Method for operating an iToF LiDAR system in which corrected received variables are determined on the basis of validated received variables, iToF LiDAR system, driver assistance system and vehicle

Abstract

A method is disclosed for operating an iToF LiDAR system, in particular of a vehicle. In the method, a transmitted light beam is transmitted into a surveillance area using a transmitting device. A portion of a received light beam coming from the surveillance area, which originates from the transmitted light beam reflected by at least one object target, is received by at least one receiving area of ​​a receiving device of the LiDAR system. From the portion of the received light beam received by the at least one receiving area, at least one received variable (A1) is determined, which characterizes a signal intensity of the received portion of the received light beam in a time range of the reception. The at least one received variable (A1) is assigned to the at least one receiving area that is hit by the at least one portion of the received light beam.At least two received quantities (A1) of the same type are determined from a portion of the received light beam. At least two of the determined received quantities (A1) of the same type are validated. Based on at least two validated received quantities (A. 1,val ) of the same type, at least one corrected received quantity (A 1,corr ) is determined. Based on at least one validated reception variable (A 1,val ) and at least one corrected reception quantity (A 1,corr ) of the same type, at least one distance value is determined.

Description

Technical field

[0001] The invention relates to a method for operating an iToF LiDAR system, in particular an iToF LiDAR system of a vehicle, in which at least one transmitted light beam is sent into at least one surveillance area using at least one transmitting device of the LiDAR system, at least a part of at least one received light beam coming from the at least one monitoring area, which originates from the at least one transmitted light beam reflected by at least one object target, is received by at least one receiving area of ​​a receiving device of the LiDAR system, at least one reception quantity is determined from the at least one part of the at least one received light beam received by the at least one reception area, which reception quantity characterizes a signal intensity of the received part of the received light beam in a time range of reception, wherein the at least one reception quantity is assigned to the at least one reception area which is hit by the at least one part of the at least one received light beam, on the basis of at least one reception variable, at least one distance variable is determined which characterizes a distance of an object target, at which the at least one transmitted light beam was reflected, to the LiDAR system.

[0002] Furthermore, the invention relates to an iToF LiDAR system, in particular an iToF LiDAR system of a vehicle, which has at least one transmitting device with which transmitted light beams can be sent into at least one monitoring area, at least one receiving device which has at least one receiving area with which received light beams coming from the at least one monitoring area, which originate from transmitted light beams reflected at at least one object target, can be received, at least one means for determining reception quantities from parts of received light beams received with a reception area, at least one means for assigning reception variables to reception areas, and at least one means for determining at least one distance quantity characterizing a distance of an object target, at which at least one transmitted light beam was reflected, to the LiDAR system, based on at least one received quantity.

[0003] Furthermore, the invention relates to a driver assistance system with at least one iToF LiDAR system, wherein the at least one iToF LiDAR system comprises at least one transmitting device with which transmitted light beams can be sent into at least one monitoring area, at least one receiving device which has at least one receiving area with which received light beams coming from the at least one monitoring area, which originate from transmitted light beams reflected at at least one object target, can be received, at least one means for determining reception quantities from parts of received light beams received with a reception area, at least one means for assigning reception variables to reception areas, and at least one means for determining at least one distance variable which characterizes a distance of an object target, at which at least one transmitted light beam was reflected, to the LiDAR system, on the basis of at least one reception variable.

[0004] Furthermore, the invention relates to a vehicle with at least one iToF LiDAR system, wherein the at least one iToF LiDAR system comprises at least one transmitting device with which transmitted light beams can be sent into at least one monitoring area, at least one receiving device which has at least one receiving area with which received light beams coming from the at least one monitoring area, which originate from transmitted light beams reflected at at least one object target, can be received, at least one means for determining reception quantities from parts of received light beams received with a reception area, at least one means for assigning reception variables to reception areas, and at least one means for determining at least one distance variable which characterizes a distance of an object target, at which at least one transmitted light beam was reflected, to the LiDAR system, on the basis of at least one reception variable. State of the art

[0005] US 2020 / 0072946 A1 discloses a flash LIDAR system or circuit configured to compensate for background. The system includes a control circuit, a timing circuit, a transmitter array with a plurality of transmitters, and a detector array with a plurality of detectors (e.g., an array of single-photon detectors). The control circuit implements a pixel processor that measures the time of flight of the illumination pulse on the path from the transmitter array to the target and back to the detector array, using direct or indirect ToF measurement methods.

[0006] The invention is based on the object of designing a method, an iToF LiDAR system, a driver assistance system and a vehicle of the type mentioned above, in which the determination of distance variables can be further improved, in particular errors in the determination of distance variables due to oversaturation of reception areas can be corrected. Disclosure of the invention

[0007] The object is achieved according to the invention in the method in that at least two received variables of the same type are determined from at least a part of the at least one received light beam, at least two of the determined received variables of the same type are validated, at least one corrected received variable is determined on the basis of at least two validated received variables of the same type, at least one distance variable is determined on the basis of at least one validated received variable and at least one corrected received variable of the same type.

[0008] According to the invention, at least a portion of a reflected received light beam is received by at least one receiving region. The received light beam originates from a transmitted light beam that was reflected by at least one object target. At least two received variables of the same type are determined from at least a portion of the received light beam received by at least one receiving region.

[0009] The received variables characterize the intensity of that portion of the received light beam that hits the respective receiving area in the time domain of reception. Using the received variables, the temporal signal profile of the received light beam can be approximated.

[0010] Received variables of the same type mean that the received variables are directly comparable. Two types of received variables can advantageously be used: differential correlation signals and signal curve variables. Received variables of the "differential correlation signals" type can contain signals, in particular electrical signals, that are caused by the portion of the received light beam striking the corresponding reception area and, if appropriate, also by portions of ambient rays, in particular ambient light. Received variables of the "signal curve variable" type can contain electrical signals that have been adjusted for the portion caused by ambient rays. Signal curve variables contain only the portion of the signals, in particular electrical signals, that are caused by received light beams.Both types of reception quantities characterize a signal intensity of the received part of the received light beam in the time domain of reception.

[0011] The more usable received variables of a type are available, the more accurately the signal profile of the received transmitted light beam can be approximated. Received variables that are not validated, especially those from oversaturated reception areas, can be ignored. According to the invention, non-validated received variables can be replaced with corrected received variables.

[0012] In LiDAR systems, sufficiently long integration times can lead to oversaturation of reception areas due to strong received light beams, which originate from transmitted light beams reflected by highly reflective targets, particularly retroreflective targets. No meaningful distance can be determined from the received values ​​of the oversaturated reception areas. The intensity of the received light beams can exceed the limit of the corresponding reception area, causing it to become oversaturated. Oversaturation can be detected, in particular, by the received value exceeding a predefined saturation threshold. The saturation threshold can be specified by the hardware used, particularly the analog-to-digital converter.

[0013] A lack of received parameters of one type can result in the distance to a target object not being able to be determined from the received parameters of that type, or insufficiently accurately. The invention allows the number of available, meaningful received parameters to be increased. This allows distances to be determined even for target objects whose reflections lead to oversaturation of the reception areas. In this way, a complete distance map to the surveillance area can be determined.

[0014] An iToF LiDAR system is a LiDAR system that uses an indirect time-of-flight method to determine distance quantities that characterize the distance of a detected target relative to the LiDAR system, in particular relative to the respective reception area. For this purpose, a transmitted light beam is sent into a surveillance area. The transmitted light beam is modulated in terms of its intensity. If the transmitted light beam hits targets in the surveillance area, it is reflected. The portion of the transmitted light beam reflected back towards the LiDAR system, which hits at least one reception area, is converted into received quantities. The time of flight between the transmission of the transmitted light beam and the reception of the corresponding received light beam depends on the distance of the reflecting target from the LiDAR system.The time of flight causes a phase shift between the signal of the transmitted light beam and the signal of the received light beam. This phase shift can be determined from the received parameters. Furthermore, a distance parameter can be determined from the received parameters, which characterizes the distance between the reflecting target and the hit reception area.

[0015] Advantageously, the signal of the at least one transmitted light beam can be approximated as a sine wave. In this way, a uniformly modulated transmitted light beam can be realized. Accordingly, the signal of the received light beam, which corresponds to the reflected signal of the transmitted light beam, can be approximated as a sine wave.

