SYSTEM AND METHOD FOR ADAPTIVE HORIZON-BASED LOCALIZATION OF VEHICLES

Abstract

The present disclosure provides a system (106) and a method for adaptive horizon-based vehicle localization. The system (106) receives a high-resolution (HD) map associated with the position of a vehicle on a road segment. The system (106) extracts one or more inputs from the HD map to define a plurality of variable sampling points (VSPs) on the road segment, the one or more inputs being based on the variation of road scenarios along the road segment. The system (106) defines one or more carpet widths of a specific dimension associated with the vehicle based on the plurality of VSPs. The system (106) concatenates the one or more carpet widths to generate the adaptive horizon road carpet associated with the vehicle.

Description

TECHNICAL AREA

[0001] This disclosure relates generally to the field of advanced driver assistance systems (ADAS). In particular, this disclosure relates to a system and a method for adaptive horizon-based localization of vehicles. BACKGROUND

[0002] The creation of horizon carpets is necessary for calculating the precise position of a vehicle on a road. When creating a horizon carpet, sparse points of the high-resolution (HD) map geometry are sampled at small intervals to enable accurate vehicle localization. Route release also uses horizon carpets to provide drivable road segments. If no sampling is performed, the accuracy of longitudinal localization decreases drastically. Oversampling overloads the localization process with a massive increase in runtime, leading to frequent buffer overload, which in turn results in smaller carpets for the map holder modules. An excessively large carpet leads to smaller road horizons for the behavior planner and lane planner, resulting in system shutdown and inaccurate route planning.Furthermore, the creation and updating of maps generates several geometry points that require extensive post-processing.

[0003] Patent US9383212B2 discloses a system and a method for generating a vehicle position approximation for use by a driver assistance application. The system performs a map matching analysis to determine a point closest to a vehicle position on a polyline representation of a road derived from Bézier curves stored in a geographic database. The system further identifies a Bézier curve in the geographic database corresponding to the point on the polyline closest to the vehicle position. The system projects the point closest to the vehicle position on the polyline onto the identified Bézier curve to obtain a vehicle position approximation. The system provides this vehicle position approximation to at least one driver assistance application.

[0004] Patent specification US11615563B2 discloses a system for navigating a host vehicle, wherein the system accesses a map representing a road segment on which the host vehicle is traveling or is expected to travel. The map contains splines representing road features associated with the road segment. Furthermore, the system locates the host vehicle relative to a drivable path for the host vehicle represented by the splines. The system determines a set of points associated with the splines based on the location of the host vehicle relative to the drivable path. The system also generates a navigation information package containing information associated with the splines and a specific set of points relative to the splines.

[0005] Conventional systems and methods determine the vehicle position based on the rate of change of the gradient, the curvature of the geometry used, and various curve types (Bézier curves). The geometry is based on the definition of the Bézier curve, where smaller samples are implemented instead of segments with larger curvature values.

[0006] Therefore, there is a great need for an improved system and method with optimal scanning for vehicle localization. OBJECTS OF THE PRESENT DECISION

[0007] A general objective of the present disclosure is to provide a system and method for adaptive horizon-based vehicle localization, which includes the implementation of an adaptive carpet as well as variable localization to provide a carpet of adequate length for all map holder modules with localization accuracy.

[0008] Another objective of the present disclosure is to provide a system that receives a high-resolution (HD) map associated with a vehicle's position on a road segment and defines multiple variable sampling points (VSPs) on the road segment based on variations of road scenarios along the road segment.

[0009] Another purpose of the present disclosure is to provide a system that defines several carpet widths of a specific dimension for the vehicle based on the VSP.

[0010] Another purpose of the present disclosure is to provide a system that links the one or more carpet widths together to create the adaptable horizon road carpet assigned to the vehicle. SUMMARY

[0011] Aspects of this disclosure relate generally to the field of advanced driver assistance systems (ADAS). In particular, this disclosure relates to a system and a method for adaptive horizon-based localization of vehicles.

