Distance Sensor Calibration for Autonomous Driving Accuracy
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Solution Overview
Problem
Autonomous driving systems face challenges in obtaining accurate shape information from distance sensors due to varying waveform characteristics and changing surroundings, leading to potential errors in object recognition.
Innovation Solution
A calibration method for distance sensors that involves emitting multiple beams, obtaining reflection signals, and adjusting parameters such as beam angles and reflectance to match reflection results with predicted outcomes based on object information, including location and shape data, to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple beams are emitted to obtain shape information about an object, then the completeness of object information is improved, but the accuracy of shape information deteriorates due to different waveform characteristics
Solution Approach 1:
The patent applies parameter changes by adjusting beam parameters (such as beam angles, wavelengths, or emission timing) to compensate for the different waveform characteristics of multiple beams. This allows the system to maintain measurement precision across all beams while still gathering comprehensive object information from multiple viewing angles.
Solution Approach 2:
The patent implements feedback mechanisms where the measured shape information from multiple beams is continuously compared and adjusted. The system uses feedback loops to refine the waveform characteristics of subsequent beams based on previous measurements, thereby improving overall accuracy while maintaining information completeness.
2Measurement precision
If calibration is performed to improve accuracy of distance sensor data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by performing calibration procedures in advance before actual distance measurements are taken. Reference objects with known geometric properties are scanned and stored as reference data, so that subsequent measurements can be quickly compared against these pre-established standards without requiring complex real-time calibration.
Solution Approach 2:
The patent uses copying by creating digital reference models of calibration objects that are stored in memory. These reference copies are then used for comparison during measurement operations, eliminating the need for physical recalibration and reducing device complexity while maintaining high measurement precision.
3Measurement precision
If parameters such as beam angles and reflectance are adjusted to match predicted outcomes, then accuracy of object recognition is improved, but loss of time increases due to iterative calibration
Solution Approach 1:
The patent performs parameter adjustments and calibration in advance, storing optimized beam parameters and reference measurements before actual operation. This preliminary setup eliminates the need for time-consuming iterative calibration during real-time object recognition, thereby reducing time loss while maintaining high accuracy.
Solution Approach 2:
The patent applies partial action by performing calibration only on critical parameters that have the greatest impact on measurement accuracy, rather than adjusting all possible parameters. This selective approach achieves sufficient accuracy while significantly reducing the time required for the calibration process.
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
The calibration method improves the accuracy of distance sensor data, reducing errors and ensuring precise object recognition in autonomous driving systems, even in dynamic environments.
Implementation Method 1
a lidar sensor 11 is mounted in a car 103. The lidar sensor 11 refers to a type of a distance sensor and is used to sense a shape of and distance to an object in the vicinity of the car 103 while driving
Implementation Method 2
obtains distance sensor data by emitting a plurality of beams from the distance sensor to the one surface of the object and receiving a plurality of reflection signals of the plurality of beams
Data Source
AI summary
A calibration method performed by a distance sensor emitting at least one beam is provided. The calibration method includes obtaining information regarding one surface of an object; obtaining distance sensor data by emitting a plurality of beams to the one surface of the object; and performing calibration on the plurality of beams emitted by the distance sensor, based on the information about the one surface of the object and the distance sensor data.