3D Lane Marking Detection Using Point Cloud Intensity
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Solution Overview
Problem
Existing vehicle lane marking detection systems face challenges in accurately detecting lane markings under varying lighting conditions and achieving reliable range accuracy, especially with limitations in camera-based systems.
Innovation Solution
A vehicle lane marking detection system utilizing a 3D sensor to scan objects and ground areas, converting the data into a digital rendering of lane markings through object or shape recognition techniques, and providing this information to driver assist components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera-based lane marking detection is used, then the system can detect lane markings, but the detection accuracy deteriorates under varying lighting conditions
Solution Approach 1:
The patent replaces the camera-based optical detection system with a LIDAR-based active illumination system. The LIDAR sensor emits laser pulses and measures the reflected light to create depth maps, substituting passive camera imaging with active 3D scanning. This eliminates dependency on ambient lighting conditions because the system provides its own illumination source.
Solution Approach 2:
The patent changes the detection parameter from 2D image intensity (camera) to 3D depth information (LIDAR). By measuring the time-of-flight of laser pulses to calculate distance, the system transforms the detection approach from relying on light reflection intensity to relying on precise time measurement, which is independent of ambient lighting conditions.
2Measurement precision
If camera-based lane marking detection is used, then the system can identify lane markings, but the range accuracy deteriorates
Solution Approach 1:
The patent replaces passive camera imaging with active LIDAR ranging. The LIDAR system directly measures distance by timing the reflection of laser pulses, providing precise depth information. This substitution transforms the system from losing depth information in 2D images to directly measuring and preserving accurate 3D range data.
Solution Approach 2:
The patent transitions from 2D image detection to 3D spatial mapping. By adding the depth dimension through time-of-flight measurement, the system creates a three-dimensional point cloud representation of the environment, enabling accurate range measurement and preserving depth information that is inherently lost in conventional 2D camera imaging.
3Measurement precision
If 3D sensor data is processed using traditional methods, then the system can identify objects, but lane marking detection accuracy deteriorates due to surface feature requirements
Solution Approach 1:
The patent inverts the traditional approach by not requiring surface features to be detectable, but rather using the absence of depth variation to identify lane markings. Instead of looking for repeating patterns on surfaces, the system detects smooth, continuous depth changes characteristic of painted road markings, enabling detection of features that lack complex surface structures.
Solution Approach 2:
The patent changes the detection criterion from surface texture and pattern recognition to depth continuity and smoothness analysis. By processing LIDAR point cloud data to identify regions with consistent depth gradients and smooth surfaces, the system can detect lane markings based on their geometric properties rather than surface features, improving accuracy for markings without complex textures.
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
The system effectively detects lane markings regardless of lighting conditions and provides accurate range information, enhancing driver safety and vehicle control by integrating the detected lane markings with driver assist components.
Implementation Method 1
a 3D sensor, installed to the vehicle and configured to scan physical objects forward of and along lateral sides of the vehicle outputting point cloud that includes a plurality of data points. Each data point of the point cloud corresponds to a surface point of a physical feature, each data point being defined by distance, direction, intensity and vertical height for each data point reflected off an object surface
Data Source
AI summary
A vehicle lane marking detection system includes a 3D sensor, a driver assist component and an electronic controller. The 3D sensor is installed to a vehicle and is configured to scan physical objects around the vehicle outputting a plurality of data points each corresponding to a surface point of a physical feature. Each data point being defined by distance, direction, intensity and vertical location relative to the vehicle. The electronic controller is connected to the 3D sensor and the driver assist component. The electronic controller evaluates a point cloud defined by the data points identifying lane markings based on the intensity of the data points. The data points having intensities greater than a predetermined level are determined to correspond to lane marking and are provided to the driver assist component with the lane markings for use thereby.