[0016] Advantageously, the at least one transmitted light beam can have a wavelength of approximately between 900 nm and 1000 nm, in particular approximately 940 nm. In this way, the at least one transmitted light beam and the at least one received light beam are invisible to humans. The beams therefore do not cause irritation or interference. This allows the LiDAR system to be used in road traffic.

[0017] The at least one transmitted light beam can comprise or consist of a transmitted light signal. In this way, additional information can be transmitted with the at least one transmitted light beam. Advantageously, the at least one transmitted light signal can be coded. This simplifies the assignment of the at least one transmitted light beam on the receiver side.

[0018] A distance quantity within the meaning of the invention is a quantity that characterizes the distance of a detected object target from a reference point, in particular a reception area of ​​the LiDAR system. In an indirect time-of-flight method, a shift, in particular a phase shift, between a transmitted light beam in the form of an amplitude-modulated transmitted light beam and the corresponding received light beam can be used as the distance quantity. Alternatively or additionally, the absolute value of a geometric distance can be used as the distance quantity.

[0019] An object target within the meaning of the invention is a location on an object at which transmitted light rays can be reflected. Each object can have multiple object targets.

[0020] Advantageously, the LiDAR system can be a flash LiDAR system. In a flash LiDAR system, each transmitted light beam – similar to a flashlight – illuminates large portions of the at least one surveillance area, in particular the entire surveillance area.

[0021] The LiDAR system can advantageously be configured as a laser-based distance measuring system. A laser-based distance measuring system can have at least one laser, in particular a diode laser, as the light source of a transmitting device. The at least one laser can be used to transmit, in particular, pulsed and / or amplitude-modulated transmitted light beams. The laser can emit transmitted light beams in wavelength ranges that are visible or invisible to the human eye. Accordingly, the receiving device can have at least one sensor designed for the wavelength of the transmitted transmitted light beam, in particular a CCD sensor, an active pixel sensor, in particular a CMOS sensor, or the like. Such sensors have a plurality of receiving areas, in particular pixels or pixel groups.Such sensors can be operated in such a way that the detected reception variables can be assigned to the respective reception areas.

[0022] The invention can be used in vehicles. An essential functional feature of a vehicle is its ability to move. Vehicles can be motor vehicles. The invention can advantageously be used in land vehicles, in particular cars, trucks, buses, motorcycles, drones, mobile robots, mobile machines, in particular construction or transport machines such as cranes, excavators, or the like, aircraft, in particular aerial drones, and / or (underwater) vehicles, in particular (underwater) drones. The invention can also be used in vehicles that can be operated autonomously or semi-autonomously. However, the invention is not limited to vehicles. It can also be used in stationary operation.

[0023] Advantageously, the LiDAR system can have at least one processor device, in particular an electronic control and evaluation device, with which the LiDAR system can be controlled and reception variables can be determined and / or processed.

[0024] The LiDAR system can advantageously be connected to or be part of at least one electronic control device of a vehicle or machine, in particular a driver assistance system or the like. In this way, at least some of the functions of the vehicle or machine can be operated autonomously or semi-autonomously.

[0025] The LiDAR system can be used to detect stationary or moving objects, in particular vehicles, persons, animals, plants, obstacles, road surface irregularities, in particular potholes or stones, road markings, traffic signs, open spaces, in particular parking spaces, precipitation or the like, and / or movements and / or gestures.

[0026] Advantageously, the method for operating an iToF LiDAR system can be implemented using software and / or hardware. Software means can be easily integrated into existing processor systems.

[0027] In an advantageous embodiment of the method, the received light beam is converted into at least one received variable of a first type, in particular into at least one differential correlation signal, and a corresponding received variable of a second type, in particular a signal curve variable, is determined from the at least one received variable of the first type, in particular the at least one differential correlation signal and / or at least one corrected received variable of the first type, in particular at least one corrected differential correlation signal, is determined from at least one corrected received variable of the second type, in particular from at least one corrected signal waveform variable, and at least one distance variable is determined from at least one validated received variable of the first type, in particular from at least one validated differential correlation signal, which forms the basis for a validated received variable of the second type, in particular for a validated signal waveform variable, and at least one corrected received variable of the first type, in particular a corrected differential correlation signal.

[0028] Advantageously, at least one received variable of a first type can be a differential correlation signal. The at least one received variable of the first type, in particular the differential correlation signal, can be composed of the effect of the received light beam plus base rays such as ambient light, noise, or the like on the corresponding reception area. The at least one received variable of the second type, in particular the signal profile variable, can characterize the temporal signal profile of the received light beam for the corresponding reception area without base rays. The signal profile variable can be the corresponding differential correlation signal minus an offset. The offset corresponds to the base rays. The signal profile variable can therefore be regarded as a normalized differential correlation signal.

[0029] The receiving device can advantageously have at least one imager chip. The imager chip can receive received light beams with spatial resolution and convert them into signal-processable signals, in particular electrical signals. The imager chip can advantageously have a CCD sensor. The pixels of the imager chip, in particular of the CCD sensor, are reception areas for received light beams. The portions of received light beams captured by the pixels can be converted into known differential correlation signals in a manner not of further interest here. In English, differential correlation signals are referred to as "differential correlation signals" or "DCS" for short.

[0030] Advantageously, the received light beam can be converted into at least one differential correlation signal. In this way, the received light radiation incident on a receiving area within a defined time range can be converted into a signal characterizing the amount of radiation. The differential correlation signal thus characterizes the intensity of the portion of the received light beam received in the corresponding receiving area within the defined time range.

[0031] Advantageously, respective differential correlation signals can be determined in different defined time ranges. In this way, the signal profile of the received light beam can be approximated using the differential correlation signals.

[0032] Advantageously, a received variable, in particular a differential correlation signal, can be determined in four temporally equidistant phases within a period of the transmitted light beam. In this way, four "interpolation points" can be determined, with the help of which the signal profile of the received light beam can be determined. The signal profile of the transmitted light beam is known. The received light beam is the transmitted light beam reflected by at least one object target. The signal profile of the received light beam is therefore also known. The signal profile of the received light beam differs from the signal profile of the transmitted light beam by a phase shift, which depends on the time of flight. Using the four differential correlation signals, the phase shift and the distance of the detected object target can be determined.

[0033] Advantageously, the phases in which the differential correlation signals are determined can be specified relative to a trigger signal. In this way, the phases can be specified in a defined manner. Advantageously, the phases can be specified relative to a reference phase of the at least one transmitted light beam, in particular relative to an inflection point of the signal profile of the at least one transmitted light beam. In this way, the phases of the received light beam can be more easily related to the phases of the transmitted light beam. Advantageously, the differential correlation signals can be determined in the four phases 0°, 90°, 180°, and 270°.

[0034] When using a transmitted light beam with a sinusoidal signal, the phase shift φ and the distance r are: φ=π+atan2(DCS3−DCS1DCS2−DCS0) r=c212 πfATSφ

[0035] Where DCS0, DCS1, DCS2, and DCS3 are the four differential correlation signals in the phases 90°, 180°, 270°, and 0°. "c" is the speed of light, and "f ATS “ the frequency of the transmitted light beam.

[0036] Advantageously, a corresponding signal waveform variable can be determined from the at least one differential correlation signal. In this way, the differential correlation signals can be converted into quantities that are easily processed in terms of signal technology, namely signal waveform variables.

[0037] Advantageously, the at least one differential correlation signal can be adjusted for a base intensity resulting from the base radiation, such as ambient light and / or noise. In this way, the signal curve can be approximated relative to a zero line of intensity.

[0038] For the differential correlation signals DCS i and the corresponding signal waveform variables Ai the following context can be used: DCSi=Ai+I0

[0039] Where i=[0, 1, 2, 3] is the index for the respective differential correlation signal DCS, which assigns the phase in which the corresponding differential correlation signal was determined.

[0040] Advantageously, at least one corrected differential correlation signal can be determined from at least one corrected signal waveform variable. At least one distance variable can be determined from at least one validated differential correlation signal, which forms the basis for a validated signal waveform variable, and at least one corrected differential correlation signal. In this way, the at least one distance variable can be determined based on the differential correlation signals.

[0041] Validated differential correlation signals are the differential correlation signals that form the basis for validated signal waveform variables. Validation can be performed based on the differential correlation signals themselves. Alternatively, the corresponding differential correlation signals can be considered "validated" after validation of the associated signal waveform variables. Alternatively, the validated differential correlation signals can be determined from the corresponding validated signal waveform variables, particularly analogously to the corrected differential correlation signals.

