Test Pattern Generator for LIDAR Distance Verification
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Solution Overview
Problem
LIDAR systems in the automotive environment face challenges in ensuring accurate distance determination due to potential malfunctions caused by incorrect timescales or time reference points, leading to errors in distance measurement, which can impact safety and autonomous driving functions.
Innovation Solution
A device and method for generating test data using a test pattern generator to produce chronological sequences of test events, which are processed by a test histogram channel to create time-correlated test histogram data, allowing for the verification of distance determination accuracy and detection of malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a LIDAR system performs distance determination using optical time-of-flight measurement, then distance measurement capability is enabled, but potential malfunctions due to incorrect timescales or time reference points can lead to measurement errors
Solution Approach 1:
The patent applies preliminary action by generating test data and performing safety analyses before actual LIDAR operation. A test pattern generator creates chronological sequences of test events that simulate various malfunction scenarios (incorrect timescales, wrong time reference points) to verify the distance determination algorithm's robustness beforehand, ensuring the system can handle potential errors before deployment
Solution Approach 2:
The patent implements feedback by comparing determined distances with nominal distances derived from test event timing information. This comparison provides feedback on whether the distance determination is accurate or if malfunctions (such as incorrect timescale configuration) are present, allowing for verification and correction of system parameters
2Reliability
If safety analyses are performed to test various malfunction effects, then system safety is improved, but testing complexity and time requirements increase
Solution Approach 1:
The patent applies copying by creating test data that replicates real-world measurement scenarios without requiring actual physical test objects. The test pattern generator produces chronological sequences of test events that copy the essential characteristics of real LIDAR measurements, allowing comprehensive safety analysis using simplified virtual representations rather than complex physical testing setups
Solution Approach 2:
The patent uses parameter changes by systematically varying test parameters (such as test event timing, nominal distance values, and expected distance outcomes) to cover multiple malfunction scenarios. This allows comprehensive safety testing through parameter variation rather than requiring separate physical test setups for each scenario
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 enables routine verification of peak detection and ensures error-free functionality by comparing determined distances with nominal distances, thereby enhancing the reliability of distance determination in LIDAR systems.
Implementation Method 1
various methods for optical runtime measurement are generally known, which can be based upon the so-called time-of-flight principle, in which the runtime of a transmitted light signal that is reflected by an object is measured, so as to determine the distance to the object based upon the runtime
Data Source
AI summary
A device for generating test data for testing a distance determination during an optical runtime measurement, comprising:A test pattern generator, which is set up to generate a chronological sequence of test events, so as to provide the latter to a test histogram channel for generating time-correlated test histogram data for testing the distance determination during the optical runtime measurement.