Lidar Sensor Testing With Retroreflectors for Debris Detection
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Solution Overview
Problem
Sensor data quality degrades due to environmental factors and internal errors, impacting vehicle navigation and obstacle detection in autonomous vehicles.
Innovation Solution
A sensor testing component uses retroreflectors to determine sensor degradation by comparing sensor data to baseline data, applying heuristics and machine learning models to identify obstructions and initiate cleaning or maintenance actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor testing is performed continuously to maintain high reliability, then sensor accuracy is improved, but device complexity and processing time increase
Solution Approach 1:
The system performs preliminary sensor testing by identifying retroreflectors in the environment and using them as pre-established test targets. This allows the sensor to be tested in advance using existing environmental features rather than requiring complex dedicated test equipment, thereby maintaining sensor accuracy while reducing system complexity.
Solution Approach 2:
Retroreflectors serve as intermediary objects between the sensor and the testing system. These passive environmental features mediate the testing process by reflecting sensor signals back to the sensor, enabling accuracy verification without requiring active test equipment or complex infrastructure.
2Measurement precision
If sensor testing uses dedicated test equipment to ensure measurement precision, then testing accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The sensor tests itself by detecting retroreflectors in its own field of view. The sensor uses its normal operational capabilities to detect the retroreflector and measure its properties, eliminating the need for external dedicated test equipment while maintaining measurement precision through self-verification.
Solution Approach 2:
The retroreflectors serve multiple functions: they act as test targets for sensor accuracy verification, provide localization references, and can be used for mapping. This multi-functionality allows precise sensor testing without requiring specialized equipment, as the same environmental features serve multiple purposes in the autonomous vehicle system.
3Reliability
If sensor testing is performed frequently to maintain safety, then reliability is improved, but loss of time and processing overhead increase
Solution Approach 1:
The system performs sensor testing periodically by checking for retroreflectors at regular intervals during normal operation. This periodic testing approach maintains vehicle safety and sensor reliability without requiring continuous testing, thereby minimizing processing time overhead while ensuring periodic verification of sensor accuracy.
Solution Approach 2:
The system performs preliminary checks for retroreflectors using existing environmental data and sensor feeds before initiating full testing sequences. This preliminary action allows the system to quickly determine whether testing is necessary and to prepare test parameters in advance, reducing the time required for actual sensor verification while maintaining safety standards.
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
Improves sensor accuracy and safety in autonomous vehicles by mitigating sensor obstructions, allowing for more reliable navigation and obstacle detection.
Implementation Method 1
determine a degraded state of a sensor based on reflectivity data associated with a retroreflector
Data Source
AI summary
Techniques for detecting degradation of a component are discussed herein. For example, a computing device can implement a sensor testing component to determine degradation caused by rain, mud, dirt, dust, snow, ice, animal droppings, or other debris on and/or near an outer surface of the lidar sensor. The sensor testing component can apply one or more heuristics and/or machine learned models to the lidar data and/or compare information associated with the lidar data (e.g., intensity, pulse information, etc.) to a baseline to determine an action for a vehicle and/or a size, a type, or a location of an obstruction blocking a lidar beam.