Vehicle Lidar Fog Detection Using Radar Correlation
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Solution Overview
Problem
Lidar systems face challenges in accurately detecting solid objects due to interference from fine particulate matter such as fog, smoke, and dust, which can reflect light and produce unreliable data, leading to incorrect surface detection and reduced effective range.
Innovation Solution
The use of a combination of radar and lidar sensors to determine the presence and density of non-impeding objects by correlating radar and lidar observations, with a machine learning approach to calculate a similarity score that distinguishes between solid surfaces and particulate matter, allowing for improved object detection and navigation in environments with high particulate density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidar is used to detect objects, then distance measurement capability is improved, but reliability deteriorates in environments with fine particulate matter such as fog, smoke, and dust
Solution Approach 1:
The patent combines radar and lidar sensors into an integrated sensor array system. The radar component transmits radio waves that can penetrate particulate matter, while the lidar component provides precise distance measurements. By merging the data from both sensors and correlating their observations, the system achieves both the precise distance measurement capability of lidar and the reliability of radar in environments with fog, smoke, and dust.
2Measurement precision
If lidar detects particulate matter reflections, then detection sensitivity is improved, but false positive rate increases due to incorrect surface detection
Solution Approach 1:
The patent uses radar as an intermediary to verify lidar detections. The radar sensor acts as a mediator that can distinguish between actual solid surfaces and particulate matter reflections. By correlating radar and lidar observations, the system confirms whether a detected reflection corresponds to a real object, thereby reducing false positives while maintaining high detection sensitivity.
Solution Approach 2:
The system implements feedback by continuously correlating radar and lidar data to validate detections. When lidar detects a potential object, the radar data provides feedback to confirm or reject the detection. This feedback mechanism allows the system to maintain high sensitivity while filtering out false positives caused by particulate matter reflections.
3Length of stationary object
If lidar effective range is extended, then detection coverage is improved, but accuracy deteriorates due to increased interference from particulate matter
Solution Approach 1:
The patent merges radar and lidar systems to extend effective range while maintaining accuracy. Radar waves can penetrate particulate matter and detect objects at longer ranges, while lidar provides accurate measurements when within its effective range. The combined system achieves extended detection coverage without sacrificing accuracy, as radar compensates for lidar's reduced accuracy at longer distances.
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 enhances the reliability of object detection by differentiating between solid objects and particulate matter, enabling autonomous vehicles to navigate safely through environments with high particulate density by weighting radar and lidar data accordingly, thereby reducing false positives and maintaining accurate distance measurements.
Implementation Method 1
Radar generally measures the distance from a radar device to the surface of an object by transmitting a radio wave and receiving a reflection of the radio wave from the surface of the object
Implementation Method 2
A lidar system has a light emitter and a light sensor. The light emitter may comprise a laser that directs light into an environment. When the emitted light is incident on a surface, a portion of the light is reflected and received by the light sensor
Implementation Method 3
A distance is then calculated based on the flight time and the known speed of light
Implementation Method 4
fine particulate matter may also reflect light. Problematically, fog, smoke, fog, exhaust, steam, and other such vapors may reflect light emitted by a lidar system
Data Source
AI summary
Techniques for detecting and determining a density for a non-impeding object based on correlation between radar and lidar returns are described herein. The techniques provide for receiving lidar data and radar data from a vehicle system operating in an environment. Portions of the lidar data and radar data are determined based on being associated with moving objects in the environment. The portions are then correlated to determine a similarity between the radar and lidar data. The similarity may then be used to determine an indication and density of the non-impeding object in the environment and cause the vehicle to operate within the environment accordingly.