Method for analyzing backscattering histogram data in an optical pulse run time method and device for data processing

Abstract

The invention relates to a method (20, 30, 40, 50) for analyzing backscatter histogram data in an optical pulse run time method, comprising: receiving (21, 31, 41, 51) backscatter histogram data; and analyzing (22, 32, 42, 52) the received backscatter histogram data.

Description

Technical Field

[0001] The present invention generally relates to a method of analyzing backscatter histogram data in an optical pulse runtime method and an apparatus for data processing. Background Art

[0002] Various optical pulse transit time methods (eg optical transit time measurement) are generally known, which may be based on the so-called time-of-flight principle, which measures the transit time of an emitted light signal reflected by an object in order to determine the distance to the object based on the transit time.

[0003] It is known to use sensors based on the so-called LIDAR (Light Detection and Ranging) principle in the context of motor vehicles, which scan the environment by periodically emitting pulses and detecting the reflected pulses. For example, a corresponding method and device are known from WO 2017 / 081294.

[0004] In LIDAR applications in automotive environments, under certain environmental conditions, such as daytime driving, the amount of ambient light is high, which reduces the signal-to-noise ratio (SNR). In this case, the detection range of the LIDAR application is also limited.

[0005] The type of light signal detected in LIDAR applications often differs, for example, depending on whether the emitted light signal is reflected by a solid object (object backscatter) or backscattered by particles in the air (e.g., in fog or exhaust fumes) (diffuse backscatter). Conclusions about the environmental conditions can be drawn from the recorded backscatter data. While solutions for analyzing backscatter data in optical pulse runtime methods are known from the prior art, the object of the present invention is to provide a method for analyzing backscatter histogram data in optical pulse runtime methods and a device for data processing. Summary of the Invention

[0006] This object is achieved by a method as defined in the claims.

[0007] In a first aspect, the present invention provides a method for analyzing backscattering histogram data in an optical pulse runtime method, comprising:

[0008] receiving backscatter histogram data; and analyzing the received backscatter histogram data.

[0009] In a second aspect, the present invention provides an apparatus for data processing, comprising means for implementing the method according to the first aspect.

[0010] As mentioned, several exemplary embodiments relate to a method for analyzing backscattering histogram data in an optical pulse runtime method, comprising:

[0011] receiving backscatter histogram data; and analyzing the received backscatter histogram data.

[0012] As mentioned at the outset, in LIDAR measurements, conclusions can be drawn from the backscatter data regarding environmental conditions (e.g., fog or other particles in the air, smoke, spray, etc.). Here, the detection event of backscattered light is independent of solid objects. More precise knowledge of environmental conditions allows for adaptive driving style based on these conditions, thereby improving safety, for example, in autonomous motor vehicles. Furthermore, in several exemplary embodiments, precise knowledge of diffuse backscatter during LIDAR measurements also allows for (more precise) detection of solid objects. This, for example, allows for a more precise determination of traffic conditions, which also improves the safety and reliability of autonomous vehicles.

[0013] A more precise understanding of the backscatter signal and the amount of ambient light also helps determine the effective detection range of LIDAR measurements. This allows for a better assessment of the reliability of LIDAR measurements during the detection of solid objects. This increases the probability of correctly identifying objects based on the measurement data, thus improving the safety and reliability of autonomous vehicles.

[0014] In exemplary embodiments, the method is for this reason used for analysis in a LIDAR system or a similar system and, for example, in a motor vehicle environment, without the invention being limited to these cases.

[0015] In various exemplary embodiments, LIDAR data typically contains signal contributions from diffuse backscatter, light reflections on objects, ambient light, interfering light signals from other light sources in the environment, etc. These data are generally known and can be displayed in a histogram.

[0016] In various exemplary embodiments, analysis of the backscatter histogram can accordingly mean that during optical runtime measurements (optical pulse runtime methods), the signal contribution of diffuse backscatter, the amount of ambient light, and the effective detection range can be determined from these data. Therefore, backscatter histogram data can be suitable for such analysis and determination because they can essentially contain the signal contributions of diffuse backscatter and the amount of ambient light.

[0017] In a number of exemplary embodiments, the optical runtime measurement is based on the so-called TCSPC (time-correlated single photon counting) measurement principle, in particular in LIDAR-based exemplary embodiments. These methods typically periodically emit light pulses lasting a few nanoseconds and mark the start time of the measurement. During the period until the next light pulse (measurement time), the light reflected by the object or the backscattered light is detected by a light detection receiving element (e.g., a single photon avalanche diode (SPAD)), where light can also be detected within a short time range before the light pulse is emitted. The measurement time is divided into multiple short time periods here (e.g., 500ps). Each time period can be assigned a time point corresponding to the duration of the start time (e.g., in a 500ps time period, 250ps can be assigned to the first time period, and 750ps can be assigned to the second time period, etc.).

[0018] Depending on the distance to the object or to the backscattering point, the light arrives at the light detection receiving element at different times. This generates an electrical signal in the light detection receiving element. The electrical signal can then be assigned to one of the time periods using a basically known time-to-digital converter (also called a "TDC" (time-to-digital converter)). Counting the electrical signals ("events") assigned to the time periods produces a so-called histogram or time-correlated histogram (also called a TCSPC histogram), wherein these histograms can, for example, also exist only as pure data and are stored as value pairs consisting of a time period and a corresponding number of entries (things or events). Therefore, the time period together with the number of events assigned to each time period constitutes histogram data, which can basically be represented by a digital signal (or an analog signal).

