Method and system for compressing the file size of road profile data of a road profile

Abstract

The invention relates to a method for compressing the file size of road profile data. The method comprises the following steps: - Capture of road profile data (270); - Storing the recorded road profile data (270), wherein the first road profile (70) recorded by the first front wheel (30) and the second road profile (80) recorded by the first rear wheel (40) are fully stored; - Calculating and storing deviations (370) between the profile information of the first front wheel (30) and the associated first rear wheel (40); - Applying a high-pass filter (430) and a low-pass filter (450) to the stored road profile data (270) of the first road profile (70) to generate a filtered road data profile (470) with a limitation to a relevant frequency range; - Performing a quantization of the filtered road data profile (470) to generate a quantized road data profile (570); - Performing Huffman coding on the quantized road data profile (570) to produce a compressed road data profile (770), where frequently occurring values ​​are represented by shorter codes than rarely occurring values; - Storing the compressed road data profile (770) and making it available for later access and use by the vehicle (20).

Description

[0001] The invention relates to a method, a system and a computer program product for compressing the file size of road profile data of a road profile.

[0002] Roads have differences in elevation for various reasons. Natural factors such as frost, heat, or rain lead to cracks and expansion in the road surface. Traffic, especially heavy goods vehicles, also contributes to wear and tear, causing ruts and potholes. Furthermore, construction defects, soil changes, and irregular repairs can lead to additional unevenness.

[0003] These differences in elevation affect the driving experience and, if not detected and compensated for in time, can lead to road surface deterioration and increased vehicle wear. Therefore, it is important to accurately measure these differences to make driving safer and more comfortable.

[0004] When creating digital road maps, the road's elevation differences are estimated and stored. This elevation information is not recorded as exact elevation values ​​of the road (e.g., above sea level), but rather as relative movements of the vehicle body in relation to the wheels or suspension. These movements are measured by sensors on the vehicle while it is driving. Typically, these sensors are accelerometers and gyroscopes that register vibrations and changes in the vehicle's orientation. Radar sensors, LiDAR (Light Detection and Ranging) systems, or camera systems can also be used to directly detect road surface irregularities.

[0005] Since the system records the movements of the vehicle body and / or wheels instead of directly measuring height differences, the resulting signals must be interpreted to estimate the actual road surface elevation changes. A two-mass oscillator represents a simplified model that treats the vehicle as two masses: one for the vehicle body and one for the wheel (or wheel suspension). These two masses are connected by the suspension and damping system and oscillate independently in response to road irregularities. The acceleration and position changes measured by the sensors affect both masses, i.e., the vehicle body and the wheels.

[0006] Analyzing the signals from the two masses allows for an estimation of the road profile. This implies that the sensors measure the movement of the vehicle body and wheels, and by modeling the vehicle dynamics, for example using a two-mass oscillator, the actual road profiles (i.e., the differences in road surface elevation) can be reconstructed. The calculation thus establishes the relationship between the detected vehicle movements and the road profile, which is not directly captured by the sensors but only through the processing of the measured signals.

[0007] The sensors measure the movements of the wheels and vehicle body caused by road surface irregularities, and from this data, the road's elevation profile is calculated using a model – that is, the height coordinate z above the road surface. A separate elevation profile is determined for each wheel of the vehicle, as each wheel track can exhibit different irregularities. This road profile describes the changes in road height and can, for example, contribute to improving driving comfort and safety.

[0008] The recorded road profiles are then uploaded to a cloud computing infrastructure, where they are stored and further processed. One specific application of this data is the so-called preview function or predictive control. This predictive control function allows the vehicle to prepare for upcoming road conditions before it reaches them. If the vehicle knows data about the road profile in advance—such as bumps, potholes, or inclines—it can adjust its driving dynamics and control strategies, such as suspension settings or speed, to increase driving comfort and safety. This not only saves energy but also improves the driving experience. For the preview function to work optimally, the road profile data must be downloaded in advance and stored in the cache so that the vehicle can use this data.