[0042] In a further advantageous embodiment of the method, for validating a received variable, it can be checked whether the received variable shows signs of oversaturation of the correspondingly assigned reception area. If the received variable shows signs of oversaturation, the received variable is not validated; otherwise, this received variable is used as the validated received variable. In this way, received variables originating from oversaturated reception areas can be disregarded for determining the distance variables.

[0043] Advantageously, an indication of oversaturation can be the reaching of a saturation threshold. Advantageously, an analog-to-digital converter can be assigned to the at least one reception area. The analog-to-digital converter can convert electrical charges generated by the received light beam in the reception area into digital values. In the event of oversaturation, the analog-to-digital converter outputs its maximum value. The maximum value indicates the saturation threshold. For example, with a 16-bit analog-to-digital converter, the saturation threshold can be 65,536. If a received value corresponds to the saturation threshold, this is a sign of oversaturation of the corresponding reception area.

[0044] In a further advantageous embodiment of the method, if a received variable is not validated, this non-validated received variable can be replaced by a corrected received variable of the same type, which is determined on the basis of at least two validated received variables, in particular two validated received variables of the same type. In this way, a received variable that cannot be used due to oversaturation can be replaced by a corrected received variable that has been corrected using validated received variables. In this way, the number of received variables of the same type required to approximate the signal curve of the received signal can be achieved. Advantageously, the at least one corrected received variable can be determined on the basis of at least two validated received variables of the same type. In this way, further conversion of the variables can be dispensed with.

[0045] In a further advantageous embodiment of the method, the at least one received light beam can be received with several reception areas of a reception area field, in particular with several reception areas of a reception matrix, of the receiving device and / or for at least two reception areas hit, at least one reception variable of the same type is determined from the respective received portion of the at least one received light beam and / or at least two received variables of the same type are determined in different phases of the at least one received light beam and / or for at least one reception area hit, at least two reception variables of the same type are determined in different phases of the at least one received light beam.

[0046] Advantageously, the at least one received light beam can be received by multiple reception areas of a reception area array. In this way, the at least one received light beam can be received with spatial resolution. A portion of the at least one received light beam can fall on each reception area.

[0047] Advantageously, the reception area array can be a reception matrix. This allows the reception areas to be evenly arranged.

[0048] Advantageously, a direction of the at least one reflective object target relative to the reception area field, in particular to the reception matrix, can be determined based on the position of the reception areas hit by the at least one received light beam within the reception area field, in particular within the reception matrix. A spatially resolved LiDAR measurement is thus possible with the reception area field, in particular the reception matrix.

[0049] Advantageously, the reception area field can be a one-dimensional reception matrix. In this way, spatial resolution can be achieved in two dimensions, in particular in the horizontal or vertical direction on the one hand, and in the distance direction on the other. Alternatively, the reception area field can advantageously be a two-dimensional reception matrix. In this way, the position of the at least one object target can be determined in three dimensions, in particular in the horizontal direction, in the vertical direction, and in the distance direction.

[0050] Advantageously, alternatively or additionally, at least one received variable can be determined for at least two reception areas from the respective received portion of the at least one received light beam. In this way, received variables from reception areas located near an oversaturated reception area can be used to reconstruct the received variables of the oversaturated reception area.

[0051] Advantageously, alternatively or additionally, at least two received variables of the same type can be determined in different phases of the at least one received light beam. In this way, the temporal signal profile of the at least one received light beam can be better approximated.

[0052] Advantageously, alternatively or additionally, at least two received variables of the same type can be determined in different phases of the received light beam for at least one received area. In this way, the temporal signal profile of the portion of the at least one received light beam that hits the respective receiving area can be better approximated.

[0053] In a further advantageous embodiment of the method, at least one corrected received variable for a reception area can be determined based on at least two validated received variables of the same type from at least one other reception area, in particular at least one reception area in a defined neighborhood of the reception area with the at least one corrected received variable. In this way, received variables from non-oversaturated reception areas can be used to reconstruct received variables from oversaturated reception areas.

[0054] Advantageously, at least one corrected received variable can be determined based on at least one reception area in a defined neighborhood of the reception area with the at least one corrected received variable of the same type. In this way, the determination of the corrected received variable can be further improved. Reception areas in a defined neighborhood are generally struck by received light beams that originate from a correspondingly small angular range. In particular, portions of the received light beam from the same angular range can be reflected by neighboring object targets.

[0055] Advantageously, the defined neighborhood area can comprise the reception areas closest to the oversaturated reception area, in particular adjacent to it. Additionally, the defined neighborhood area can comprise more distant reception areas, in particular the reception areas next to the next but one or more reception areas after the next but one. In this way, the reception areas associated with a correspondingly small angular range can be limited.

[0056] In a further advantageous embodiment of the method, at least one corrected received variable is determined on the basis of at least two validated received variables, in particular of the same type, in the same phase of the at least one received light beam and / or at least one corrected received variable is determined on the basis of at least two validated received variables, in particular of the same type, in different phases of the at least one received light beam.

[0057] Advantageously, at least one corrected received variable can be determined based on at least two validated received variables, in particular of the same type, in the same phase. In this way, the received variable can be extrapolated exclusively spatially.

[0058] Advantageously, additionally or alternatively, at least one corrected received variable can be determined based on at least two validated received variables, in particular of the same type with different phases. In this way, the received variable can be extrapolated over time.

[0059] If the procedure is further developed, at least one corrected received variable is determined on the basis of mean values ​​of at least two validated received variables, in particular of the same type and / or at least one corrected received variable is determined on the basis of mean values ​​of at least two validated received variables, in particular of the same type, in the same phase of the at least one received light beam and / or at least one corrected received variable is determined on the basis of mean values ​​of at least two validated received variables, in particular of the same type, in different phases of the at least one received light beam.

[0060] Advantageously, at least one corrected received variable can be determined based on mean values ​​of at least two validated received variables, in particular of the same type. This makes it easier to achieve an approximation.

[0061] Advantageously, alternatively or additionally, at least one corrected received variable can be determined based on mean values ​​of at least two validated received variables, in particular of the same type, in the same phase. This enables spatial extrapolation to realize the at least one corrected received variable.

[0062] Advantageously, validated reception variables, in particular of the same type, can be averaged from at least two reception areas in the same row of the reception area field in which the reception area for which the at least one corrected reception variable is to be determined is located. Alternatively or additionally, validated reception variables, in particular of the same type, can be averaged from at least two reception areas in the same column of the reception area field in which the reception area for which the at least one corrected reception variable is to be determined is located. Alternatively or additionally, validated reception variables, in particular of the same type, can be averaged from at least one reception area nearest and after the reception area for which the at least one corrected reception variable is to be determined.The alternatives in this paragraph, individually or in combination, have the advantage that for an oversaturated reception area for which no meaningful reception value can be determined directly, the validated reception values, in particular of the same type, of the non-oversaturated reception areas in the vicinity can be used.

[0063] Advantageously, alternatively or additionally, at least one corrected received variable can be determined based on mean values ​​of at least two validated received variables, in particular of the same type, in different phases. This enables temporal extrapolation for the realization of the at least one corrected received variable.

[0064] Advantageously, at least one corrected received variable can be determined based on mean values ​​of at least two validated received variables, in particular of the same type in the same phase, and at least two validated received variables, in particular of the same type in different phases. In this way, spatial extrapolation and temporal extrapolation can be combined.

[0065] In a further advantageous embodiment of the method, at least one corrected received variable can be modeled using at least one approximation function based on at least two validated received variables. In this way, the corrected received variables can be determined easily and quickly.

[0066] Advantageously, corrected received variables for a reception area can be modeled using validated received variables of the same type. This allows the received variables to be directly related to each other without further conversion.

[0067] Alternatively or additionally, corrected received variables for a reception area can advantageously be modeled using validated received variables for the same reception area. This eliminates the need for validated received variables for other reception areas within the reception area field.

[0068] Furthermore, the object is achieved according to the invention in the iToF LiDAR system in that the iToF LiDAR system has at least some means for carrying out the method according to the invention.

[0069] Advantageously, at least some of the means for implementing the method can be implemented in software. This allows the means to be easily integrated into the hardware, particularly existing hardware, of the LiDAR system.

[0070] Advantageously, at least some of the means for carrying out the method can be embodied as a software program. Advantageously, the method according to the invention can be a computer program.

[0071] Furthermore, the object is achieved according to the invention in the driver assistance system in that the driver assistance system has at least some means for carrying out the method according to the invention.