[0012] In one aspect, the present disclosure relates to a system for generating an adaptive horizon road carpet. The system comprises a processor operationally coupled to a memory, the memory storing instructions that cause the processor to receive a high-resolution (HD) map associated with the position of a vehicle on a road segment. The processor extracts one or more inputs from the HD map to define a plurality of variable sampling points (VSPs) on the road segment, the one or more inputs being based on the variation of road scenarios along the road segment. The processor defines one or more carpet widths of a specific dimension, associated with the vehicle, based on the plurality of VSPs. The processor concatenates the one or more carpet widths to generate the adaptive horizon road carpet associated with the vehicle.

[0013] In one embodiment, the one or more inputs may include speed information, one or more lane attributes associated with the road segment, one or more landmarks associated with the road segment, and lane geometry information associated with the road segment.

[0014] In one embodiment, the processor can be configured to determine a corresponding first velocity associated with the road segment in response to the detection of one or more road events. The processor can be configured to define multiple VSPs with an offset on the road segment based on the one or more landmarks and the corresponding first velocity. The processor can be configured to determine a corresponding second velocity associated with the road segment in response to the identification of one or more offsets for a change in the road scenario. The processor can be configured to define a plurality of VSPs with the offset on the road segment based on the one or more landmarks and the corresponding second velocity.

[0015] In one embodiment, the processor can be configured to define the one or more carpet widths of a given dimension by calculating a parabolic representation of the plurality of VSPs associated with the road segment. Alternatively, the processor can be configured to define the one or more carpet widths of a given dimension by calculating a width associated with the one or more carpet widths based on an inverse parabolic representation of the plurality of VSPs. The width can be based on a distance between at least two VSPs, a normalized vehicle speed value for the road segment, and a variance of one or more vehicle position coordinates on the road segment.The processor for defining one or more carpet widths of a given dimension can be configured to define one or more anchor points associated with the width of the one or more carpet widths. The processor for defining one or more carpet widths of a given dimension can be configured to combine the one or more anchor points, the lane geometry information, and the one or more reference points to define the one or more carpet widths with the width.

[0016] In one embodiment, the processor for calculating the parabolic representation of the multiple VSPs can be configured to calculate a parabolic curve associated with the at least two VSPs, based on the distance between the at least two VSPs, the normalized speed value of the vehicle for the road segment, and a non-normalized thickness of the road segment. The processor for calculating the parabolic representation of one or more VSPs can be configured to determine an intersection point of one or more straight lines between the at least two VSPs and the parabolic curve in order to determine the one or more carpet widths in front of the vehicle.

[0017] In one aspect, the present disclosure relates to a method for generating an adaptive horizon road carpet. The method comprises the reception of an HD map associated with a vehicle's position on a road segment by a processor connected to a system. The method comprises the processor extracting one or more inputs from the HD map to define a plurality of VSPs on the road segment, the one or more inputs being based on the variation of road scenarios along the road segment. The method comprises the processor defining one or more carpet widths of a specific dimension, associated with the vehicle, based on the plurality of VSPs. The method comprises the processor concatenating the one or more carpet widths to generate the adaptive horizon road carpet associated with the vehicle.

[0018] In one embodiment, the one or more inputs may include speed information from the HD map, one or more lane attributes associated with the road segment, one or more landmarks associated with the road segment, and lane geometry information associated with the road segment.

[0019] In one embodiment, the method may include, in response to the detection of one or more road events associated with the road segment, the processor determining a corresponding first velocity associated with the road segment. The method may involve the processor defining the plurality of VSPs with an offset on the road segment based on the one or more landmarks and the corresponding first velocity. The method may also include, in response to the identification of one or more offsets to change the road scenario, the processor determining a corresponding second velocity associated with the road segment. This method may involve the processor defining the plurality of VSPs with an offset on the road segment based on the one or more landmarks and the corresponding second velocity.

[0020] In one embodiment, the method may involve the processor defining the one or more carpet widths of the specified dimension by calculating a parabolic representation of the multiple VSPs associated with the road segment to determine the one or more carpet widths. Alternatively, the method may involve the processor defining the one or more carpet widths of the specified dimension by calculating a width associated with the one or more carpet widths based on an inverse parabolic representation of the plurality of VSPs. The width may be based on a distance between at least two VSPs, a normalized vehicle speed value for the road segment, and a variance of one or more position coordinates of the vehicle on the road segment.The method can involve the processor defining one or more carpet widths of a given dimension by defining one or more anchor points associated with the width of those one or more carpet widths. Alternatively, the method can involve the processor defining the one or more carpet widths of a given dimension by combining the one or more anchor points, the lane geometry information, and the one or more reference points to define the one or more carpet widths with the given width.

