection invariant to lighting conditions.
2Measurement precision
If a high resolution LIDAR sensor (at least 32 layers) is used to detect lane stripes, then the detection accuracy is improved, but the cost and computational burden increase significantly
Solution Approach 1:
The patent segments the point cloud data into multiple horizontal layers at different elevation thresholds. By dividing the detection space into discrete height layers and processing points within each layer separately, the system achieves accurate lane stripe detection using fewer LIDAR layers, reducing both sensor complexity and computational requirements.
Solution Approach 2:
The patent applies partial action by selectively processing only those scan lines and point cloud layers that contain relevant lane stripe information. Rather than processing all high-resolution data, the system identifies and focuses computational resources on regions where lane stripes are detected, reducing overall computational burden while maintaining accuracy.
3Measurement precision
If a high resolution LIDAR sensor is used to obtain detailed point cloud data, then the detection accuracy is improved, but the data volume and processing time increase
Solution Approach 1:
The patent extracts and removes redundant information from the point cloud data by filtering out points that do not contribute to lane stripe detection. By eliminating unnecessary data points and processing only relevant horizontal layers, the system reduces data volume and accelerates processing while preserving detection accuracy.
Solution Approach 2:
The patent performs preliminary filtering and organization of point cloud data before detailed lane stripe analysis. By pre-processing the data to identify candidate regions and organize points into horizontal layers in advance, the system reduces the computational complexity of subsequent detection steps, improving overall processing efficiency.
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 method improves the accuracy and correctness of lane stripe detection, reduces computational time, and lowers costs by using a low-resolution LIDAR sensor, enhancing the safety and flexibility of autonomous driving systems while maintaining robustness against ambient light variations.
Implementation Method 1
a three-dimensional LIDAR sensor disposed on the vehicle is used to obtain a plurality of three-dimensional LIDAR scan point coordinates of the road surface
Data Source
AI summary
A lane stripe detecting method based on a three-dimensional LIDAR is for detecting a lane stripe of a road surface around a vehicle. A data acquisition transforming step is for obtaining a plurality of vehicle scan point coordinates. The vehicle scan point coordinates are divided into a plurality of scan lines, and each of the scan lines has a plurality of scan points. A horizontal layer lane stripe judging step is judging whether each of the scan points is a horizontal stripe point or a non-horizontal stripe point according to a threshold value of each of the scan lines. At least two of the threshold values of the scan lines are different from each other. A vertical layer lane stripe judging step is for judging whether each of the horizontal stripe points is a same lane stripe point or a different lane stripe point.