[0072] Advantageously, the at least one iToF LiDAR system of the driver assistance system, in particular of the driver assistance system according to the invention, can comprise at least some of the means for carrying out a method according to the invention. Since the at least one iToF LiDAR system is part of the driver assistance system, means of the at least one iToF LiDAR system are thus also part of the driver assistance system. This applies analogously to means of a vehicle that has at least one driver assistance system and / or at least one iToF LiDAR system.

[0073] Furthermore, the object is achieved according to the invention in the vehicle in that the vehicle has at least some means for carrying out the method according to the invention.

[0074] The vehicle has at least one iToF LiDAR system. The at least one iToF LiDAR system can be used to monitor the vehicle's surroundings. The iToF LiDAR system can be used to determine distances to detected target objects.

[0075] Advantageously, the vehicle can have at least one driver assistance system. With the at least one driver assistance system, the vehicle can be operated autonomously or semi-autonomously.

[0076] Advantageously, at least one iToF LiDAR system can be part of or connected to at least one driver assistance system. In this way, information obtained with the at least one iToF LiDAR system can be transmitted to a control unit of the at least one driver assistance system. With the at least one driver assistance system, information obtained by the at least one iToF LiDAR system can be used for autonomous or semi-autonomous operation of the vehicle.

[0077] Additionally or alternatively, at least some of the means for carrying out the method according to the invention can be implemented separately from the at least one iToF LiDAR system, in particular with a control unit of the vehicle and / or a control unit of the driver assistance system.

[0078] Furthermore, the features and advantages presented in connection with the method according to the invention, the iToF LiDAR system according to the invention, the driver assistance system according to the invention, and the vehicle according to the invention, and their respective advantageous embodiments, apply to one another accordingly and vice versa. The individual features and advantages can, of course, be combined with one another, whereby further advantageous effects can arise that go beyond the sum of the individual effects. Short description of the drawings

[0079] Further advantages, features, and details of the invention will become apparent from the following description, in which exemplary embodiments of the invention are explained in more detail with reference to the drawings. Those skilled in the art will expediently consider the features disclosed in the drawings, the description, and the claims in combination individually and combine them to form useful further combinations. The figures show schematically: Fig. 1 a front view of a vehicle with a driver assistance system having an iToF LiDAR system for detecting objects; Fig. 2 a functional representation of the driver assistance system with the LiDAR system from the Fig. 1; Fig. 3 a front view of a reception matrix of a receiving device of the iToF LiDAR system from the Fig. 1 and Fig. 2, wherein the reception matrix has a plurality of reception areas; Fig. 4 a signal strength-time diagram with four exemplary differential correlation signals DCS0 to DCS3, which are generated with respective phase shifts (or sampling intervals) of 90° from a received light signal of a reflected transmitted light signal of the iToF LiDAR system from the Fig. 1 and Fig. 2 and which are used to determine distances from objects; Fig. 5 is a distance image of a scene with multiple objects in grayscale, one of the objects being a partially retroreflective road sign; Fig. 6 an intensity profile through a line of the distance image from the Fig. 5; Fig. 7 to 10 each show a table with the respective signal waveform variables A0, A1, A2 and A3 for the scene from the Fig. 5 for a section of the reception matrix of the receiving device of the iToF LiDAR system from the Fig. 1 and Fig. 2, wherein a portion of the waveform quantities originate from supersaturated receive regions resulting from received light signals reflected from retroreflective object targets; Fig. 11 and Fig. 12 the tables with the respective signal waveform sizes A1 and A3 from the Fig. 8 and 10 respectively, wherein the signal waveform sizes of oversaturated reception areas are represented by respective corrected signal waveform sizes A 1,corr or A 3,corr are replaced; Fig. 13 to 16 each show a three-dimensional axis diagram with the signal waveform variables A0, A1, A2 and A3 from the Fig. 7 to 10 differential correlation quantities DCS0 to DCS3; Fig. 17 and Fig. 18 shows a three-dimensional axis diagram with the signal waveform variables A1 and A3 from the Fig. 11 and Fig. 12 differential correlation variables DCS1 and DCS3; Fig. 19 a signal strength-time diagram with two signal waveform variables A determined from measurements and validated 0,val and A 2,val and a corrected signal waveform quantity A 1,corr , which is obtained by modeling with an approximate function from the validated signal waveform variables A 0,val and A 2,val was determined, whereby the approximate function and a signal curve calculated from the validated signal curves A 0,val and A 2,val and the corrected signal waveform size A 1,corr determined signal curve of the received light signals is shown.

[0080] In the figures, identical components are provided with the same reference symbols. Embodiment(s) of the invention

[0081] In the Fig. 1 shows a vehicle 10, exemplified in the form of a passenger car, in a front view. The vehicle 10 has a driver assistance system 12. Fig. 2 shows a functional representation of the driver assistance system 12. With the help of the driver assistance system 12, the vehicle 10 can be operated autonomously or semi-autonomously.

[0082] The driver assistance system 12 has a control device 14 and a LiDAR system 16.

[0083] The LiDAR system 16 is configured as a Flash iToF LiDAR system 16. The LiDAR system 16 is arranged, for example, in the front bumper of the vehicle 10. With the LiDAR system 16, a surveillance area 18 in front of the vehicle 10 in the direction of travel can be monitored for objects 20. The LiDAR system 16 can also be arranged at a different location on the vehicle 10 and oriented differently. With the LiDAR system 16, object information, such as distance variables, direction variables, and speed variables, can be determined, which characterize the distances 22, directions, and speeds of objects 20 relative to the vehicle 10 or to the LiDAR system 16.

[0084] The objects 20 can be stationary or moving objects, for example other vehicles, persons, animals, plants, obstacles, road surface irregularities, for example potholes or stones, road markings, traffic signs, open spaces, for example parking spaces, precipitation or the like.

[0085] Each object 20 typically has multiple object targets 24. An object target 24 is a location on an object 20 at which transmitted light beams in the form of transmitted light signals 26, which are transmitted by the LiDAR system 16 into the surveillance area 18, can be reflected.

[0086] In the Fig. 2, an object 20 in the form of a street sign is indicated by a symbol. The area of ​​the street sign with the symbol has normal reflectivity with respect to the transmitted light signals 26. The object targets there are designated 24. For better clarity, only one of the object targets 24 is indicated by a cross. The area of ​​the street sign surrounding the symbol has a retroreflective coating. The object targets there are designated 24 for better differentiation. R Here, too, for the sake of clarity, only one of the object targets 24 is shown as an example. R indicated with a cross.

[0087] The LiDAR system 16 is connected to the control device 14 of the driver assistance system 22. In this way, control signals can be transmitted to the LiDAR system 16 and vice versa via the control device 14. Furthermore, information obtained by the LiDAR system 16 can be transmitted to the control device 14.

[0088] The LiDAR system 16 comprises, for example, a transmitting device 28, a receiving device 30 and a control and evaluation device 32.

[0089] The control and evaluation device 32 is, for example, an electronic control and evaluation device, for example, with one or more processors. The functions of the control and evaluation device 32 can be implemented centrally or decentrally using software and / or hardware. Parts of the functions of the control and evaluation device 32 can also be integrated into the transmitting device 28 and / or the receiving device 30 and / or the control device 14.

[0090] Electrical transmission signals can be generated using the control and evaluation device 32. The transmission device 28 can be controlled by the electrical transmission signals so that it transmits amplitude-modulated transmitted light signals 26 in the form of laser pulses into the monitored area 18.

[0091] The transmitting device 28 has, for example, a laser as its light source. The laser can generate transmitted light signals 26 in the form of laser pulses. Furthermore, the transmitting device 28 has an optical device with which the transmitted light signals 26 are expanded so that they can spread—similar to a flash light—over the entire surveillance area 18. In this way, the entire surveillance area 18 can be illuminated with each transmitted light signal 26. The transmitted light signals 26 can therefore also be referred to as "flash transmitted light beams." The LiDAR system 16 is therefore also referred to as a flash LiDAR system.

[0092] Transmitted light signals 26 reflected from an object 20 in the direction of the receiving device 30 can be received by the receiving device 30. For ease of differentiation, the transmitted light signals 26 reflected in the direction of the receiving device 30 are referred to as received light beams or received light signals 34, respectively.

[0093] The receiving device 30 can optionally have a received light signal deflection device. With the received light signal deflection device, the received light signals 34 can be Fig. 3 shown receiving matrix 36 of the receiving device 30.