[0009] A vehicle's cache is a fast, temporary storage device that buffers data for short-term access. In this context, the cache stores route profile data downloaded from the cloud computing infrastructure. This data is then immediately available when the vehicle travels the saved route again. The cache is particularly useful for ensuring that the vehicle control system can access relevant information without delay and without relying on a cellular connection.

[0010] Since the storage space in the vehicle's control unit and the bandwidth of the mobile connection are limited, it is important to keep the amount of data as small as possible. The goal is therefore efficient data compression to save storage space and bandwidth while ensuring that the information relevant for the preview function is retained.

[0011] The challenge lies in finding the right balance between data accuracy and data volume. Too much data can lead to storage and transmission problems, while too little data can impair the accuracy of the road profiles and thus the efficiency of the preview function.

[0012] German patent DE 10 2014 213 326 A1 discloses a method for processing data from a road profile representing the local physical road surface properties of a road segment. For this purpose, vehicle position information is determined. In addition, road profile data representative of the local physical road surface properties of the road segment are recorded. Subsequently, a spectral distribution of the data is performed and assigned to the position information. This spectral distribution significantly reduces the amount of data required for road surface properties.

[0013] US Patent 5,065,618 A describes a method and apparatus for measuring the physical profile of a road surface. Force transducers and accelerometers are integrated into each of the four wheels of a vehicle to measure the irregularities of the road surface perpendicular to the direction of travel. A shaft encoder is mounted on each of the front wheels to detect wheel rotations and thus determine the distance traveled.

[0014] The object of the invention is the development of an efficient data compression method for reducing the storage and transmission requirements of road profile data acquired by vehicle sensors. The aim is to achieve compression with minimal loss that meets the specific requirements of vehicle control. The resulting compression method must ensure that the compressed road profile data can be efficiently retrieved from a cloud computing infrastructure, stored in the vehicle's cache memory, and used for the preview function, which enables predictive control during driving by providing relevant data in advance.

[0015] This problem is solved according to the invention with respect to a system by the features of claim 1, with respect to a method by the features of claim 9, and with respect to a computer program product by the features of claim 15. The further claims relate to preferred embodiments of the invention.

[0016] The invention provides a highly efficient method for processing and storing data relevant to vehicle control for a road profile for preview functions. By combining several data compression methods, storage and transmission requirements are significantly minimized, while simultaneously preserving the information necessary for precise vehicle control.

[0017] A key aspect of the invention is the differentiation process, which is based on exploiting the similarity of the road profiles of the vehicle wheels. By eliminating redundant information, it is ensured that at least one complete road profile is stored, while the difference values ​​of the remaining road profiles are efficiently compressed. This not only reduces the amount of data but also enables the targeted storage of the most important information.

[0018] A further advantage is achieved by reducing the frequency range. The recorded road profile data is filtered so that only the frequencies relevant for vehicle control, typically in the range of 0.5 Hz to 25 Hz, are retained. This targeted filtering optimizes the relevance of the stored data and improves the efficiency of the vehicle dynamics control system.

[0019] Additionally, a quantization method is implemented that varies the data accuracy depending on the frequency. Low frequencies are captured with high accuracy, medium frequencies with medium accuracy, and high frequencies with low accuracy. This flexible handling of data accuracy enables optimal use of storage resources while simultaneously ensuring the level of detail required for vehicle control.

[0020] Finally, Huffman coding is applied to further compress the remaining data. By taking into account the frequency of elevation changes or frequencies in the road profiles, frequently occurring patterns are efficiently stored using shorter bit sequences. This leads to a further reduction in the amount of data, which optimizes transmission over mobile networks.

[0021] Overall, the combination of these four methods enables a significant reduction in the storage requirements of a cache memory and an improvement in the efficiency of data transmission while retaining the information necessary for vehicle control. The system according to the invention thus represents a solution that ensures precise and efficient vehicle control.