[0019] Therefore, in TCSPC-based LIDAR measurements, histogram data of back-reflected or back-scattered light can be output with high temporal resolution. This can correspond to an analog-to-digital conversion of the back-reflected or back-scattered light output as a function of time and / or distance. In various exemplary embodiments, the time-dependent histogram data is data generated based on the electrical signals of the light detection receiving element at the (accompanying) measurement time and a time shortly before the measurement time. Therefore, these data typically contain signal contributions composed of diffuse backscatter, light reflections on objects, ambient light, interference light signals from other light sources in the environment, and the like.

[0020] In various exemplary embodiments, the backscatter histogram data herein corresponds to an accumulation of time-correlated histogram data from a plurality of light detecting receiving elements. The accumulation of time-correlated histogram data can facilitate determining various parameters of the measurement (analysis of the backscatter histogram data) because the signal-to-noise ratio (also referred to as "SNR") of the diffuse backscatter contribution and the ambient light contribution is increased compared to other signal contributions (e.g., reflections on an object or interfering light sources). In various exemplary embodiments, this enables better analysis of the backscatter histogram data.

[0021] In some exemplary embodiments, this stems from the fact that the accumulation of multiple time-correlated histogram data (backscatter histogram data) can spread out reflections (signal contributions of objects) at different distances and / or in different regions of the field of view measured by the LIDAR. In such exemplary embodiments, objects typically only exist within a narrow range of the field of view, where the field of view generally describes the spatial region being detected. In contrast, the contributions of diffuse backscatter and ambient light are typically similar across the entire field of view of the LIDAR system.

[0022] Ambient light is also typically constant during the measurement time and therefore typically makes a constant contribution across all time intervals. The signal contribution from reflections from objects is also often peaked, meaning the reflected light is only detected for one or a few time intervals, as the light pulse may be received with a reduced amplitude but a nearly identical pulse duration. For example, with a typical pulse duration of 10 ns, precise positioning may require a temporal resolution of 250 ps.

[0023] During diffuse backscattering, such as that from fog or particles in the air, continuous backscattering occurs during the propagation of low-intensity light. Here, the light pulse may be significantly spread or diffused in time. For example, a 10 ns light pulse with a geometric spread of 1.5 m can readily produce diffuse backscattering within a depth range of 1.5 m. For this reason, in some exemplary embodiments, significantly reduced temporal resolution is sufficient.

[0024] In some exemplary embodiments, backscattered histogram data is generated by one or more histogram accumulation units and provided for analysis. The histogram accumulation unit has multiple signal inputs. The histogram accumulation unit receives time-correlated histogram data at the signal input or at each signal input. Here, it is not necessary to always receive a histogram at each signal input. Some exemplary embodiments provide even more signal inputs, which do not receive histogram data or only receive histogram data according to corresponding configurations. Backscattered histogram data is generated based on the time-correlated histogram received at the signal input.

[0025] In some exemplary embodiments, the maximum number of histogram accumulation units is determined by the number of light detection receiving elements in a system for optical runtime measurement (e.g., a LIDAR system). Here, the histogram accumulation unit can essentially be or comprise an electronic circuit that receives a digital signal or data (e.g., a time-correlation histogram) via a signal input and generates backscatter histogram data as described herein. The electronic circuit can include electronic components, digital storage elements, and the like to perform the functions described herein. The electronic circuit can be implemented using an FPGA (field programmable gate array), a DSP (digital signal processor), and the like. In other exemplary embodiments, the histogram accumulation unit is implemented using a memory and a microprocessor. In other exemplary embodiments, the histogram accumulation unit is implemented using software, wherein the signal input corresponds to a parameter or property of a software function or method. The generation of the backscatter histogram data corresponds to executing a sequence of commands on a computer for performing a specific arithmetic operation, such that after processing all commands, the backscatter histogram data is obtained. In some exemplary embodiments, the histogram accumulation unit can also be implemented using a mixture of hardware-based and software-based components, with the functions described herein distributed across these components accordingly.

[0026] In some exemplary embodiments, the histogram accumulation unit generates backscatter histogram data by summing the received time-correlated histogram data.

[0027] Here, the number of events detected within a time period ("bin") can be summed based on all received time-correlated histogram data, thereby generating backscatter histogram data in which each time period accurately contains the sum of all events within that time period. The time-correlated histogram data is preferably accumulated or summed into integers, so that in some exemplary embodiments, weak diffuse backscatter can be measured. This is advantageous because the SNR of the diffuse backscatter contribution can be increased compared to other contributions.

[0028] In some exemplary embodiments, the histogram accumulation unit calculates an arithmetic mean based on the received time-correlated histogram data to generate the backscattering histogram data.

[0029] Here, the received time-correlated histogram data is summed and divided by the number of signal input terminals. This may be advantageous in some exemplary embodiments implemented with fixed-point and floating-point numbers (as opposed to exemplary embodiments that accumulate integers).

[0030] In some exemplary embodiments, the histogram accumulation unit accumulates the received time-correlated histogram data of a plurality of time periods into one time period to generate backscattered histogram data.

[0031] In some exemplary embodiments, the histogram accumulation unit is further configured to generate the backscatter histogram data without considering time-correlated histogram data received for a time period exceeding a specific time threshold.

