SPAD LIDAR Concurrence Detection for Cross-Talk Reduction
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Solution Overview
Problem
Existing LIDAR sensors using SPAD technology face challenges with erroneous detections due to background light and cross-talk from highly reflective objects, which can lead to incomplete or inaccurate surroundings detection.
Innovation Solution
A method involving a SPAD-based LIDAR sensor that emits a predefined transmission pulse pattern of consecutive light pulses, detects photons, generates run-time histograms, and employs 'concurrence detection' to filter out undesirable detections, using a histogram evaluation window to select the best individual histograms for generating a reliable 3D point cloud, thereby reducing cross-talk and enhancing detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If concurrence detection with majority decision is used to avoid erroneous detections, then reliability of detection is improved, but device complexity increases due to multiple macropixels and histogram processing
Solution Approach 1:
The receiving surface is divided into multiple macropixels, each consisting of several individual pixels. This segmentation enables concurrence detection by comparing detections across multiple macropixels to filter out erroneous detections caused by background light or cross-talk, thereby improving reliability while distributing the complexity across modular units.
Solution Approach 2:
The system uses periodic emission of light pulses in defined pulse patterns and generates histograms based on these periodic measurements. By evaluating multiple histograms and selecting the best match, the system achieves reliable detection while managing complexity through structured, repeating measurement cycles.
2Measurement precision
If multiple histograms are evaluated and combined to improve detection accuracy, then measurement precision is improved, but loss of time increases due to processing multiple histograms
Solution Approach 1:
The system pre-generates multiple histograms corresponding to different pulse patterns before final evaluation. By having these histograms prepared in advance and using selection criteria to choose the best match, the system achieves high measurement precision without excessive processing delays during critical detection phases.
Solution Approach 2:
Instead of processing all possible histograms equally, the system evaluates a selected subset based on predefined criteria such as signal quality or correlation with expected patterns. This partial evaluation approach maintains high detection accuracy while reducing the overall processing time by focusing computational resources on the most relevant histograms.
3Reliability
If transmission pulse pattern with variations is used to reduce cross-talk, then reliability is improved, but use of energy increases due to multiple pulses
Solution Approach 1:
The system employs periodic transmission of light pulses in defined patterns with intentional variations between pulses. These periodic variations help distinguish genuine reflections from cross-talk signals while managing energy consumption through structured pulse sequences rather than continuous high-power emission.
Solution Approach 2:
The transmission pulse pattern varies parameters such as pulse width, intensity, or timing between consecutive pulses. By changing these parameters periodically, the system can identify and filter out cross-talk signals that do not match the expected pattern, improving reliability without requiring maximum energy at all times.
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 method ensures reliable and flexible surroundings detection, reducing cross-talk and improving signal-to-noise ratio, leading to more accurate object recognition and enhanced traffic safety for autonomous vehicles.
Implementation Method 1
the LIDAR sensor is preferably designed as a so-called time-of-flight (TOF) sensor, which is configured, based on a determination of light propagation times, to ascertain distances of objects
Implementation Method 2
Some LIDAR sensors in the related art include receiving units which are designed based on the so-called single-photon avalanche diode (SPAD) technology and, due to the use of this technology, have a particularly high sensitivity
Data Source
AI summary
A method and to a device for activating a SPAD-based LIDAR sensor and a surroundings detection system. The method includes: emitting a predefined transmission pulse pattern into surroundings of the LIDAR sensor, the transmission pulse pattern being made up of a plurality of consecutive light pulses; detecting photons arriving in the LIDAR sensor within a predefined detection time period after the emission of a respective light pulse; generating histograms which represent a frequency of detected photons with respect to respective reception points in time, each histogram referring to a respective detection time period, and to a respective macropixel; ascertaining a histogram evaluation window, based on which those histograms corresponding to the transmission pulse pattern are selected from a chronological sequence of histograms which, during an allocation to a total histogram, meet predefined criteria for the total histogram; and providing the total histogram for generating a 3D point cloud.