[0022] According to a first aspect, the invention provides a method for compressing the file size of road profile data. The method comprises the following process steps: - Acquisition of road profile data, wherein the road profile data represent the height above the road surface and are acquired by vehicle sensors of at least one first front wheel and at least one first rear wheel of a vehicle, wherein the vehicle sensors measure the movements of the wheels and the vehicle body, from which the height is calculated by modeling; - Storing the recorded road profile data, whereby the first road profile recorded by the first front wheel and the second road profile recorded by the first rear wheel are fully stored; - Calculating and storing deviations between the profile information of the first front wheel and the associated first rear wheel, where the deviations are defined as the difference between the recorded data points of the first road profile of the first front wheel and the synchronously recorded data points of the second road profile of the associated first rear wheel; - Applying a high-pass filter and a low-pass filter to the stored road profile data of the first road profile to generate a filtered road data profile with a limitation to a relevant frequency range; - Performing quantization of the filtered road data profile to generate a quantized road data profile, reducing data accuracy in less relevant frequency ranges; - Performing Huffman coding on the data of the quantized road data profile to generate a compressed road data profile, with frequently occurring values ​​represented by shorter codes than rarely occurring values; - Saving the compressed road data profile and making it available for later access and use by the vehicle.

[0023] Further training stipulates that the vehicle sensors for recording road profile data should include at least acceleration sensors, laser scanners or camera sensors, each designed to detect changes in elevation and surface condition of the roadway.

[0024] In an advantageous embodiment, it is provided that the road profile data acquired by the various sensor types, such as accelerometers, laser scanners and cameras, are fused to enable a more detailed and accurate recording of the road surface.

[0025] In a further embodiment, it is provided that the relevant frequency range, which is limited by the high-pass filter and the low-pass filter, includes frequencies between 0.5 Hz and 25 Hz in order to detect the road irregularities essential for vehicle control.

[0026] Advantageously, low frequencies below 0.5 Hz are removed to filter out slow changes in altitude, which are of little importance for vehicle dynamics and vehicle control.

[0027] In particular, high frequencies above 25 Hz are removed to filter out rapid and subtle changes in altitude that are of little importance for vehicle dynamics and vehicle control.

[0028] In a further development, it is planned that the compressed road data profile and the deviations from a storage module, which is integrated in particular into a cloud computing infrastructure, will be downloaded as needed and temporarily stored in a cache memory of the vehicle to ensure fast availability of the data for real-time vehicle control.

[0029] Advantageously, the compressed road data profile and deviations are continuously downloaded into the vehicle's cache memory to provide current and relevant road data for predictive driving control.

[0030] According to a second aspect, the invention provides a system for compressing the file size of road profile data. The system comprises a data acquisition module with vehicle sensors configured to acquire road profile data, wherein the road profile data represents the height above the road surface and is acquired by vehicle sensors of at least one first front wheel and at least one first rear wheel of a vehicle, wherein the vehicle sensors measure the movements of the wheels and the vehicle body, from which the height is calculated by modeling;a data processing module configured to store the captured road profile data, wherein the first road profile captured by the first front wheel and the second road profile captured by the first rear wheel are fully stored, and to calculate and store deviations between the profile information of the first front wheel and the associated first rear wheel, wherein the deviations are defined as the difference between the captured data points of the first road profile of the first front wheel and the synchronously captured data points of the second road profile of the associated first rear wheel; a filtering module with a high-pass filter and a low-pass filter configured to apply the high-pass filter and the low-pass filter to the stored road profile data of the first road profile to generate a filtered road data profile limited to a relevant frequency range;a quantization module configured to perform quantization of the filtered road data profile to produce a quantized road data profile, reducing data accuracy in less relevant frequency ranges; and an encoding module configured to perform Huffman coding on the quantized road data profile to produce a compressed road data profile, with frequently occurring values ​​represented by shorter codes than rarely occurring values; and a storage module configured to store the compressed road data profile and make it available for later access and use by the vehicle.

[0031] Further training stipulates that the vehicle sensors for capturing road profile data should include at least acceleration sensors, laser scanners or camera sensors, each designed to detect changes in elevation and surface condition of the road; and that the road profile data captured by the different sensor types should be fused to enable a more detailed and accurate capture of the road surface.