[0032] In some exemplary embodiments, the histogram accumulation unit is further configured to weight the received time-correlated histogram data to generate backscattered histogram data.

[0033] In some exemplary embodiments, the histogram accumulation unit is further configured to output backscatter histogram data to determine backscatter. For example, the backscatter histogram data can then be output to a processor, FPGA, etc. to determine backscatter.

[0034] In some exemplary embodiments, a receiving system for optical runtime measurement (eg, a LIDAR system) may include a receiving matrix having a plurality of light detecting receiving elements, wherein each light detecting receiving element is configured to detect light and generate an electrical signal in response to detecting the light.

[0035] In some exemplary embodiments, each light detection receiving element can be activated and deactivated. In some exemplary embodiments, the light detection receiving elements in the receiving matrix are arranged in columns and rows (as is generally known), wherein, without loss of generality, in some exemplary embodiments, the same number of light detection receiving elements is provided in each row.

[0036] In some exemplary embodiments, the device includes a plurality of evaluation units, wherein each evaluation unit is connected to the light detection receiving elements in a column, or each evaluation unit is connected to the light detection receiving elements in a row.

[0037] In some exemplary embodiments, each evaluation unit is configured to generate time-dependent histogram data based on the electrical signal of the light detection receiving element.

[0038] In some exemplary embodiments, only activated light detection receiving elements are considered to generate the time-correlated histogram data.

[0039] In some exemplary embodiments, each signal input of the histogram accumulation unit is connected to one of the evaluation units, so that the time-dependent histogram data is transmitted from the evaluation unit to the corresponding histogram accumulation unit.

[0040] In a method for analyzing backscatter histogram data during an optical runtime measurement, backscatter histogram data are initially received. These backscatter histogram data are generated by a histogram accumulation unit(s).

[0041] Analyzing the received backscatter histogram data. Here, the analysis can involve a calculation or series of calculations used to determine various parameters of the optical runtime measurement (backscatter signal, amount of ambient light, effective detection range, etc.). Here, the calculations use the backscatter histogram data as input to mathematical operations, such as arithmetic averaging or applying a predefined function.

[0042] Here, the backscattered histogram data received can be analyzed by a processor, FPGA, DSP etc. basically. In such exemplary embodiment, the analysis is implemented by software. The analysis of the backscattered histogram data corresponds to executing a sequence of commands for performing specific arithmetic operations on a computer, thereby having analyzed the backscattered histogram data after processing all commands. In other exemplary embodiments, a specific electronic circuit with corresponding electronic components can be provided to analyze the backscattered histogram data. In some exemplary embodiments, the analysis of the backscattered histogram data can also be implemented by a mixture of hardware-based components and software-based components, wherein the methods described herein are correspondingly distributed on these components. The aforementioned exemplary embodiment can be an exemplary embodiment of a device for data processing, which can also include a storage element for data storage in addition.

[0043] The amount of light detected based on diffuse backscatter is typically low compared to the amount of ambient light (e.g., in daylight) and the amount of light reflected from objects, so determining backscatter can be difficult and imprecise. This is why, in some exemplary embodiments, a method for analyzing backscatter histogram data corresponding to the accumulated time-dependent histogram data is used to determine the backscatter signal during the optical runtime measurement.

[0044] Diffuse backscatter at short distances (e.g., 5 m) is typically higher than at long distances (e.g., 200 m) and can continuously decrease. Thus, in some exemplary embodiments, the diffuse backscatter during the optical runtime measurement can have a typical signal form, which can have a maximum backscattered light at short distances and rapidly decrease over longer distances. In such exemplary embodiments, the backscatter signal in the backscatter histogram data is then correlated with the typical signal form. This is advantageous because the backscatter signal can be identified based on the signal form at short distances. However, in such exemplary embodiments, assuming the presence of an object at a short distance, the backscatter signal cannot typically be determined.

[0045] Therefore, in some exemplary embodiments for analyzing received backscatter histogram data, a similarity metric, such as a correlation, is calculated between the received backscatter histogram data and a predetermined reference backscatter signal to determine the backscatter signal.

[0046] Correlation can be a measure of the similarity between two or more temporal or spatial signal processes or a statistical association between signal processes. In some exemplary embodiments, correlation is calculated by correlation integral, which is generally known.

[0047] The predetermined reference backscatter signal may correspond to a typical signal form of backscatter, wherein the typical signal form of backscatter may vary in various exemplary embodiments. In some exemplary embodiments, the predetermined reference backscatter signal may be stored as histogram data in a memory that can be accessed (e.g., by a processor) to analyze the backscatter histogram. In other exemplary embodiments, the predetermined reference backscatter signal may be calculated dynamically (e.g., at a desired time during the analysis) according to a predefined function.

[0048] For example, a typical signal form of the backscatter can be determined by searching for a characteristic peak or sequence of peaks over a short distance, wherein the position, signal form and intensity are evaluated. For example, in an exemplary embodiment with a LIDAR system for optical runtime measurement, the backscatter has a system-dependent signal form and peak position, the LIDAR system having parallax between the transmitter (from which the light pulses are emitted) and the receiver (e.g., a receiving matrix with a plurality of light-detecting receiving elements). In such an exemplary embodiment, it can be checked whether the intensity of the peak corresponds to an object, thereby determining the typical signal form. For example, a typical reference backscatter signal can be as shown in Figure 1 、 Figure 2 and Figure 3A typical reference backscatter signal will be described in more detail below, as shown in FIG. For example, a distinction can be made between single-beam and multi-beam LIDAR systems (a single-beam system can only emit one light pulse (light beam) at a time, while a multi-beam system can emit multiple light pulses simultaneously from different positions, such as the LIDAR system described in DE 10 2017 222971 A1).