[0021] In one embodiment, the method may involve the processor calculating the parabolic representation of the multiple VSPs by calculating a parabolic curve associated with the at least two VSPs based on the distance between at least two VSPs, a normalized vehicle speed value for the road segment, and a non-normalized thickness of the road segment. The method may also involve the processor calculating the parabolic representation of the multiple VSPs by determining an intersection point of one or more straight lines between the at least two VSPs and the parabolic curve to determine the one or more carpet widths in front of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings serve to further understand the present disclosure and are an integral part of this description. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. Fig. Figure 1 shows an example of an architecture diagram 100 of a proposed system 106 according to an embodiment of the present disclosure. Fig. Figure 2 shows an exemplary block diagram 200 of the proposed system 106 in accordance with an embodiment of the present disclosure. Fig. Figure 3 shows an exemplary flowchart 300 of an adaptable horizon mechanism implemented by the proposed system 106 in accordance with an embodiment of the present disclosure. Fig. Figure 4 shows an exemplary flowchart 400 of the calculation of variable sampling points by the proposed system 106 in accordance with an embodiment of the present disclosure. Fig. Figure 5 shows an example representation of 500 variable sampling points on a road segment according to an embodiment of the present disclosure. Fig. Figure 6 shows an exemplary flowchart 600 of a parabolic presentation concept implemented by the proposed system 106 in accordance with an embodiment of the present disclosure. Fig. Figure 7 shows an example of a parabolic representation concept 700, which is implemented by the proposed system 106 in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] A detailed description of the embodiments of the disclosure illustrated in the accompanying drawings follows. The embodiments are described in sufficient detail to clearly convey the disclosure. However, the intention is not to limit foreseeable variations of embodiments through this level of detail; rather, the aim is to cover all modifications, equivalents, and alternatives that fall within the spirit and scope of the present disclosure as defined by the accompanying claims.

[0024] Embodiments of the present disclosure generally relate to the field of advanced driver assistance systems (ADAS). In particular, the present disclosure relates to a system and a method for adaptive horizon-based localization of vehicles.

[0025] Various embodiments of the present disclosure are described with reference to the Fig. 1-7 explained in more detail. 1-7.

[0026] Fig. Figure 1 shows an example of an architecture diagram 100 of a proposed system 106 according to an embodiment of the present disclosure.

[0027] As in Fig. As shown in Figure 1, in one embodiment the system 102 can be connected to one or more vehicles (102-1, 102-2...102-N) via a network 104. A person skilled in the art will understand that the one or more vehicles (102-1, 102-2...102-N) can also be referred to as the vehicles 102 or the vehicle 102 throughout the entire disclosure.

[0028] In one embodiment, the network 104 may, by way of example but not limiting, comprise at least one section of one or more networks with one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, one or more messages, packets, signals, waves, voltage or current levels, etc. The network 104 may also comprise one or more wireless networks, wired networks, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad-hoc network, an infrastructure network, a public switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or a combination thereof, to name only a few.

[0029] In one embodiment, the system 106 can receive a high-resolution (HD) map of the vehicle 102 and determine the vehicle 102's position on a specific segment of a highway. The system 106 can analyze the HD map to determine a multitude of variable sampling points (VSPs) based on the vehicle 102's position or the vehicle 102's position as it moves due to a change in lane topology. The system 106 can record changes associated with the multitude of VSPs based on the current highway speed and the vehicle 102's speed, resulting in dynamic horizon creation to determine the vehicle 102's position on any given highway.

[0030] Fig. Figure 2 shows an exemplary block diagram 200 of the proposed system 106 in accordance with an embodiment of the present disclosure.

[0031] As in Fig. As shown in Figure 2, the System 106 can include one or more processors 202, which may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuits, and / or any devices that process data based on operating instructions. Among other capabilities, the one or more processor(s) 202 can be configured to retrieve and execute computer-readable instructions stored in a memory 204 of the System 106. The memory 204 can be configured to store one or more computer-readable instructions or routines in a non-volatile, computer-readable storage medium that can be retrieved and executed to create or exchange data packets over a network service. The memory 204 can include any non-volatile device, such as...volatile memory such as random access memory (RAM) or non-volatile memory such as erasable programmable read-only memory (EPROM), flash memory, and the like.