[0032] In an advantageous embodiment, the relevant frequency range, which is limited by the high-pass filter and the low-pass filter, includes frequencies between 0.5 Hz and 25 Hz in order to detect the road irregularities that are essential for vehicle control.

[0033] In a further embodiment, it is provided that low frequencies below 0.5 Hz are removed to filter out slow changes in altitude that are of little importance for vehicle dynamics and vehicle control; and that high frequencies above 25 Hz are removed to filter out fast and subtle changes in altitude that are of little importance for vehicle dynamics and vehicle control.

[0034] In a further training program, it is envisaged that the storage module is integrated into a cloud computing infrastructure, whereby the compressed road data profile and deviations stored in the cloud computing infrastructure can be downloaded and temporarily stored in a cache memory of the vehicle in order to ensure fast availability of the compressed road data profile for real-time vehicle control.

[0035] Advantageously, the compressed road data profile is continuously downloaded into the vehicle's cache memory to provide current and relevant road data for predictive driving control.

[0036] According to a third aspect, the invention provides a computer program product comprising an executable program code configured to perform the method according to the first aspect when executed.

[0037] The invention will now be explained in more detail with reference to exemplary embodiments shown in the drawing.

[0038] This shows: Fig. 1 a block diagram to illustrate an embodiment of a system according to the invention; Fig. 2 a flowchart to explain the individual process steps of a process according to the invention; Fig. 3 a block diagram of a computer program product according to an embodiment of the third aspect of the invention.

[0039] Additional features, aspects and advantages of the invention or its embodiments are explained in the following description in conjunction with the claims.

[0040] Fig. Figure 1 shows a system 100 according to the invention for compressing the file size of road profile data. The system 100 comprises a data acquisition module 200 for acquiring road profile data 270 by vehicle sensors 250 of a vehicle 20, a data processing module 300, a filtering module 400, a quantization module 500, a coding module 700, and a storage module 800, each of which performs various functions for acquiring, processing, and storing the road profile data 270. Furthermore, communication links are provided, in particular between the data acquisition module 200 and the data processing module 300, as well as between the storage module 800 and a cache memory 50 of the vehicle 20. The modules can each be equipped with a storage unit and / or a processor.

[0041] In the context of the invention, a "module" is defined as a self-contained, specialized unit of software and / or hardware components. A module is designed to perform a specific function or task and is independent and self-contained; that is, it accepts specific inputs, performs internal processing, and then delivers specific outputs or results. A module can communicate with other modules or components via interfaces. These interfaces determine how data or commands are input into the module and how results or information are output.

[0042] In the context of the invention, a "processor" can be, for example, a machine or an electronic circuit. A processor can, in particular, be a central processing unit (CPU), a microprocessor, or a microcontroller, such as an application-specific integrated circuit or a digital signal processor, optionally in combination with a memory unit for storing program instructions. A processor can also be a virtualized processor, a virtual machine, or a soft CPU. It can, for example, also be a programmable processor equipped with configuration steps for executing the method according to the invention, or configured with configuration steps such that the programmable processor implements the features of the method, the modules, or other aspects and / or partial aspects of the invention.In particular, the processor can contain highly parallel processing units and powerful graphics modules. In the context of the invention, a "memory unit" or "memory module" and the like can refer, for example, to volatile memory in the form of random access memory (RAM), permanent memory such as a hard drive or data storage medium, or, for example, a replaceable memory module. The memory module can also be a cloud storage solution.

[0043] The term "database" refers to both a storage algorithm and the hardware in the form of a storage unit. In particular, a database can be part of a cloud computing infrastructure.

[0044] In the context of the invention, "data" includes both raw data and processed data from sensor measurement results and other data sources.

[0045] The communication links for exchanging and transmitting data between the individual modules are primarily wireless communication links, e.g., mobile communication links (e.g., 4G LTE, 5G, 6G) and / or near-field communication links, e.g., Bluetooth. ® , Ethernet, NFC (Near Field Communication) or Wi-Fi ® executed.