[0049] In other exemplary embodiments, the reference backscatter signal may be determined experimentally by simulating various environmental conditions and measuring typical signal forms, locations, and intensities of backscatter.

[0050] In other exemplary embodiments, the reference backscatter signal can be determined by calculating the correlation integral (cross-correlation) between the expected backscatter signal and the received backscatter histogram data. In such exemplary embodiments, the correlation level can be used to assess whether the expected backscatter signal can be used as a reference backscatter signal. Multiple expected backscatter signals can be tested and their correlation levels compared to determine the reference backscatter signal for a particular system.

[0051] In an exemplary embodiment in which a correlation is calculated between the received backscatter histogram data and a predetermined reference backscatter signal by correlation integration, a backscatter signal that varies with time during the measurement time (and the time immediately before the measurement time) is generated, wherein the amplitude of the calculated backscatter signal corresponds to the backscattered light output (hereinafter abbreviated as AB). Therefore, in such an exemplary embodiment, backscatter can be determined based on the backscatter histogram data.

[0052] In some exemplary embodiments, a method for analyzing backscatter histogram data is used to determine an amount of ambient light based on received backscatter histogram data.

[0053] The ambient light level (hereinafter abbreviated as AL) generally corresponds to the signal contribution that is present and detected in a LIDAR system, for example, as a result of sunlight or streetlights that are unrelated to the emitted light pulses. Furthermore, the ambient light level may also include a portion of receiver electronic noise that depends on the temperature. However, in this exemplary embodiment, the contributions of ambient light and noise to the ambient light level are identical and are therefore not differentiated.

[0054] During the measurement time (and the time shortly before the measurement time), the ambient light is typically constant and therefore typically produces a constant contribution across all time periods. The signal contribution of reflections on objects also often has a spike, which means that the reflected light is only detected in one or a few time periods. To this end, in some exemplary embodiments, the amount of ambient light can be determined based on backscatter histogram data received for multiple time periods that are located shortly before the light pulse is emitted (start time). In other exemplary embodiments, the amount of ambient light can be determined based on backscatter histogram data received for multiple time periods corresponding to long distances (as long as there are no objects within the long distance). In further exemplary embodiments, the amount of ambient light can be determined based on a combination of the above methods. In multiple exemplary embodiments, the method for analyzing the received backscatter histogram data is used to determine the effective detection range of the optical runtime measurement based on the backscatter signal and the amount of ambient light.

[0055] The effective detection range (EDR) of optical runtime measurement can correspond to the distance at which a signal generated by reflection from a solid object can still be clearly detected and assigned. Here, the effective detection range basically refers to an object with a predetermined reflectivity and a predetermined constant probability of light detection under a predetermined amount of ambient light.

[0056] In some exemplary embodiments, the effective detection range may correspond to an absolute value (e.g., 100 m). In other exemplary embodiments, the effective detection range may correspond to a relative value, wherein the effective detection range in such exemplary embodiments is relative to a nominal detection range determined for the aforementioned reference value.

[0057] The effective detection range can be reduced by having high backscatter because, in such exemplary embodiments, the light output of the emitted light pulse is suppressed by backscatter as distance increases. Therefore, the light output available for reflection from solid objects is less than in exemplary embodiments with lower backscatter, resulting in a lower reflected light output that decreases again on its way to the receiver.

[0058] In exemplary embodiments with high ambient light levels, the effective detection range may be reduced by a decrease in the SNR, as the ambient light level generally contributes to the noise level. In other exemplary embodiments, the effective detection range increases at low ambient light levels, as this increases the SNR. To this end, the effective detection range of the optical runtime measurement may be determined based on the backscatter signal and the ambient light level according to the received backscatter histogram data.

[0059] In some exemplary embodiments, a transform function is applied to the backscatter signal to determine a signal damping factor.

[0060] As described above, high backscatter can suppress the light output of the emitted light pulses as distance increases, reducing the effective detection range. Thus, the backscatter signal can be used to determine the magnitude of this suppression.

[0061] Therefore, a transformation function is applied to the backscattered signal, wherein, in some exemplary embodiments, the transformation function may be a predefined (mathematical) function that calculates a signal damping factor based on the backscattered signal. In other exemplary embodiments, the transformation function may be a series of calculations. The transformation function may be determined experimentally or obtained empirically. In some exemplary embodiments, the transformation function may be determined experimentally in advance during development, and, for example, key graphs and / or characteristic curves corresponding to the transformation function may be stored in the software.

[0062] In some exemplary embodiments, the signal damping factor may be the distance-dependent percentage reduction in light output due to backscattering.

[0063] Thus, in some exemplary embodiments, the transfer function is determined experimentally. For example, signal damping can be measured under various environmental conditions, making it possible to find a transfer function that determines the signal damping factor from the backscattered signal (the transfer function can also be determined experimentally as described above) and that matches the measured value of signal damping well.

[0064] In some exemplary embodiments, an arithmetic mean is calculated based on the received backscatter histogram data for a plurality of time periods before the start time to determine the amount of ambient light.