[0032] In one embodiment, the system 106 may comprise one or more interfaces 206. The interface(s) 206 may comprise a variety of interfaces, such as interfaces for data input and output (I / O) devices, storage devices, and the like. The interface(s) 206 may also provide a communication path for one or more components of the system 106. Examples of such components include, among others, the processing machine(s) 208 and a database 210, wherein the processing machine(s) 208 may include, among others, a data input machine 212 and one or more other machines 214. In one embodiment, the other machine(s) 214 may include, but are not limited to, a data management machine, an input / output machine, and a notification machine.

[0033] In one embodiment, the processing machine(s) 208 can be implemented as a combination of hardware and programming (e.g., programmable instructions) to implement one or more functionalities of the processing machine(s) 208. In the examples described here, such combinations of hardware and programming can be implemented in various ways. For example, the programming for the processing machine(s) 208 can consist of processor-executable instructions stored on a non-volatile, machine-readable storage medium, and the hardware for the processing machine(s) 208 can include a processing resource (e.g., one or more processors) to execute such instructions. In the present examples, the machine-readable storage medium can store instructions which, when executed by the processing resource, implement the processing machine(s) 208.In such examples, System 106 may comprise the machine-readable storage medium that stores the instructions and the processing resource for executing the instructions, or the machine-readable storage medium may be separate but accessible to System 106 and the processing resource. In other examples, the processing machine(s) 208 may be implemented by electronic circuits.

[0034] In one embodiment, the processor 202 can receive an HD map associated with the position of a vehicle (e.g., 102) on a road segment via the data input engine 212. The processor 202 can store the HD map in the database 210. The processor 202 can extract one or more inputs from the HD map to define a variety of VSPs on the road segment. In one embodiment, the one or more inputs can be based on, but are not limited to, variations of road scenarios along the road segment. The one or more inputs can include, among other things, speed information, one or more lane attributes associated with the road segment, one or more landmarks associated with the road segment, and lane geometry information associated with the road segment.

[0035] In one embodiment, the processor 202 can determine a corresponding first speed associated with the road segment in response to the detection of one or more road events. The processor 202 can define the plurality of VSPs with an offset on the road segment based on the one or more landmarks and the corresponding first speed. In another embodiment, the processor 202 can determine a corresponding second speed associated with the road segment in response to the identification of one or more offsets for a change in the road scenario. The processor 202 can define the plurality of VSPs with the offset on the road segment based on the one or more landmarks and the corresponding second speed.

[0036] In one embodiment, the processor 202 can define one or more carpet widths of a specific dimension associated with the vehicle 102 based on the plurality of VSPs. In another embodiment, to define the one or more carpet widths of the specific dimension, the processor 202 can compute a parabolic representation of the one or more VSPs associated with the road segment. The processor 202 can compute a width associated with the one or more carpet widths based on an inverse parabolic representation of the plurality of VSPs. The width can be based on a distance between at least two VSPs, a normalized speed value of the vehicle 102 for the road segment, and a variance of a Global Positioning System (GPS) value of the vehicle 102 on the road segment.The Processor 202 can define one or more anchor points associated with the width of one or more carpet widths. The Processor 202 can combine the one or more anchor points, the lane geometry information, and the one or more reference points to define the one or more carpet widths with the width.

[0037] In one embodiment, the processor 202 can calculate a parabolic curve associated with the at least two VSPs to calculate the parabolic representation of the multiple VSPs, based on the distance between the at least two VSPs, a normalized speed value of the vehicle 102 for the road segment, and a non-normalized thickness of the road segment. The processor 202 can determine an intersection point of one or more straight lines between the at least two VSPs and the parabolic curve to determine the one or more carpet widths in front of the vehicle 102. In one embodiment, the processor 202 can concatenate the one or more carpet widths to generate the adaptive horizon road carpet associated with the vehicle 102.