[0094] The reception matrix 36 is implemented, for example, with an area sensor in the form of a CCD sensor. The reception matrix 36 has a plurality of reception areas 38. Each reception area 38 can be implemented, for example, by a group of pixels. The reception matrix 36 described here has, for example, 240 rows x and 320 columns y with reception areas 38. In the Fig. For the sake of clarity, only a section with 7 x 7 reception areas 38 is shown as an example in Figure 3.

[0095] Instead of a CCD sensor, a different type of area sensor, such as an active pixel sensor or the like, can also be used.

[0096] With means of the receiving device 30, which are not of further interest here, the portions of received light signals 34 which respectively impinge on the receiving areas 38 of the receiving matrix 36 can be converted into corresponding, for example, Fig. 4 designated signal waveform quantities A0, A1, A2 and A3, which are also generally referred to as signal waveform quantities A i The index i = [0,1,2,3]. The signal waveform quantities A0, A1, A2 and A3 are determined from differential correlation signals DCS0, DCS1, DCS2 and DCS3, which are also generally referred to as differential correlation signals DCS i The signal waveform variables A i and the differential correlation signals DCS i can be assigned to the respective reception areas 38. The differential correlation signals DCS i and the signal waveform variables A i are received quantities which are determined from the received light signals 34. For example, the differential correlation signals DCS i Receive variables of a first type in the sense of the inventors. The signal waveform variables A iare, for example, reception quantities of a second type within the meaning of the invention.

[0097] Each reception area 38 is, for example via suitable closure means, such as shutters, for the detection of received light signals 34 for defined recording time ranges TB0, TB1, TB2 and TB3, which are also generally referred to as recording time ranges TB i can be activated. For example, the reception areas 38 can each be activated in four recording time ranges TB0, TB1, TB2 and TB3 for the detection of received light signals 34. The recording time ranges TB i are time ranges of reception of received light signals 34.

[0098] Each recording time range TB i is defined by a start time and an integration duration. The time intervals between each two defined recording time periods TB i are smaller than the period t MODthe modulation period MP of the transmitted light signals 26 and thus also of the received light signals 34.

[0099] During a recording time range TB i Portions of received light signals 34 that reach the respective receiving area 38 can be converted into corresponding electrical received signals. From the received signals, the respective differential correlation signals DCS i and their signal waveform variables A i determine which respective signal sections of the received light signal 34 in the respective recording time ranges TB i characterize. The differential correlation signals DCS i and their signal waveform variables A i characterize the respective amount of light that is emitted during the recording time ranges TB i with the correspondingly activated reception area 38 of the reception matrix 36.

[0100] The received light signals 34, for example, are sinusoidal signal waveforms. Therefore, the differential correlation signals DCS i the following relationships: DCS0=I0+a sin(ωt+φ) DCS1=I0+a cos(ωt+φ) DCS2=I0−a sin(ωt+φ) DCS3=I0−a cos(ωt+φ)

[0101] I0 is a basic quantity which includes, for example, the intensity of the ambient light and noise.

[0102] From equations (Eq. 1) to (Eq. 4) we get I0=DCS0+DCS22=DCS1+DCS32 (2a)2=(DCS0−DCS2)2+(DCS1−DCS3)2

[0103] In general, the equations (Eq. 1) to (Eq. 4) can also be formulated as DCSi=Ai+I0 where A i the corresponding signal waveform size is A0=a sin(ωt+φ) A1=a cos(ωt+φ) A2=−a sin(ωt+φ)=−A0 A3=−a cos(ωt+φ)=−A1

[0104] Accordingly, a signal waveform quantity A i from the corresponding differential correlation signal DCS i be determined according to Ai=DCSi−I0

[0105] Once a has been determined, missing signal waveform variables A i Then the missing differential correlation signals DCS i can be determined from equation (Eq. 7). To determine a, the sinusoidal received light signal 34 must be derived from the existing differential correlation signals DCS i be reconstructed.

[0106] For example, each receiving area 38 can be individually activated and read. The shutter means can be implemented in software and / or hardware. For example, the receiving areas 38 can be controlled with corresponding periodic recording control signals in the form of trigger signals. The trigger signals can be initiated via the electrical transmission signals with which the laser of the transmitting device 28 is controlled, or together with them. In this way, the signal waveform variables A i and the transmitted light signals 26 are related to one another. For example, the electrical transmission signals can be triggered at a starting time ST. The reception areas 38 can be triggered with the correspondingly time-shifted trigger signals.

[0107] The receiving device 30 can optionally have optical elements with which received light signals 34 coming from the monitoring area 18 are imaged onto respective receiving areas 38 depending on the direction from which they come. Thus, from the position of the illuminated receiving areas 38 within the receiving matrix 36, the direction of an object target 24 or 24 R at which the transmitted light signal 26 is reflected.

[0108] In the Fig. 4 shows a modulation period MP of a received signal curve 40 of the received light signals 34 and the corresponding differential correlation signals DCS0, DCS1, DCS2, and DCS3 as an example for one of the reception areas 38 in a signal strength-time diagram. The signal strength axis is labeled "S" and the time axis is labeled "t."

[0109] The received signal curve 40 is offset in time from the starting time ST. The time offset, in the form of a phase shift φ, characterizes the flight time between the transmission of the transmitted light signal 26 and the reception of the corresponding received light signal 34.

[0110] From the phase shift φ, a distance value r can be determined as a distance quantity for the distance 22 of the reflecting object 20. The phase shift φ itself can also be used as a distance quantity for the distance 22. The time of flight is known to be proportional to the distance 22 of the object target 24 relative to the LiDAR system 16.

[0111] The received signal curve 40 can be approximated by, for example, four support points in the form of the four differential correlation signals DCS0, DCS1, DCS2 and DCS3 and / or in the form of the four signal waveform variables A0, A1, A2 and A3 determined from the differential correlation signals. Alternatively, the received signal curve 40 can also be approximated by more or fewer support points in the form of differential correlation signals DCS i or signal waveform variables A i be approximated.

[0112] The recording time ranges TB0, TB1, TB2, and TB3 are each started at the start time ST, for example, in relation to a reference event in the form of a trigger signal for the electrical transmission signal. For example, the modulation period MP of the transmitted light signal 26 extends over 360°. The recording time ranges TB0, TB1, TB2, and TB3 each start, for example, with a phase difference of 90° from each other, relative to the modulation period MP. The recording time ranges TB0, TB1, TB2, and TB3 therefore start in phases of 90°, 180°, 270°, and 0° relative to the start time ST.

[0113] The phase shift φ can be determined as a distance variable for characterizing the distance 22 of a detected object target 24, for example, from the differential correlation signals DCS0, DCS1, DCS2 and DCS3 for a respective reception area 38 according to the following equation: φ= π+atan2(DCS3−DCS1DCS2−DCS0)

[0114] Using the phase shift φ, the distance value r can be determined as the distance value of the detected object target 24 according to the following equation: r=c212πfATSφ

[0115] The distance value r can also be determined directly on the basis of the differential correlation signals DCS0, DCS1, DCS2 and DCS3 without prior calculation of the phase shift φ by combining equations (Eq. 13) and (Eq. 14).

[0116] In the Fig. Figure 5 shows a distance image of an exemplary scene in grayscale representation, which was captured with the LiDAR system 16.

[0117] In the distance image from the Fig. 5, the 320 columns y of the reception matrix 36 are indicated in the horizontal dimension. Each column y characterizes the horizontal direction from which the received light signals 34 received by the reception areas 38 of column y come, i.e. in which the corresponding object target 24 or 24 R In the vertical dimension of the distance image, the 240 rows x of the reception matrix 36 are specified. Each row x characterizes the vertical direction from which the received light signals 34 received by the reception areas 38 of row x come, i.e. in which the corresponding object target 24 or 24 R The distance values ​​r in centimeters for the detected object targets 24 and 24 R are defined in grayscale according to a grayscale shown next to the distance image.

[0118] Fig. 6 shows an intensity profile exemplified by the line x with the number 90 of the distance image from the Fig. 5.

[0119] In the scene from the Fig. 5 there are several objects 20, for example two walls and the street sign from the Fig. 2, in the surveillance area 18 of the LiDAR system 16.

[0120] The use of the LiDAR system 16 with an integration time which is long enough to detect even normally reflecting object targets 24, such as in the Fig. 5 to detect the walls leads to an oversaturation of reception areas 38, which are located at the retroreflective object targets 24 R reflected, strong received light signals 34.

