Lidar Particulate Matter Detection for Autonomous Driving Paths
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately identifying and distinguishing particulate matter, such as mist, fog, and dust, from solid objects using existing sensing technologies, which can lead to unsafe driving maneuvers and reduced visibility, affecting the precision and safety of navigation.
Innovation Solution
The implementation of lidar technology to detect and classify particulate matter by analyzing the spatial distribution, velocity distribution, and intensity data of returned signals, utilizing Time-of-Flight (ToF) and coherent Doppler-assisted lidar data to differentiate between rigid objects and particulate matter, and determine wind velocity and direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sensing technologies are used to detect objects, then the sensing system can identify solid objects, but it cannot accurately distinguish particulate matter from solid objects
Solution Approach 1:
The patent applies parameter changes by utilizing multiple sensing parameters (velocity, spatial distribution, intensity) beyond what traditional sensors provide. The lidar system measures velocity values of return points and analyzes their spatial distribution patterns to differentiate particulate matter from solid objects, transforming a single-parameter detection problem into a multi-parameter analysis that resolves the identification ambiguity.
Solution Approach 2:
The patent introduces velocity information as an intermediary parameter that mediates between the sensor and the target object classification. By measuring the velocity of return points and analyzing their distribution, the system creates an intermediate layer of information that enables distinction between particulate matter (which exhibits characteristic velocity patterns) and solid objects (which have different velocity characteristics).
2Reliability
If particulate matter is not distinguished from solid objects, then the sensing system operates simply, but unsafe driving maneuvers occur
Solution Approach 1:
The patent applies universality by enabling the sensing system to perform multiple functions: traditional object detection plus particulate matter identification. The same lidar hardware and processing system that detects solid objects is extended to also identify particulate matter through velocity analysis, eliminating the need for separate specialized sensors while improving driving safety in foggy or dusty conditions.
Solution Approach 2:
The system performs preliminary classification of detected objects as either solid objects or particulate matter before making driving decisions. By analyzing velocity distribution patterns upfront, the system pre-identifies hazardous conditions (particulate matter indicating fog or dust) and adjusts driving behavior accordingly, preventing unsafe maneuvers before they occur.
3Measurement precision
If velocity data is analyzed to identify particulate matter, then classification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into regions based on velocity characteristics. Instead of analyzing all return points uniformly, the system segments the sensing data into particulate matter regions (identified by characteristic velocity distributions) and solid object regions, enabling focused analysis that improves accuracy while managing computational complexity through regional processing.
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 object segmentation and classification, enabling autonomous vehicles to safely navigate through environments with reduced visibility and improving driving path determination by distinguishing between particulate matter and solid objects, thereby reducing the risk of accidents and ensuring optimal driving decisions.
Implementation Method 1
the instant specification relates to improving autonomous driving systems and components using light detection and ranging data
Implementation Method 2
utilizing Time-of-Flight (ToF) and coherent Doppler-assisted lidar data
Implementation Method 3
coherent Doppler-assisted lidar data to differentiate between rigid objects and particulate matter
Implementation Method 4
An autonomous (fully and partially self-driving) vehicle (AV) operates by sensing an outside environment with various electromagnetic (e.g., radar and optical) and non-electromagnetic (e.g., audio and humidity) sensors
Data Source
AI summary
Aspects and implementations of the present disclosure address shortcomings of the existing technology by enabling lidar-assisted segmentation and identification of particulate matter in autonomous vehicle (AV) applications, by: obtaining, by a sensing system of the AV, a plurality of return points, each return point having one or more velocity values and one or more coordinates of a reflecting region that reflects a signal emitted by the sensing system, identifying, in view of the one or more velocity values of each of a first set of the return points of the plurality of return points, that the first set of the return points is associated with a particulate matter in an environment of the AV, and causing a driving path of the AV to be determined in view of the particulate matter.