[0065] Here, the start time is the time at which the light pulse used to determine the distance to the solid object is emitted. Because ambient light is only detectable shortly before the light pulse is emitted (e.g., 20 nanoseconds), in some exemplary embodiments, it is advantageous to consider the time period for the received backscatter histogram data preceding the start time when determining the amount of ambient light. Furthermore, it is advantageous to calculate the arithmetic mean of multiple time periods to balance statistical fluctuations in ambient light, thereby obtaining a more accurate value for the amount of ambient light. However, the amount of ambient light determined in such exemplary embodiments may result from reflections from very distant objects.

[0066] In some exemplary embodiments, an arithmetic mean is calculated based on backscatter histogram data received for a plurality of time periods exceeding a specific time threshold to determine the amount of ambient light.

[0067] Diffuse backscatter during optical runtime measurements is often no longer detectable at long distances because the light levels are too low. Furthermore, after a light pulse hits the road surface or a solid object, a constant ambient light level can be detected, for example in LIDAR systems, because the light energy or light output is absorbed or reflected.

[0068] To this end, in some exemplary embodiments, it is advantageous to consider time periods for the received backscatter histogram data that exceed a specific time threshold (e.g., starting from a time threshold corresponding to a distance of 20 meters) when determining the amount of ambient light. Furthermore, it is advantageous to calculate an arithmetic mean over multiple time periods to balance statistical fluctuations in ambient light, thereby obtaining a more accurate value for the amount of ambient light. However, the amount of ambient light determined in such exemplary embodiments may result from reflections from distant objects.

[0069] In various exemplary embodiments, an arithmetic mean is calculated based on backscatter histogram data received for a plurality of time periods before a start time and based on backscatter histogram data received for a plurality of time periods exceeding a specific time threshold to determine the amount of ambient light.

[0070] In such an exemplary embodiment, the two aforementioned methods are combined to determine the amount of ambient light. This is advantageous because the potential influence of very distant objects on the determination of the amount of ambient light is thus reduced. Furthermore, calculating the arithmetic mean is advantageous because this can further reduce statistical fluctuations.

[0071] In some exemplary embodiments, the amount of ambient light is determined by determining a local minimum that meets a specific or predetermined criterion based on the received backscatter histogram data exceeding a specific time threshold. As described above, in some exemplary embodiments, a constant amount of ambient light can be determined for a larger distance (corresponding to the determined time threshold) if no reflections from an object contribute to the received backscatter histogram data within the time range. In such exemplary embodiments, the amount of ambient light can correspond to a local minimum in the received backscatter histogram data exceeding the specific time threshold.

[0072] The signal contribution resulting from reflections from objects typically has a specific signal form that can extend over multiple time periods. If multiple objects are present at a greater distance, in some exemplary embodiments, the two signal forms can overlap, resulting in local minima that do not correspond to the amount of ambient light. This is why, in some exemplary embodiments, only those local minima that meet specific or predetermined criteria are considered for determining ambient light, where this specific or predetermined criterion represents the various possibilities for the presence of a local minimum in the received backscatter histogram data exceeding a specific time threshold. Local minima can be classified based on this criterion.

[0073] In some exemplary embodiments, the amount of ambient light can be determined by initially determining a first local minimum and classifying the first local minimum (starting from a time threshold). If the local minimum does not meet the requirements for determining the amount of ambient light according to the classification, another local minimum can be searched for in the received backscatter histogram data, wherein, in such exemplary embodiments, the other local minimum is after the time threshold and comes after the first local minimum. The search can be continued accordingly until the end of the measurement time. If the local minimum meets the requirements for determining the amount of ambient light according to the classification, the amount of ambient light is determined to be the local minimum. In some exemplary embodiments, determining the amount of ambient light involves the following steps:

[0074] calculating an arithmetic mean value based on the received backscattering histogram data of a plurality of time periods before the start time to obtain a first ambient light amount;

[0075] Determining a local minimum value that meets a specific criterion based on the received backscattering histogram data exceeding a specific time threshold to obtain a second ambient light amount; and

[0076] The amount of ambient light is determined based on comparing the first amount of ambient light to the second amount of ambient light, wherein the amount of ambient light is determined to be the smaller of the two amounts of ambient light.

[0077] Based on the received backscatter histogram data for multiple time periods prior to the start time, an arithmetic mean is initially calculated to obtain a first ambient light quantity. This can be expressed algorithmically as min_ambient = first ambient light quantity, where min_ambient corresponds to the minimum value to be determined for the ambient light quantity. Subsequently, a local minimum in the received backscatter histogram data exceeding a specific time threshold is determined, and this local minimum corresponds to a second ambient light quantity. This can be written as current_far_ambient = second ambient light quantity. As described above, this local minimum is classified according to specific criteria. If the local minimum meets the requirements for determining the ambient light quantity according to the classification, the smaller of the two ambient light quantities is set to min_ambient. This can be expressed algorithmically as min_ambient = min(min_ambient, current_far_ambient). If the local minimum does not meet the requirements for determining the ambient light quantity according to the classification, the next local minimum is determined as the second ambient light quantity and classified again, as described above, and so on. If the search period reaches the end of the measurement time, the ambient light quantity is set to AL = min_ambient.

[0078] The described method may thus correspond to a combination of the two methods described above. This may be advantageous because statistical fluctuations and the influence of distant objects are thereby reduced, so that the amount of ambient light can be determined more accurately.

[0079] In some exemplary embodiments, the effective detection range of the optical runtime measurement is determined with the aid of a predefined function.