[0121] For oversaturated reception areas 38, no meaningful differential correlation signals DCS ibe determined. Therefore, no distance determination is possible for the corresponding reception areas 38. Oversaturation of a reception area 38 is detected by the fact that the corresponding determined differential correlation signal lies above a saturation limit of the analog-to-digital converter of the corresponding reception area 38. For a 16-bit A / D converter, the saturation limit is, for example, 65,536.

[0122] According to the well-known Whittaker-Shannon interpolation formula, a missing sample signal x(t) for a continuous function can be reconstructed using available sample signals x(nT). The Whittaker-Shannon interpolation formula is: x(t)=∑n=−∞n=∞x(nT)sinc(t−nTT)

[0123] Here, T is the sampling period. For a signal with a modulation frequency f, the sampling period is T = 1 / 4f. For example, for a modulation frequency f of 10 MHz, the sampling period is T = 1 / 4f =10-7 / 4 = 25 ns.

[0124] A missing sampling signal x(t) in the signal curve of the received light signals 34 can therefore be determined using the Whittaker-Shannon interpolation formula from differential correlation signals DCS i can be determined using the following equation: x(t)=DCS0sic(tT)+DCS1sinc(t−TT)+DCS2sinc(t−2TT)+DCS3sinc(t−3TT)

[0125] Accordingly, the signal waveform of the received light signal 34 can be reconstructed if all four differential correlation signals DCS i are available and validated.

[0126] In order to ensure that even with integration times that lead to oversaturation of reception areas 38 by retroreflective object targets 24 R incoming received light signals 34 and therefore distance determinations for the retroreflective object targets 24 Rmake it impossible to determine the distance values, the LiDAR system 16 is operated as described below.

[0127] In the method for operating the LiDAR system 16, an amplitude-modulated flash transmitted light signal 26 is transmitted into the surveillance area 18 using the transmitting device 28.

[0128] The transmitted light signal 26 is, if present, directed to object targets 24 or 24 R reflected. In the Fig. In the scene shown in Figure 5, the transmission signal 20 is, for example, applied to the object targets 24 and 24 R of the street sign and the walls. A part of the transmitted light signal 26 is reflected at the object targets 24 and 24 R reflected and radiated as received light signals 34 in the direction of the receiving device 30.

[0129] With the receiving device 30, a measurement is carried out with a defined integration period, during which the received light signals 34 from the monitoring area 18 are received by the receiving areas 38 in the four recording time ranges TB0, TB1, TB2 and TB3.

[0130] The length of the integration period is selected such that even the weaker received light signals 34 from the normally reflective object targets 24, for example, the gas station symbol on the street sign and the walls, are sufficient to generate received signals distinguishable from noise in the reception areas 38. The received light signals 34 from the retroreflective object targets 24 R However, the reflected received light signals 34 are so strong that they lead to oversaturation for the corresponding reception areas 38 of the reception matrix 36. In the grayscale representation of the Fig. 5, the oversaturated reception areas 38 are shown in black. Fig. In the intensity profile of the distance image shown in Figure 6, no values ​​for the distance values ​​r are entered for the overdriven reception areas 38, since these cannot be determined due to the oversaturation.

[0131] For each of the reception areas 38, the respective received portion of the received light signals 34 is converted into the four corresponding differential correlation quantities DCS0, DCS1, DCS2, and DCS3 assigned to the respective reception area 38. The respective signal waveform quantities A0, A1, A2, and A3 are calculated from the differential correlation quantities DCS0, DCS1, DCS2, and DCS3 according to equation (Eq. 1).

[0132] In the Fig. 7 is a table with the signal waveform sizes A0 for the scene from the Fig. 5 shows a section of the reception matrix 36 for the reception areas 38 of the rows x with the numbers 55 to 60 and the columns y with the numbers 151 to 155. In the section from the Fig. 7 is part of the retroreflective object targets 24 R of the street sign. Fig. 13 shows a three-dimensional axis diagram with the signal waveform variables A0 from Fig. 7 belonging to the differential correlation quantities DCS0 in the corresponding section of the reception matrix 36.

[0133] Fig. 8 shows a table with the signal waveform variables A1 for the section of the reception matrix 36 from Fig. 7. Fig. 14 shows a three-dimensional axis diagram with the signal waveform variables A1 from Fig. 8 belonging to the differential correlation quantities DCS1 in the corresponding section of the reception matrix 36.

[0134] Fig. 9 shows a table with the signal waveform variables A2 for the section of the reception matrix 36 from Fig. 7. Fig. 15 shows a three-dimensional axis diagram with the signal waveform variables A2 from Fig. 9 belonging to the differential correlation quantities DCS2 in the corresponding section of the reception matrix 36.

[0135] Fig. 10 shows a table with the signal waveform variables A3 for the section of the reception matrix 36 from Fig. 7. Fig. 16 shows a three-dimensional axis diagram with the signal waveform variables A3 from Fig. 10 belonging to the differential correlation quantities DCS3 in the corresponding section of the reception matrix 36.

[0136] The reception areas 38 are each checked for oversaturation. A reception area 38 is identified as oversaturated if one of the differential correlation variables DCS0, DCS1, DCS2, and DCS3 assigned to this reception area 38 is above a specified saturation threshold. A reception area 38 is identified as non-oversaturated if all of the differential correlation variables DCS0, DCS1, DCS2, and DCS3 assigned to this reception area 38 are below the saturation threshold. Differential correlation variables DCS0, DCS1, DCS2, and DCS3 identified as non-oversaturated and their signal waveform variables A0, A1, A2, and A3 are validated and subsequently referred to as validated differential correlation variables DCS 0,val , DCS 1,val , DCS 2,val and DCS 3,val , or generally DCS i,val , and validated signal waveform quantities A 0,val , A 1,val , A 2,val and A 3,val , or generally A i,val, with the additional index “val”.

[0137] In the table of Fig. 8 and Fig. 10, the non-validated signal waveforms A1 and A3 are marked with dashed ovals. The non-validated signal waveforms A1 and A3 belong to oversaturated reception areas 38, which are hit by received light signals 34 that are reflected at retroreflective object targets 24 of the scene from the Fig. 5 are reflected. In the tables of the Fig. 7 and Fig. 9 are all signal waveform variables A 0,val or A 2val validated, since none of the reception areas 38 were oversaturated in the 0° and 270° phases.

[0138] For the sake of clarity, the tables show Fig. 7 to 10 only one of the validated signal waveform variables A i,val provided with a reference symbol and the respective coordinates (x, y) as an example.

[0139] In the following course of the procedure, the non-validated signal waveform variables A0, A1, A2 and A3, or generally A i , which belong, for example, to oversaturated reception areas 38, by corresponding corrected signal waveform variables A 0,corr , A 1,corr , A 2,corr and A 3,corr , or generally A i,corr , replaced. For this purpose, the corrected signal waveform variables A i,corr based on validated signal waveforms A i,val determined.

[0140] To determine the corrected signal waveform values ​​A i,corr Three correction methods are used, which for ease of differentiation are referred to as “spatial correction method”, “temporal correction method” and “modeling correction method”.

[0141] In the spatial correction method, to determine a corrected signal waveform quantity A i,corr the validated signal waveform variables A i,valthe nearest and next but one neighbors in the same column y and the nearest and next but one neighbors in the same row x of the signal waveform quantity A to be corrected i (x,y), which is located at the position (row x, column y). The values ​​of the validated signal waveform variables A i,val are subjected to the following mean calculation: Ai,corr(x,y)=mean([ai,z(x,y),ai,s(x,y)]) with ai,z(x,y)=Ai,val(x−1,y)∗Ai,val(x−1,y)Ai,val(x−2,y) and ai,s(x,y)=Ai,val(x,y−1)∗Ai,val(x,y−1)Ai,val(x,y−2)

[0142] Where the index “z” means that the term a i,z is assigned to a row. The index “s” means that the term a i,s is assigned to a column. "Mean" means that the term in the brackets is averaged.

[0143] For explanation purposes, the corrected signal waveform size A is used as an example below.3,corr (59,153) for the reception area 38 with the coordinates (59,153). This reception area 38 is oversaturated. The corresponding signal waveform A3 (59,153) shows Fig. 10 has a value of 63395. After subtracting the base value I0, this value is above the saturation limit for differential correlation signals DCS described above. Therefore, the signal waveform A3 (59,153) from the receiving area 38 with the coordinates (59,153) is not validated. The non-validated signal waveform A3 (59,153) cannot be used for distance determination. It must be replaced by the corresponding corrected signal waveform A 3,corr (59, 153), which is subsequently determined using the spatial correction method.