[0046] In particular, the Data Processing Module 300, the Filtering Module 400, the Quantization Module 500, the Encoding Module 700, and the Storage Module 800 are integrated into a Cloud Computing Infrastructure 900. Using a Cloud Computing Infrastructure 900 for the Storage Module 800 and the other modules offers numerous advantages. First, the Cloud Computing Infrastructure 900 has virtually unlimited storage capacity, allowing large volumes of road profile data 270 to be stored efficiently and securely. Furthermore, the Cloud Computing Infrastructure 900 facilitates access to this data from various vehicles 20 and locations. By utilizing cloud-based resources, computationally intensive tasks such as data processing and data analysis can also be performed efficiently without burdening the vehicle hardware.Furthermore, the Cloud Computing Infrastructure 900 enables seamless integration with other applications and systems, facilitating the development of new features and improvements. Overall, the Cloud Computing Infrastructure 900 optimizes the performance, efficiency, and scalability of the entire System 100.

[0047] The data acquisition module 200 is connected to the vehicle sensors 250, which are located in the area of ​​a first front wheel 30 and a first rear wheel 40 of the vehicle 20. These sensors 250 continuously record the elevation profiles of the road over which the vehicle 20 travels and generate road profile data 270, which represent the vertical height z above the road surface.

[0048] The high-precision sensors 250 are mounted in the area of ​​the wheels 30, 35, 40, and 45 of the vehicle 20. These sensors 250 are primarily accelerometers, laser scanners (especially lidar sensors), or camera sensors, each designed to detect changes in elevation and the surface characteristics of the road. The accelerometers measure the vertical movements of the vehicle 20, while the lidar and camera sensors detect the geometry and texture of the road. A first road profile 70, representing the changes in road elevation in front of the vehicle 20, is generated from the fusion of the data acquired by the first front wheel 30. Simultaneously, the sensors in the area of ​​the first rear wheel 40 generate a second road profile 80, describing the differences in elevation behind the vehicle 20.Additionally, further sensors 250 can be installed in the area of ​​a second front wheel 35, which record a third road profile 75, as well as in the area of ​​a second rear wheel 45, which record a fourth road profile 85. These road profiles 70, 75, 80, 85 provide a complete picture of the road surface along the vehicle's lane 20, which is particularly important for precise vehicle control.

[0049] These recorded road profile data 270 are then forwarded to the data processing module 300 and / or the storage module 800. The complete road profiles 70, 80 of the first front wheel 30 and the first rear wheel 40 are stored in the data processing module 300 and / or the storage module 800. Furthermore, the data processing module 300 calculates the deviations 370 between the road profile data 270 of these two road profiles 70, 80 by determining the difference between the data points of the first road profile 70 of the first front wheel 30 and the synchronously recorded data points of the second road profile 80 of the corresponding first rear wheel 40.

[0050] Furthermore, the complete third and fourth road profiles 75, 85 of the second front wheel 35 and the second rear wheel 45 can be stored in the data processing module 300. Deviations 370 between the road profile data 270 of the third road profile 75 and the fourth road profile 85 are also determined and stored in the storage module 800.

[0051] Difference analysis is an efficient data compression method that exploits the similarity of the road profiles 70, 80 of the wheels 30, 40 on one side of a vehicle 20. A road profile 70, 80 consists of a series of measurement points that describe the height z of a particular road segment relative to the horizontal plane of the vehicle 20. These measurements are taken continuously in the direction of travel, and the resulting road profiles 70, 80 form a sequence of data points that represent the road topography. When a vehicle 20 travels on a road, the wheels 30, 40 on the same side (e.g., front left and rear left) perceive essentially the same road segment because they follow the same track. The first and second road profiles 70, 80 of the first front wheel 30 and the first rear wheel 40 therefore contain very similar information about the road's elevation changes.