[0038] Fig. Figure 3 shows an exemplary flowchart 300 of an adaptable horizon mechanism implemented by the proposed system 106 in accordance with an embodiment of the present disclosure. As in Fig. As shown in Figure 3, the flowchart 300 can include the following steps.

[0039] In step 302, the system 106 can receive the HD map based on the vehicle's position (e.g., 102). In step 304, the system 106 can parse the HD map. In step 306, the system 106 can extract one or more lane attributes associated with the road segment and one or more landmarks associated with the road segment from the HD map.

[0040] In step 308, the system can extract speed information from the HD map. In step 310, the system can extract lane geometry information associated with the road segment from the HD map. In step 312, the system can calculate a multitude of VSPs on the road segment and define one or more carpet widths of a specific dimension, assigned to the vehicle 102, based on the multitude of VSPs.

[0041] In step 314, the system 106 can calculate a width associated with one or more carpet widths, based on an inverse parabolic representation of the plurality of VSPs. In step 316, the system 106 can combine one or more anchor points, the lane geometry information, and the one or more reference points to define the one or more carpet widths in front of the vehicle 102 based on the previous width.

[0042] In step 318, the system 106 can chain together one or more carpet widths to create the adaptable horizon carpet assigned to the vehicle 102. In step 320, the system 106 can process the one or more carpet widths for the localization of the vehicle 102 and provide information for various functions of the Advanced Driver Assistance System (ADAS).

[0043] Fig. Figure 4 shows an exemplary flowchart 400 of the calculation of variable sampling points by the proposed system 106 in accordance with an embodiment of the present disclosure. As in Fig. As shown in Figure 4, the flowchart 400 can include the following steps.

[0044] In step 402, System 106 can determine whether one or more road events related to the road segment have been detected. In step 404, based on a positive finding from step 402, System 106 can determine a corresponding speed on the road segment using the HD map. In step 406, in response to a negative finding from step 402, System 106 can determine whether one or more road scenario change offsets are identified. Based on a negative finding in this step, System 106 can proceed to step 404.

[0045] In step 408, based on a positive determination from step 406, System 106 can check whether landmark information is available from the HD map. Based on a positive determination in this step, System 106 can proceed to step 404. In step 410, System 106 can calculate the multitude of VSPs with respect to the event offset and "x" meters behind the offset, based on the vehicle's speed. In step 412, System 106 can convert the one or more VSPs (including points) into the World Geodetic System (WGS) based on the nearest HD map geometry points.

[0046] In step 414, System 106 can search for one or more VSPs that are close together, average out two VSPs, and retain one. In step 416, System 106 can send the information to a buffer for valuable point segments.

[0047] In one embodiment, the system 106 can define the plurality of VSPs on a road reference line obtained from the HD map. Furthermore, the system 106 can receive the speed information associated with a specific road segment. The system 106 can define the VSP point before the event offset ('x') based on the speed information of the road segment. The placement of the event offset can depend on the speed information. The system 106 can also convert these VSPs into WGS to correlate the geometry of the lanes with the VSP. During the summation process, the system 106 can also check whether multiple VSPs are close to each other. If two of the VSPs are less than 'y' meters apart (e.g., approximately 2 meters), the system 106 can average the two VSPs and retain one.System 106 can create a buffer from the multitude of VSPs, which can be used when creating one or more carpet widths.

[0048] Fig. Figure 5 shows an example representation of 500 variable sampling points on a road segment according to an embodiment of the present disclosure.

[0049] As in Fig. As shown in Figure 5, in one embodiment, the system 106 can extract one or more inputs from the HD map to define a variety of VSPs on the road segment. The one or more inputs can include, among other things, speed information, one or more lane attributes associated with the road segment, one or more landmarks associated with the road segment, and lane geometry information associated with the road segment. Furthermore, the one or more inputs can include one or more lane event types, such as lane splits, lane merges, lane entry points, lane exit points, relative changes in the slope of the road segment with respect to one threshold, and relative changes in the geometric curvature of the road segment with respect to another threshold.The one or more inputs can also include one or more road events, where the VSP points can be defined "x" meters before the start of the construction site, or one or more dynamic events. In one embodiment, the system 106 can determine the VSP (from the one or more VSPs) before the event offset "x" based on the vehicle's speed information.