[0144] The nearest neighbors in the same column y and the nearest neighbors in the same row x of the signal waveform quantity A3 (59,153) to be replaced have, according to the table from Fig. 10 the values: A3,val(57,153)=−582 A3,val(58,153)=−1368 A3,val(59,151)=−38 A3,val(59,152)=−184

[0145] It results from equation (Eq.18) a3,z(59,153)=A3,val(58,153)∗A3,val(58,153)A3,val(57,153) a3,z(59,153)=−1368∗−1368−582=3215.5 and from equation (Eq.19) a3,s(59,153)=A3,val(59,152)∗A3,val(59,152)A3,val(59,151) from equation (Eq.17) the corrected signal waveform size is finally A3,corr(59,153)=mean([a3,s(59,153),a3,s(59,153)])=−2053,2

[0146] The signal waveform size A3 (59,153) of the reception area 38 with the coordinates (59,153) is corrected by the signal waveform size A 3,corr(59.153) is replaced with the value - 2053.2.

[0147] All other non-validated signal waveforms A i of the other reception areas 38 are correspondingly corrected by the respective signal waveform variables A i,corr replaced.

[0148] The temporal correction method is described below. The temporal correction method utilizes a change in the intensity of the corresponding received light signal 34 corresponding to the temporal signal profile between the phases, namely 0°, 90°, 180°, and 270°, in which the differential correction signals DCS are determined and to which the signal profile variables A belong. A change, in particular an increase or decrease, in a signal profile variable A i in a phase with the index i is associated with a change, in particular an increase or decrease, of at least one other signal waveform variable A jin another phase with the index j. These relationships can be used to replace signal waveform variables A that result, for example, from oversaturated reception areas 38.

[0149] In the temporal correction method, the corrected signal waveform size A i,corr the validated signal waveform variables A i,val the nearest and next but one neighbors in the same row x and the nearest and next but one neighbors in the same column y of the same phase i of the signal waveform quantity A to be corrected i,val (x,y), which is located at the position (row x, column y). Furthermore, the validated signal waveform variables A j,val the nearest and next but one neighbors in the same row x and the nearest and next but one neighbors in the same column y of a different phase j of the signal waveform quantity A to be corrected i,val(x,y). The values ​​of the respective validated signal waveform variables A i,val subjected to the following mean calculation: Ai,corr(x,y)=mean([Az,As]) Ai,corr(x,y)=mean([Aj,val(x,y)∗az,Aj,val(x,y)∗as])

[0150] Where “az” is the ratio mean of the neighboring rows x az=mean([z10,z11]) with z10=Ai,val(x−2,y)Aj,val(x−2,y) and with z11=Ai,val(x−1,y)Aj,val(x−1,y) and where “as” is the ratio mean of the adjacent columns z as=mean([s20,s21]) with s20=Ai,val(x,y−2)Aj,val(x,y−2) and with s21=Ai,val(x,y−1)Aj,val(x,y−1)

[0151] For explanation purposes, the corrected signal waveform size A is used as an example below. 1,corr(57,155) for the receiver with the coordinates (57,155). This reception area 38 is oversaturated. The corresponding signal waveform A1(57,155) shows Fig. 8 has a value of 63420.5. After subtracting the base value I0, this value is above the saturation limit for differential correlation signals DCS described above. The signal waveform A1(57,155) from the reception area 38 with the coordinates (57,155) is therefore not validated. The non-validated signal waveform A1(57,155) cannot be used for distance determination. It must be replaced by the corresponding corrected signal waveform A 1,corr (57,155), which is determined below using the temporal correction method.

[0152] The nearest and next but one neighbors in the same row x and the next but one neighbors in the same column y of the signal waveform quantity A1(57,155) to be replaced in the same phase with the index 1, namely 180°, have, according to the table from Fig. 8 the values: A1,val(57,154)=1392 A1,val(57,153)=490 A1,val(56,155)=112.5 A1,val(55,155)=35.5

[0153] The nearest and next but one neighbors in the same row x and the next but one neighbors in the same column y of the signal waveform quantity A1(57,155) to be replaced in the other phase with the index 0, namely 90°, have, according to the table from Fig. 7 the values: A0,val(57,154)=−64 A0,val(57,153)=−14 A0,val(56,155)=−0.5 A0,val(55,155)=4.5

[0154] From equations (Eq. 22) to (Eq. 24) we get A0(57,155)∗az=A0(57,155)∗mean(A1(57,154)A0(57,154),A1(57,153)A0(57,153)) so A0(57.155)∗az=A0(57.155)∗mean(1392−64.490−14)=208.5∗−28.375=5916.2

[0155] From equations (Eq. 25) to (Eq. 27) we get A0(57,155)∗as=A0(57,155)∗mean(A1(56,155)A0(56,155),A1(55,155)A0(55,155)) so A0(57.155)∗az=A0(57.155)∗mean(112.5−0.535.54.5)=−208.5∗−108.5556=22634 Thus, from equation (Eq. 21) the corrected signal waveform quantity A 1,corr (57, 155) A1,corr(57,155)=mean([A0(57,155)∗az,A0(57,155)∗as]) A1,corr(57,155)=mean([5916,2;22634])=14275

[0156] In the table in Fig. 11 are the non-validated signal waveform quantities A1(x,y) for the phase with index 1 from the table in Fig. 8 each by the corrected signal waveform variables A determined according to the time correction method 1,corr(x,y). Accordingly, the table in the Fig. 12 the non-validated signal waveform values ​​A3(x,y) for the phase with index 3 from the table Fig. 10 each by the corrected signal waveform variables A determined according to the time correction method 3,corr (x,y) is replaced.

[0157] The modeling correction method is explained in more detail below. In the modeling correction method, the signal curve of the received light signals 34 is calculated from existing validated signal curve variables A i,val modeled. Subsequently, corrected signal waveform variables A i,corr for missing, i.e. non-validated signal waveform variables, are determined from the modulated signal waveform. For this purpose, for example, an approximation function can be used using the available validated signal waveform variables A i,val and the corrected signal waveform size A i,corr can be determined from the approximation function 42.

[0158] Advantageously, the degree of the approximation function can be lower than the degree of the function of the signal curve of the received light signals 34. In the described embodiment, the signal curve of the received light signals 34 corresponds, for example, to a sine function. A parabolic function can be selected for the approximation function.

[0159] In the Fig. Figure 19 shows an example of the ideal signal curve for the received light signals 34 in the form of a sine function shown in dashed lines. Due to the oversaturation of the corresponding reception areas 32, the signal curve variable A1 for the 180° phase and the signal curve variable A3 for the 0° phase cannot be used. The approximate function in the form of a parabolic function is represented by the validated signal curve variable A 0,val for the phase 90° and the validated signal waveform size A 2,val for the phase 270°. In the Fig.19, the approximation function 42 is shown as a solid line. The corrected signal waveform quantity A 1,corr is determined for the phase 180° from the approximation function 42.

[0160] From the signal waveform quantities A corrected according to the correction methods described above, namely the spatial correction method, the temporal correction method and the modeling correction method 0,corr , A 1,corr , A 2,corr and A 3,corr , or generally A i,corr , corrected differential correlation values ​​DCS 0,corr , DCS 1,corr , DCS 2,corr and DCS 3,corr , or generally DCSi,corr, is determined. For this purpose, the respective corrected signal waveform variables A i,corr the base quantity I0 explained above in connection with equations (Eq. 1) to (Eq. 12) is added. The corrected signal waveform quantities A i,corrfrom one of the three correction methods or from a combination of corrected signal waveform variables A i,corr of two or three of the correction methods. The non-validated differential correlation variables DCS i , which belong, for example, to oversaturated reception areas 38, are replaced by the corresponding corrected differential correlation quantities DCSi,corr.