[0052] By calculating the difference between the synchronously recorded data points of the road profiles 70, 80 of the two wheels 30, 40, redundant information can be eliminated. The difference is calculated by subtracting the height z2 recorded by the rear wheel 40 from the height z1 recorded simultaneously (synchronously) by the front wheel 30 for each measurement point along the route. The resulting difference profile thus describes the minimal differences in the perception of the road surface by the two wheels 30, 40. Ideally, i.e., with a uniform road surface and synchronous measurement, the road profiles 70, 80 are almost identical, meaning the difference is very small or even zero.This leads to a significant reduction in the amount of data, since only the complete first road profile 70 of the first front wheel 30 needs to be stored and transmitted, while for the first rear wheel 40 only the difference values ​​of the deviations 370 are needed to reconstruct the complete second road profile 80.

[0053] This method of calculating differences can also be applied to the road profiles 75, 85 of the wheels 35, 45 on the other side of the vehicle 20 (e.g., front right and rear right). By combining the difference calculation for both sides of the vehicle, the total amount of data is further reduced, since similar height differences recorded on both sides of the vehicle can also be expressed as differences and thus compressed.

[0054] By recording road profile data not only for one lane, i.e., for the left or right lane of vehicle 20, but for both lanes (left and right wheels), the road profiles can be compared between the left front wheel 30 and the left rear wheel 40, as well as between the right front wheel 35 and the right rear wheel 45. Additionally, it is also possible to compare the road profiles between the right front wheel 35 and the left rear wheel 40, as well as between the left front wheel 30 and the right rear wheel 45, and to calculate the differences. These variations enable a comprehensive analysis of road irregularities and offer various options for optimizing ride comfort and safety ratings.

[0055] Another possibility is to average the two determined road profiles of a lane (e.g., the left lane) of vehicle 20, so that only one averaged profile per lane (left and right) is transmitted. The difference between the averaged road profiles of the left and right lanes can then be calculated. This further reduces the amount of data to be transmitted without losing essential information.

[0056] Difference calculation thus represents a particularly efficient method for data reduction, as it utilizes the inherent redundancy of the road profile data 270 and thereby optimizes the transmission and storage of the relevant information. This is crucial to minimize the storage space and bandwidth requirements of the mobile communication connection between the storage module 800 and a cache memory 50 of the vehicle 20, while simultaneously ensuring the accuracy and relevance of the data for further processing and use.

[0057] In the next step, the filtering module 400 and the quantization module 500 are used. The filtering module 400 contains a high-pass filter 430 and a low-pass filter 450, which are applied to the stored road profile data 270 of the first road profile 70 and the third road profile 75, respectively, in order to extract only the relevant frequency ranges that are crucial for vehicle dynamics. This means that both very slow changes in altitude (low frequencies) and very rapid changes (high frequencies) are filtered out.

[0058] Road profiles contain information about changes in road surface elevation, represented in different frequency ranges. These frequencies represent the spatial density of irregularities. Lower frequencies represent larger, undulating changes such as hills or bumps, while higher frequencies represent finer, smaller irregularities such as cracks or small potholes. However, not all of these frequencies are equally relevant for vehicle control. To optimize the amount of data, frequency ranges that are less important for vehicle behavior are filtered out.

[0059] In the context of road profiles, the unit "Hertz" (Hz) is used to describe the spatial frequency of road surface irregularities that a vehicle travels over at a given speed per second. The frequency of a road profile thus describes how often a particular bump or wave occurs along the road as a vehicle travels over it. For example, a frequency of 1 Hz means that at a constant speed, the vehicle experiences one complete wave motion (one up and one down movement) per second. The effective spatial wavelength of this irregularity depends on the vehicle's speed. As the vehicle travels faster, the distance over which the wave motion occurs increases at the same frequency.

[0060] Frequency analysis in Hz makes it possible to identify the frequency ranges relevant to vehicle dynamics and to filter out unnecessary or less important frequencies, leading to more efficient data processing and storage. By using filters, the frequency range of the road profile data 270 acquired by the sensors 250 can be limited to the relevant frequency ranges. For example, frequencies below 0.5 Hz, which represent very slow and large undulations in the road surface, can often be disregarded because they are of minor importance for vehicle control. They are removed by the high-pass filter 430, which blocks the low frequencies and allows only the higher frequencies to pass.On the other hand, frequencies above 25 Hz, which represent very small and fine details such as cracks in the road surface, can also be considered less relevant, as they only have a minor effect on driving behavior. These high frequencies are filtered out using the 450 Hz low-pass filter.