[0050] As illustrated in FG. 5, in one embodiment, the system 106 can calculate the starting point of the carpet section using a parabolic method 502. Furthermore, the system 106 can map geometry points 504 (part of the lane geometry information) and the plurality of VSP 506 to calculate the starting point 502 of the carpet section.

[0051] Fig. Figure 6 shows an exemplary flowchart 600 of a parabolic presentation concept implemented by the proposed system 106 in accordance with an embodiment of the present disclosure.

[0052] As in Fig. As shown in Figure 6, the flowchart 600 can include the following steps.

[0053] In step 602, System 106 can use the equation of a parabolic arc to fit a parabola onto at least two consecutive VSPs. In step 604, System 106 can assign the height of the parabolic segment based on the normalized velocity values.

[0054] In step 606, the system 106 can assign a standard minimum thickness for a first section corresponding to the VSP. In step 608, the system 106 can draw a line perpendicular to one end of the first road segment. In step 610, the system 106 can determine an intersection point on the parabolic segment and calculate the length of a line extending from the VSP to the parabolic curve. In step 612, the system 106 can use the length of the line to determine the carpet section in front of the vehicle 102. Furthermore, in step 616, the system 106 can repeat steps 608 to 612 until another VSP from the plurality of VSPs is reached. In step 614, the system 106 can output all one or more carpet widths between the plurality of VSPs.

[0055] Fig. Figure 7 shows an example of a parabolic representation concept 700, which is implemented by the proposed system 106 in accordance with an embodiment of the present disclosure.

[0056] As in Fig. As shown in Figure 7, in one embodiment the height of the parabolic segment can be based on the distance between at least two consecutive VSPs and normalized velocity information from a minimum and a maximum velocity range. The equation of the parabolic segment can be defined as follows: y = h (1- ((x - a) 2 / a 2 )), where h is the height, 2a is the base distance and y is the non-normalized thickness of the road segment, where the size of the carpet section is the intersection of a straight line between the x-axis and the parabolic curve.

[0057] As in Fig.As shown in Figure 7, in one embodiment, parabola 1 702 (at a speed of 50 km / h) and parabola 1 704 (at a speed of 80 km / h) can be represented along a graph, with the size of the carpet width 706 along the Y-axis and the distance along the X-axis 708. The system 106 can calculate a width 710 associated with one or more carpet widths, based on an inverse parabolic representation of the plurality of VSPs. The one or more carpet widths can be incorporated into the carpet section 712.

[0058] While the foregoing describes various embodiments of the disclosure, other and further embodiments of the invention can be developed without departing from the fundamental scope of the disclosure. The scope of the disclosure is determined by the following claims. The disclosure is not limited to the described embodiments, versions, or examples that are included to enable a person with ordinary technical knowledge to produce and use the disclosure when combined with information and knowledge available to such a person. BENEFITS OF THE PRESENT DISCLOSURE

[0059] The present disclosure offers a scenario-specific and speed-based carpet formation that takes into account longer horizon formation and lane-based horizon formation, thus enabling earlier decision-making about a longer road based on carpet formation.

[0060] The present disclosure provides an adaptable variable sampling point (VSP) approach for creating the carpet, using a parabolic concept to determine the sampling rate of the road segments.

[0061] The present disclosure offers speed-based sampling along with geometric sampling for carpet manufacturing.

[0062] The present disclosure generates a scenario-based adaptable horizon carpet based on the calculation of VSPs based on moving vehicles and a change in lane topology.

[0063] The present disclosure offers a compromise regarding longitudinal localization in longer road sections.

[0064] The present disclosure proposes a change to the VSP sampling method, which is based on the current motorway speed and the vehicle speed, resulting in dynamic horizon creation for each motorway.

[0065] This disclosure enables the localization of specific use cases based on the variable carpet concept.

[0066] The present disclosure provides a width calculation of carpet widths based on an inverse parabola using distances between the VSPs.

[0067] The present disclosure provides a velocity-dependent, horizon-based power generation based on varying thickness and height of the parabolic curve.

[0068] The present disclosure enables the sampling rate to be determined by the parabolic concept, resulting in an optimal sampling level (without over- or undersampling). QUOTES INCLUDED IN THE DESCRIPTION

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

[0000] US 9383212B2

[0003] US 11615563B2

[0004]

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