[0161] From the validated differential correlation values ​​DCS i,val and the corrected differential correlation values ​​DCS i,corr The phase shift φ and the distance value r are determined from equations (Eq. 13) and (Eq. 14). The corrected differential correlation values ​​DCS i,corr from one of the three correction methods. The corrected differential correlation values ​​DCS determined using two or three of the correction methods i,corrcan also be combined with each other, for example averaged or the like. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 2020 / 0072946 A1

[0005]

Claims

[1] Method for operating an iToF LiDAR system (16), in particular an iToF LiDAR system (16) of a vehicle (10), in which at least one transmitted light beam (26) is transmitted by at least one transmitting device (28) of the LiDAR system (16) into at least one monitoring area (18), at least a part of at least one received light beam (34) coming from the at least one monitoring area (18), which is received by the at least one at at least one object target (24, 24 R ) reflected transmitted light beam (26), is received by at least one receiving area (38) of a receiving device (30) of the LiDAR system (16), at least one reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) is determined from the at least one part of the at least one received light beam (34) received by the at least one reception area (38), which characterizes a signal intensity of the received part of the received light beam (34) in a time range (TB0, TB1, TB2, TB3) of the reception, wherein the at least one reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) is assigned to the at least one reception area (38) that is hit by the at least one part of the at least one received light beam (34), on the basis of at least one reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) at least one distance variable (r, φ) is determined, which represents a distance (22) of an object target (24, 24 R ), at which the at least one transmitted light beam (26) was reflected, to the LiDAR system (16), characterized by , that at least two reception quantities (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) of the same type are determined from at least a part of the at least one received reception light beam (34), at least two of the determined reception variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) of the same type are validated, based on at least two validated reception variables (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ; A 0,val , A 1,val , A 2,val , A 3,val ) of the same type at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) is determined on the basis of at least one validated reception quantity (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ; A 0,val , A 1,val , A 2,val , A 3,val ) and at least one corrected received variable (DCS 0,corr , DCS1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) of the same type at least one distance value (r, φ) is determined. [2] Method according to claim 1, characterized by , that the received light beam (34) is converted into at least one received variable (DCS0, DCS1, DCS2, DCS3) of a first type, in particular into at least one differential correlation signal, and a corresponding received variable (A0, A1, A2, A3) of a second type, in particular a signal curve variable, is determined from the at least one received variable (DCS0, DCS1, DCS2, DCS3) of the first type, in particular the at least one differential correlation signal and / or from at least one corrected received quantity (A 0,corr , A 1,corr , A 2,corr , A 3,corr) of the second type, in particular from at least one corrected signal waveform variable, at least one corrected reception variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ) of the first type, in particular at least one corrected differential correlation signal, is determined and from at least one validated received variable (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ) of the first type, in particular from at least one validated differential correlation signal, which forms the basis for a validated received quantity (A 0,val , A 1,val , A 2,val , A 3,val ) of the second type, in particular for a validated signal waveform variable, and at least one corrected reception variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ) of the first type, in particular a corrected differential correlation signal, at least one distance value (r, φ) is determined. [3] Method according to claim 1 or 2, characterized by that, in order to validate a reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3), it is checked whether the reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) shows signs of oversaturation of the correspondingly assigned reception area (38), and, if the reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) shows signs of oversaturation, the reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) is not validated, otherwise this reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) is used as a validated reception variable (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ; A 0,val , A 1,val , A 2,val , A 3,val ) is used. [4] Method according to claim 3, characterized bythat if a received variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) is not validated, this non-validated received variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) is replaced by a corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) of the same type, which is based on at least two validated reception variables (DCS 0,val , DCS 1,val , DCS 2,val , DCS3,val; A 0,val , A 1,val , A 2,val , A 3,val ), in particular two validated reception variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) of the same type. [5] Method according to one of the preceding claims, characterized by that the at least one received light beam (34) is received by a plurality of reception areas (38) of a reception area field (36), in particular by a plurality of reception areas (38) of a reception matrix, of the receiving device (30) and / or for at least two reception areas (38) hit, at least one reception variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) of the same type is determined from the respectively received portion of the at least one reception light beam (34) and / or at least two received variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) of the same type are determined in different phases of the at least one received light beam (34) and / or for at least one reception area (38) hit, at least two reception variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) of the same type are determined in different phases of the at least one reception light beam (34). [6] Method according to one of the preceding claims, characterized by that at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr) for a reception area (38) on the basis of at least two validated reception variables (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ; A 0,val , A 1,val , A 2,val , A 3,val ) of the same type from at least one other reception area (38), in particular at least one reception area (38) in a defined neighborhood of the reception area (38) with the at least one corrected reception variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ), is determined. [7] Method according to claim 6, characterized by , that at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) based on at least two validated reception variables (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val; A 0,val , A 1,val , A 2,val , A 3,val ) in particular of the same type in the same phase of the at least one received light beam (34) and / or at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) based on at least two validated reception variables (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ; A 0,val , A 1,val , A 2,val , A 3,val ), in particular of the same type, in different phases of the at least one received light beam (34). [8] Method according to claim 6 or 7, characterized by that at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr) based on mean values ​​of at least two validated reception quantities (DCS 0,val , DCS 1,val , DCS 2,val , DCS3,val; A 0,val , A 1,val , A 2,val , A 3,val ) in particular of the same type and / or at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) based on mean values ​​of at least two validated reception quantities (DCS 0,val , DCS 1,val , DCS 2,val , DCS3,val; A 0,val , A 1,val , A 2,val , A 3,val ) in particular of the same type in the same phase of the at least one received light beam (34) and / or at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr) based on mean values ​​of at least two validated reception quantities (DCS 0,val , DCS 1,val , DCS 2,val , DCS3,val; A 0,val , A 1,val , A 2,val , A 3,val ), in particular of the same type, in different phases of the at least one received light beam (34). [9] Method according to one of the preceding claims, characterized by that at least one corrected received variable (DCS 0,corr , DCS 1,corr , DCS 2,corr , DCS 3,corr ; A 0,corr , A 1,corr , A 2,corr , A 3,corr ) by means of at least one approximation function (42) based on at least two validated reception variables (DCS 0,val , DCS 1,val , DCS 2,val , DCS 3,val ; A 0,val , A 1,val , A 2,val , A 3,val ) is modeled. [10] iToF LiDAR system (16), in particular an iToF LiDAR system (16) of a vehicle (10), which has at least one transmitting device (28) with which transmitted light beams (26) can be sent into at least one monitoring area (18), at least one receiving device (30) which has at least one receiving area (38) with which received light beams (34) coming from the at least one monitoring area (18) and which are detected by at least one object target (24, 24 R ) reflected transmitted light beams (26), at least one means for determining received quantities (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) from parts of received light beams (34) received with a receiving area (38), at least one means for assigning reception variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) to reception areas (38), and at least one means for determining at least one distance value (r, φ) which represents a distance (22) of an object target (24, 24 R), at which at least one transmitted light beam (26) was reflected, to the LiDAR system (16), based on at least one received variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3), characterized by , that the iToF LiDAR system (16) comprises at least some of the means for carrying out the method according to one of claims 1 to 9. [11] Driver assistance system (12) with at least one iToF LiDAR system (16), wherein the at least one iToF LiDAR system (16) has at least one transmitting device (28) with which transmitted light beams (26) can be sent into at least one monitoring area (18), at least one receiving device (30) which has at least one receiving area (38) with which received light beams (34) coming from the at least one monitoring area (18) and which are detected by at least one object target (24, 24 R) reflected transmitted light beams (26), at least one means for determining received quantities (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) from parts of received light beams (34) received with a receiving area (38), at least one means for assigning reception variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) to reception areas (38), and at least one means for determining at least one distance value (r, φ) which represents a distance (22) of an object target (24, 24 R ), at which at least one transmitted light beam (26) was reflected, to the LiDAR system (16), based on at least one received variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3), characterized by , that the driver assistance system (12) comprises at least some of the means for carrying out the method according to one of claims 1 to 9. [12] Vehicle (10) with at least one iToF LiDAR system (16), wherein the at least one iToF LiDAR system (16) comprises at least one transmitting device (28) with which transmitted light beams (26) can be sent into at least one monitoring area (18), at least one receiving device (30) which has at least one receiving area (38) with which received light beams (34) coming from the at least one monitoring area (18) and which are detected by at least one object target (24, 24 R ) reflected transmitted light beams (26), at least one means for determining received quantities (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) from parts of received light beams (34) received with a receiving area (38), at least one means for assigning reception variables (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3) to reception areas (38), and at least one means for determining at least one distance value (r, φ) which represents a distance (22) of an object target (24, 24 R ), at which at least one transmitted light beam (26) was reflected, to the LiDAR system (16), based on at least one received variable (DCS0, DCS1, DCS2, DCS3; A0, A1, A2, A3), characterized by , that the vehicle (10) comprises at least some means for carrying out the method according to one of claims 1 to 9.

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