[0061] A 430 high-pass filter allows frequencies above a certain threshold (the so-called cutoff frequency) to pass through and attenuates or suppresses frequencies below this threshold. In this application, this means that frequencies below 0.5 Hz, which represent slow, large-scale changes in the road surface, are attenuated or suppressed. This is beneficial because these low frequencies often represent large, gentle undulations or gradients that are less relevant for direct vehicle control. Instead, the vehicle control system focuses on the faster, more abrupt changes in the road surface that are more critical for driving behavior. Suppressing these low frequencies reduces the amount of data while retaining the information crucial for vehicle dynamics.

[0062] A 450 Hz low-pass filter allows frequencies below a certain threshold (the so-called cutoff frequency) to pass through and attenuates or suppresses frequencies above this threshold. In this application, this means that frequencies above 25 Hz, which represent very rapid and subtle pitch changes, are attenuated or removed. This is beneficial because, while such high frequencies can be useful for a detailed analysis of the road surface, they are of less importance for direct vehicle control. Removing these high frequencies reduces the amount of data without sacrificing the information essential for vehicle dynamics.

[0063] This targeted reduction of the frequency range optimizes the amount of data by filtering out superfluous or irrelevant information, while retaining the frequencies essential for vehicle control and analysis, typically in the range of 0.5 Hz to 25 Hz. In this way, the vehicle can continue to react effectively to road irregularities, while minimizing storage and transmission requirements.

[0064] The filtered road data profile 470 is then further processed in the quantization module 500 to generate a quantized road data profile 570, in which the accuracy is reduced in less relevant frequency ranges in order to further reduce the amount of data.

[0065] Quantization is a fundamental data compression technique that converts continuous values ​​into discrete steps. This means that instead of the exact, continuous measurements, only specific, predefined values ​​are stored. This reduces the amount of data by decreasing the precision of the stored data. In practice, this is often achieved by defining "quantization levels" that determine which exact values ​​are rounded to the nearest level. The higher the number of levels, the greater the precision, but also the larger the data volume.

[0066] In the context of road profile data 270, quantization means that elevation information is captured and stored in different frequency ranges with varying degrees of accuracy. Low frequencies (up to 8 Hz) are particularly important for vehicle control 20 because they represent larger, but less frequent, irregularities that can significantly affect driving behavior. These frequencies must therefore be reproduced very accurately, which means they are stored with a higher number of quantization levels and thus with less data reduction.

[0067] Between 8 and 15 Hz, a range less critical for vehicle dynamics, medium accuracy is sufficient. Here, the amount of data is further reduced by choosing coarser quantization levels.

[0068] In the 15 to 25 Hz range, which is less relevant for vehicle control but still important for detecting road conditions, a lower level of accuracy is sufficient. Quantization here is performed in a way that allows for further data reduction by considering only coarse height differences.

[0069] This tiered approach ensures that the highest accuracy is maintained only where it is truly necessary, while in less critical areas the amount of data is significantly reduced through lower accuracy. In this context, "accuracy" refers to the granularity with which the data is stored; that is, the finer the granularity, the more accurate, but also the more data-intensive the data storage.

[0070] The encoding module 700 then processes the quantized road data profile 570 into a compressed road data profile 770. The encoding module 700 performs Huffman coding, in which frequent values ​​of the data in the quantized road data profile 570 are represented by shorter bit sequences than rare values. This further reduces the overall size of the data to be stored in the road profile 70 without losing any essential information.

