LIDAR Atmospheric Estimation in Dynamic Environments
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Solution Overview
Problem
Existing systems calibrated for static environments struggle to accurately determine signal attenuation and atmospheric properties in dynamic and unfamiliar environments, impacting sensor performance and data reliability in real-world conditions.
Innovation Solution
A LIDAR-based system that detects distance and light intensity to estimate atmospheric properties, such as visibility range and precipitation intensity, by determining the reflectivity of objects and extinction coefficient, and uses these properties to control actuators like windshield wipers, without relying on pre-calibration or known objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If systems are statically calibrated to determine signal attenuation, then measurement precision is improved in controlled environments, but adaptability deteriorates in dynamic and unfamiliar environments
Solution Approach 1:
The system transitions from static calibration to dynamic adaptation by continuously learning optical properties of objects in real-time. The extensible framework allows the system to adapt to changing environmental conditions by updating its understanding of object reflectivity and atmospheric properties as new objects are encountered, rather than relying on pre-established calibration data.
Solution Approach 2:
The system changes its operational parameters by adjusting its approach to signal attenuation determination based on environmental context. Instead of using fixed calibration parameters, the system dynamically modifies its measurement and interpretation methods according to the specific atmospheric conditions and object types encountered in each situation.
2Adaptability or versatility
If LIDAR sensors are used to detect atmospheric properties in dynamic environments, then adaptability is improved, but measurement precision deteriorates due to unfamiliar and changing conditions
Solution Approach 1:
The system implements feedback mechanisms where measurements from LIDAR sensors are continuously analyzed and used to refine estimates of atmospheric properties. The extensible framework allows the system to learn from accumulated data about object optical properties and atmospheric conditions, progressively improving measurement precision through iterative refinement based on observed patterns and relationships.
Solution Approach 2:
The system performs self-calibration and self-improvement by automatically learning optical properties of objects and atmospheric characteristics from its own measurements. Rather than requiring external calibration inputs, the system serves itself by building its own understanding of the environment through continuous observation and analysis of LIDAR return data.
3Device complexity
If static calibration methods are used, then device complexity is reduced, but reliability deteriorates in real-world dynamic conditions
Solution Approach 1:
The system performs preliminary learning and characterization of objects and atmospheric conditions before making critical measurements. By building a database of optical properties and atmospheric characteristics in advance through continuous observation, the system prepares itself to handle dynamic conditions more reliably when actual measurements are needed, rather than reacting to conditions in real-time without prior knowledge.
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
Enables accurate estimation and control of atmospheric properties in dynamic environments, improving sensor performance and reliability by learning optical properties of encountered objects over time, and adjusting actuators accordingly.
Implementation Method 1
A LIDAR sensor may be used to determine a reflectivity of an object and an extinction factor of the environment
Implementation Method 2
precipitation may absorb a portion of the transmitted signal so that a degraded signal is received
Implementation Method 3
light detection and ranging (LIDAR)
Data Source
AI summary
Systems and methods are provided for estimating atmospheric properties using a LIDAR sensor. A control system includes a LIDAR sensor configured to detect a distance to an object, and an intensity of light reflected by the object. A controller's a target selection module determines whether the object is a target for use in estimating the atmospheric properties. A data collection module collects values of the distance and the intensity as detected by the LIDAR sensor. The atmospheric properties are determined based on the values of the distance and the intensity. In response to the determined atmospheric properties, the actuator is operated to effect an action.