[0080] As described above, the effective detection range can be determined based on the backscatter signal (AB) and the amount of ambient light (AL). In some exemplary embodiments, the predefined function here can be a predefined (mathematical) function that calculates the effective detection range based on the backscatter signal and the amount of ambient light. In other exemplary embodiments, the predefined function can be a series of calculations. The predefined function can be determined experimentally or obtained from experience. This can be formally expressed as: DER = f(AL, AB), where f is the predefined function.

[0081] In some exemplary embodiments, an effective detection range of the optical runtime measurement is determined based on a characteristic map.

[0082] Here, the characteristic diagram can be a simple, tabular representation that is not very computationally demanding and depicts the relationship between the input and output variables of a system. A characteristic diagram can be used to represent virtually any mathematical relationship or formula where the number of input variables is limited. Therefore, in some exemplary embodiments, the characteristic diagram can be a graph of the function f(AL, AB), which stores the values ​​of the effective detection range for a large number of values ​​of AL and AB. This is advantageous because, in such exemplary embodiments, the determination of the effective detection range does not require computation, thereby conserving computational power.

[0083] As described above, in some exemplary embodiments, the effective detection range of the optical runtime measurement is determined based on a comparison with a predetermined reference value.

[0084] Some exemplary embodiments relate to a device for data processing, comprising means for performing the steps of the method described herein. The device may be built into a motor vehicle or implemented in a component of a motor vehicle, such as an onboard computer, controller, etc. Furthermore, the device may include one or more (micro)processors, memory devices, and other electronic components generally required to implement the functionality described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Exemplary embodiments of the present invention will now be described exemplarily with reference to the accompanying drawings, in which:

[0086] Figure 1A first exemplary embodiment of a reference backscatter signal is shown;

[0087] Figure 2 A second exemplary embodiment of a reference backscatter signal is shown;

[0088] Figure 3 A third exemplary embodiment of a reference backscatter signal is shown;

[0089] Figure 4 An embodiment of a receiving system for optical distance measurement is shown;

[0090] Figure 5 A flow chart illustrating a first exemplary embodiment of a method for analyzing backscatter histogram data during an optical runtime measurement;

[0091] Figure 6 shows a flow chart of a second exemplary embodiment of a method for analyzing backscatter histogram data during an optical runtime measurement;

[0092] Figure 7 a flow chart illustrating a third exemplary embodiment of a method for analyzing backscatter histogram data during an optical runtime measurement; and

[0093] Figure 8 A flow chart of a fourth exemplary embodiment of a method for analyzing backscatter histogram data during an optical runtime measurement is shown. DETAILED DESCRIPTION

[0094] Figure 1 A first exemplary embodiment of a reference backscatter signal is shown.

[0095] Figure 1 The reference backscatter signal shown corresponds to a typical kind of signal form generated in a coaxial LIDAR system, ie a LIDAR system without parallax between the transmitter and receiver. The reference backscatter signal (in other words, the intensity of the backscattered light) decreases monotonically over time.

[0096] Figure 2 A second exemplary embodiment of a reference backscatter signal is shown.

[0097] Figure 2 The reference backscatter signal shown corresponds to a typical type of signal form generated in a dual-axis single-beam LIDAR system. In a dual-axis system (i.e., the transmitting and receiving systems are at a defined distance (e.g., 10 cm) and have a defined beam divergence), overlap only occurs starting from a minimum distance (the beginning of the signal rise of the reference backscatter signal). Thereafter, the reference backscatter signal is calculated according to Figure 1The reference backscatter signal has a low intensity at very small distances, rises to a maximum value, and then decreases monotonically with time.

[0098] Figure 3 A third exemplary embodiment of a reference backscatter signal is shown.

[0099] Figure 3 The reference backscatter signal shown corresponds to a typical signal form typically generated in a dual-axis multi-beam LIDAR system (e.g., according to DE 10 2017 222 971 A1). This reference backscatter signal is similar to the signal in a dual-axis single-beam LIDAR system, but has multiple maxima at distances where the individual beams pass through the field of view of the light detection receiving element. At greater distances, the intensity decreases monotonically over time.

[0100] Figure 4 An exemplary embodiment of a receiving system 1 for optical distance measurement is shown.

[0101] The receiving system 1 includes a receiving matrix 2 on which a plurality of light detecting receiving elements (ENxM, in this exemplary embodiment, E0,0 to E127,255) are arranged in rows (Z0 to Z127) and columns (S0 to S255). M = 256 light detecting receiving elements (E0,0 to E127,255) are arranged in each of N = 128 rows (Z0 to Z127) (corresponding to M = 256 columns (S0 to S255)). In this exemplary embodiment, the light detecting receiving elements (E0,0 to E127,255) are SPADs.

[0102] The receiving system 1 also includes a plurality of evaluation units (A0 to A127), each of which is connected to a row (Z0 to Z127) of light detection receiving elements (E0,0 to E127,255) via a multiplexer (not shown). In each row (Z0 to Z127), only two light detection receiving elements (E0,0 and E0,1 to E127,0 and E127,1) in columns S0 and S1 are activated at any given time (indicated by the second circle within the light detection receiving elements (E0,0 and E0,1 to E127,0 and E127,1)). When light is detected, the activated light detection receiving elements (E0,0 and E0,1 to E127,0 and E127,1) generate electrical signals, from which time-correlated histogram data is generated with the aid of a time-to-digital converter (not shown) in each evaluation unit (A0 to A127). In this exemplary embodiment, the time-correlated histogram data of two activated light detection receiving elements (E0,0 and E0,1 to E127,0 and E127,1) are added in the evaluation unit (A0 to A127) to generate and output the time-correlated histogram data. In other exemplary embodiments, any desired number of light detection receiving elements (M = 256 light detection receiving elements (E0,0 to E127,255)) in each row can be activated, for example, E0,0 to E0,10, E1,0 to E1,10, E2,0 to E2,10, ..., E127,0 to E127,10.

