Road Hazard Detection Using LiDAR-Image Point Cloud Mapping
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Solution Overview
Problem
Existing roadwork monitoring technologies rely on manual operators for hazardous object detection, leading to delays and reduced accuracy under adverse conditions, especially in low lighting or adverse weather, and struggle to identify small objects reliably.
Innovation Solution
A system combining a detection and ranging sensor, such as LiDAR, to generate a three-dimensional point cloud and an image capture device to provide visual data, with a processing module that identifies and maps hazardous objects, enhancing automation and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operators are used to monitor and detect hazardous objects, then the system can identify objects with contextual understanding, but the response time is delayed and productivity is reduced
Solution Approach 1:
The patent replaces the manual mechanical monitoring system with an automated sensor-based detection system. LiDAR sensors and cameras automatically capture data from the road environment, and processing modules automatically analyze this data to identify hazardous objects, eliminating the need for manual operator intervention while maintaining detection accuracy and significantly improving response time.
Solution Approach 2:
The system performs self-detection and self-analysis of hazardous objects through automated processing modules that continuously monitor sensor data. The system serves itself by automatically generating alerts and notifications without requiring external human intervention, thereby improving productivity while maintaining reliable detection capabilities.
2Loss of information
If cameras are used for detection, then visual data can be captured, but performance deteriorates in low lighting, adverse weather or over long ranges
Solution Approach 1:
The patent combines multiple sensor types - specifically LiDAR sensors and cameras - to create a complementary detection system. LiDAR sensors provide reliable distance and spatial information that is unaffected by lighting conditions, while cameras provide visual context. By merging the data from both sensor types, the system overcomes the limitations of cameras alone in low lighting, adverse weather, or long-range scenarios.
Solution Approach 2:
The system uses a composite sensing approach, integrating data from different sensor modalities (optical cameras and electromagnetic LiDAR) to create a more robust detection capability. This composite sensing strategy ensures reliable operation across varying environmental conditions by leveraging the strengths of each sensor type.
3Productivity
If conventional radar sensors are used, then detection can be performed, but accuracy is reduced in adverse weather and small objects cannot be reliably identified
Solution Approach 1:
The patent replaces conventional radar sensors with LiDAR technology, which uses laser light instead of radio waves. This substitution provides superior measurement precision for object identification, particularly for small objects, while maintaining productive detection capabilities. The LiDAR system's ability to resolve fine spatial details overcomes the limitations of conventional radar.
Solution Approach 2:
The system changes the detection parameter from radio wave frequency (conventional radar) to laser light frequency (LiDAR). This parameter change enables much higher spatial resolution and improved ability to detect and identify small objects, while the rapid pulse repetition rates of modern LiDAR systems maintain high detection productivity.
4Productivity
If automated detection systems are implemented, then response time is improved and productivity increases, but system complexity increases
Solution Approach 1:
The patent divides the automated detection system into distinct functional modules: data capture modules (sensors and cameras), data processing modules (for analyzing sensor data), and output modules (for generating alerts). This segmentation allows each module to be optimized independently and simplifies the overall system architecture, making the complex automated system more manageable while maintaining high verification speed.
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
Facilitates faster and more accurate detection of hazardous objects, reducing human error and response times by automating the verification process and providing contextual information.
Implementation Method 1
detection and ranging sensors (hereinafter 'ranging sensors') typically transmit a series of signals which sweep across a field of view, the signals being reflected from the scene and their time of flight measured so as to measure distances to the reflection point
Implementation Method 2
the signals being reflected from the scene and their time of flight measured
Implementation Method 3
distances to an object which are determined using passive visual data, e.g. triangulation using data from a camera
Data Source
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AI summary
A system (100) for detecting the presence of a hazardous object on a road. The system includes a detection and ranging sensor (110), an image capture device (120) and a processing module (130). The detector and ranging sensor is used to capture data from the road and generate a three-dimensional point cloud based on the captured data. The image capture device captures visual data from the road. The processing module generates an identification of at least one hazardous object within the three-dimensional point cloud, where the identification includes a subset of points within the three-dimensional point cloud, and maps the subset of points in the three-dimensional point cloud to corresponding parts of the visual data.