[0103] The receiving system 1 also has a plurality of histogram accumulation units (HA0 to HAX). Each histogram accumulation unit (HA0 to HAX) has P = 16 signal input terminals (not explicitly shown), wherein each signal input terminal is connected to a corresponding evaluation unit (A0 to A127). Therefore, in this exemplary embodiment, X = N / P = 8 histogram accumulation units are required in N = 128 rows (Z0 to Z127), and these histogram accumulation units accumulate the time-correlated histogram data of P = 16 evaluation units (A0 to A127) accordingly. The time-correlated histogram data output by the evaluation units (A0 to A127) are transmitted to the histogram accumulation units (HA0 to HAX) so that these time-correlated histogram data are received at the signal input terminals. Based on the received time-correlated histogram data, the histogram accumulation units (HA0 to HAX) generate backscattered histogram data. In this exemplary embodiment, the time-correlated histogram data received at each signal input is summed to generate backscatter histogram data.

[0104] The receiving system 1 also has a device 3 for data processing, which has a processor and a storage element (not shown). The histogram accumulation units (HA0 to HAX) output the generated backscatter histogram data, which are received by the device 3 for data processing. The device 3 for data processing analyzes the received backscatter histogram data. In this exemplary embodiment, the device 3 for data processing calculates the received backscatter histogram data and the backscatter histogram data from the device 3. Figure 3 The backscatter signal is correlated with a reference backscatter signal to determine the backscatter signal, such as determining the backscatter signal as a backscatter indicator or a backscatter signal strength.

[0105] Figure 5 A flow chart of a first exemplary embodiment of a method 20 for analyzing backscatter histogram data during an optical runtime measurement is shown.

[0106] As explained herein, backscatter histogram data is received at 21 .

[0107] As explained herein, the received backscatter histogram data is analyzed at 22 .

[0108] As explained herein, a similarity measure between the received backscatter histogram data and a predetermined reference backscatter signal is calculated at 23 to determine a backscatter signal.

[0109] As explained herein, a transform function is applied to the backscatter signal at 24 to obtain a signal damping factor.

[0110] Therein, as explained herein, the transformation function in step 24 is (preliminarily) determined experimentally at 25 .

[0111] Figure 6 A flow chart of a second exemplary embodiment of a method 30 for analyzing backscatter histogram data during an optical runtime measurement is shown.

[0112] As explained herein, backscatter histogram data is received at 31 .

[0113] As explained herein, the received backscatter histogram data is analyzed at 32 .

[0114] As explained herein, the amount of ambient light is determined at 33 based on the received backscatter histogram data.

[0115] Steps 34 to 36 are options to be implemented individually.

[0116] As explained herein, an arithmetic mean is calculated at 34 based on the received backscatter histogram data for a plurality of time periods prior to the start time to determine the amount of ambient light.

[0117] As explained herein, an arithmetic mean is calculated at 35 based on the received backscatter histogram data for a plurality of time periods exceeding a certain time threshold to determine the amount of ambient light.

[0118] As explained herein, at 36 , an arithmetic mean is calculated based on the backscatter histogram data received for a plurality of time periods prior to the start time and based on the backscatter histogram data received for a plurality of time periods exceeding a certain time threshold to determine the amount of ambient light.

[0119] Figure 7 A flow chart of a third exemplary embodiment of a method 40 for analyzing backscatter histogram data during an optical runtime measurement is shown.

[0120] As explained herein, backscatter histogram data is received at 41 .

[0121] As explained herein, the received backscatter histogram data is analyzed at 42 .

[0122] As explained herein, the amount of ambient light is determined from the received backscatter histogram data at 43. Steps 44 and 45 are options that can each be implemented separately.

[0123] As explained herein, a local minimum that meets certain criteria is determined at 44 based on the received backscatter histogram data exceeding a certain time threshold to determine the amount of ambient light.

[0124] As explained herein, at 45, an arithmetic mean is calculated based on the received backscatter histogram data for a plurality of time periods prior to the start time to obtain a first ambient light amount, a local minimum satisfying a specific criterion is determined based on the received backscatter histogram data exceeding a specific time threshold to determine a second ambient light amount, and the ambient light amount is determined based on comparing the first ambient light amount with the second ambient light amount, wherein the ambient light amount is determined as the smaller of the two ambient light amounts.

[0125] Figure 8 A flow chart of a fourth exemplary embodiment of a method 50 for analyzing backscatter histogram data during an optical runtime measurement is shown.

[0126] As explained herein, backscatter histogram data is received at 51 .

[0127] As explained herein, the received backscatter histogram data is analyzed at 52 .

[0128] As explained herein, a similarity metric is calculated at 53 between the received backscatter histogram data and a predetermined reference backscatter signal to determine a backscatter signal.

[0129] As explained herein, the amount of ambient light is determined at 54 based on the received backscatter histogram data.

[0130] As explained herein, an effective detection range for the optical runtime measurement is determined at 55 based on the backscatter signal and the amount of ambient light, wherein the backscatter signal is determined, for example, as a backscatter indicator or a backscatter signal strength.

[0131] Steps 56 to 58 are options to be implemented individually.

[0132] As explained herein, the effective detection range of the optical runtime measurement is determined at 56 by means of a predefined function.

[0133] As explained herein, an effective detection range for the optical runtime measurement is determined at 57 based on the characteristic diagram.

[0134] As explained herein, a valid detection range for the optical runtime measurement is determined at 58 based on a comparison with a predetermined reference value.

[0135] Reference Signs List

[0136] 1Receiving system

[0137] 2 receiving matrix

[0138] 3 devices

[0139] 20, 30, 40, 50 methods

[0140] 21, 31, 41, 51 receive backscatter histogram data

[0141] 22, 32, 42, 52 analyze the received backscatter histogram data

[0142] 23, 53 calculates a similarity measure between the received backscatter histogram data and a predetermined reference backscatter signal to determine the backscatter signal

[0143] 24 Apply the transformation function to the backscattered signal to obtain the signal damping factor

[0144] 25 Determine the transformation function through experiments

[0145] 33, 43, 54 determine the amount of ambient light based on the received backscatter histogram data

[0146] 34 Calculate the arithmetic mean value based on the backscatter histogram data received for multiple time periods before the start time to determine the amount of ambient light

[0147] 35 Calculate the arithmetic mean based on the backscatter histogram data received for multiple time periods exceeding a specific time threshold to determine the amount of ambient light

[0148] 36 calculates an arithmetic mean value based on the backscattered histogram data received for a plurality of time periods before the start time and based on the backscattered histogram data received for a plurality of time periods exceeding a specific time threshold to determine the amount of ambient light

[0149] 44 determines the amount of ambient light by determining a local minimum that meets a specific standard based on the backscattered histogram data received that exceeds a specific time threshold

[0150] 45. Calculating an arithmetic mean based on the received backscatter histogram data for a plurality of time periods before the start time and based on the received backscatter histogram data for a plurality of time periods exceeding a specific time threshold to determine a first ambient light amount; determining a local minimum value that meets a specific criterion based on the received backscatter histogram data exceeding the specific time threshold to determine a second ambient light amount; and determining the ambient light amount based on comparing the first ambient light amount with the second ambient light amount, wherein the ambient light amount is determined as the smaller of the two ambient light amounts.

[0151] 55 Determine the effective detection range of optical runtime measurement based on backscatter signal and ambient light

[0152] 56 Determine the effective detection range of optical runtime measurement using predefined functions

[0153] 57 Determining the effective detection range of optical runtime measurement based on the characteristic diagram

[0154] 58 Determine the effective detection range of the optical runtime measurement based on comparison with a predetermined reference value

[0155] A0 to A127 evaluation units

[0156] ENxM, E0,0 to E127,255 light detection receiving element

[0157] HA0 to HAX histogram accumulation units

[0158] Columns S0 to S255

[0159] Rows Z0 to Z127.

Claims

1. A method for analyzing backscattering histogram data in an optical pulse run time method, comprising: Periodically emits light pulses; The light detection and ranging LIDAR device activates multiple groups of light detection receiving elements; A processor in the LIDAR device creates a time correlation histogram based on signals generated by the light detection receiving element in different time periods, and the processor accumulates the time correlation histogram to generate received backscattering histogram data; calculating, by the processor, a similarity metric between the received backscatter histogram data and predetermined reference backscatter data; determining a backscatter signal in response to calculating the similarity metric; as well as The processor determines the amount of ambient light based on a local minimum that meets a specific or predetermined criterion, wherein the local minimum is determined based on backscatter histogram data received exceeding a specific time threshold.

2. The method according to claim 1, wherein Calculating the similarity measure includes: A correlation between the received backscatter histogram data and the reference backscatter data is calculated by the processor.

3. The method according to claim 1, further comprising: An effective detection range for runtime measurement is determined by the processor based on the backscattered signal and the amount of ambient light.

4. The method according to claim 1, further comprising: applying, by the processor, a transform function to the backscattered signal; as well as A signal damping factor is determined, by the processor, in response to applying the transform function.

5. The method according to claim 4, further comprising: The processor determines the transformation function through experiments.

6. The method according to claim 1, further comprising: The processor calculates an arithmetic mean value based on the received backscatter histogram data of multiple time periods before the start time; as well as The amount of ambient light is determined, by the processor, in response to calculating the arithmetic mean.

7. The method according to claim 6, wherein: The arithmetic mean is calculated based on the backscatter histogram data received for a plurality of time periods exceeding a specific time threshold.

8. The method according to claim 1, wherein Determining the amount of ambient light includes: The processor calculates an arithmetic mean value based on the received backscattering histogram data of multiple time periods before the start time to obtain a first ambient light amount; The processor determines, based on the received backscatter histogram data exceeding a specific time threshold, a local minimum value meeting a specific standard to obtain a second ambient light amount; and The amount of ambient light is determined, by the processor, based on comparing the first amount of ambient light to the second amount of ambient light, wherein the amount of ambient light is determined to be the lesser of the first amount of ambient light and the second amount of ambient light.

9. An apparatus for data processing, comprising means for implementing the method according to any one of claims 1 to 8.

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