[0071] Huffman coding is a lossless data compression method named after computer scientist David A. Huffman, who developed it in 1952. Huffman designed the algorithm while searching for a way to minimize the average length of codes used to represent data. The method is based on the frequency of symbols or data values ​​in a dataset. Shorter bit sequences are assigned to the most frequent data values, while less frequent data values ​​receive longer bit sequences. This results in an overall reduction in the average amount of data, as the most frequently occurring data values ​​in the road profiles require less storage space.

[0072] A bit sequence is a sequence of bits, i.e., binary digits (0s and 1s), which represent the smallest unit of information in the digital world. Bits are combined in different lengths to represent specific data. In Huffman coding, a bit sequence consists of a combination of zeros and ones that correspond to a specific data value. Frequently occurring values ​​are represented by shorter bit sequences (e.g., "01"), while less frequent values ​​are represented by longer bit sequences (e.g., "11011").

[0073] These bit sequences are stored in digital storage formats such as binary files. The bits are written sequentially into memory, and their length and order are crucial for how the data can later be decoded and converted back into its original values. By cleverly assigning short bit sequences to frequent values ​​and long bit sequences to rare values, Huffman coding reduces the average storage requirement without information loss, making it a very efficient and lossless compression method.

[0074] Huffman coding is particularly effective when certain elevation changes or frequencies occur more frequently than others in the road profile data. This means that road profiles exhibiting regular or repeating patterns are ideal for this type of compression. When a vehicle travels along a road, certain types of irregularities or regular patterns in the elevation profiles can recur. These recurring values ​​can be efficiently encoded using shorter bit sequences because of their frequency. For example, small, regular waves in the road surface or typical grooves in the asphalt can occur regularly in road profiles. Such frequently recurring patterns require less memory in Huffman coding because the bit sequences assigned to them are shorter.

[0075] Rare or irregular changes in elevation, such as suddenly appearing potholes or unusually high bumps, occur less frequently and are therefore assigned longer bit sequences. Since these unusual values ​​are less common, assigning longer bit sequences to them still reduces the overall storage requirement. Huffman coding is therefore particularly efficient when the road profile data exhibits 260 unique, frequently recurring patterns, as these patterns can be represented by short, space-saving bit sequences.

[0076] Finally, the compressed road profile data 770 is stored in the memory module 800. The memory module 800 is configured to provide the compressed data of road profile 70 in the form of the compressed road data profile 770 for later retrieval and use by the vehicle 20.

[0077] The data from the compressed road data profile 770 is loaded from storage module 800 into the vehicle's cache memory 50 to ensure rapid availability. The data is temporarily stored in cache memory 50 and is available there for real-time processing. This real-time processing is crucial for the dynamic adaptation of vehicle control, such as optimizing the chassis, adjusting the damping systems, or fine-tuning driver assistance systems. Cache memory 50 allows the vehicle 20 direct access to the road data without delays caused by data transfers from the cloud computing infrastructure 900 or other storage locations. This enables responsive control of the vehicle 20 and improved driving performance on different road surfaces.

[0078] In Fig. Figure 2 shows the process steps for compressing the file size of road profile data 270 of a road profile.

[0079] In step S10, road profile data 270 are recorded, wherein the road profile data 270 represent the height z above the road surface and are recorded by vehicle sensors 250 of at least one first front wheel 30 and at least one first rear wheel 40 of a vehicle 20.

[0080] In step S20 the recorded road profile data 270 are stored, whereby the first road profile 70 recorded by the first front wheel 30 and the second road profile 80 recorded by the first rear wheel 40 are fully stored.

[0081] In step S30, the deviations 370 between the profile information of the first front wheel 30 and the associated first rear wheel 40 are calculated and stored, whereby the deviations 370 are defined as the difference between the recorded data points of the first road profile 70 of the first front wheel 30 and the synchronously recorded data points of the second road profile 80 of the associated first rear wheel 40.

[0082] In step S40, a high-pass filter 430 and a low-pass filter 450 are applied to the stored road profile data 270 of the first road profile 70 to generate a filtered road data profile 470 with a limitation to a relevant frequency range.

[0083] In step S50, the filtered road data profile 470 is quantized to create a quantized road data profile 570, reducing data accuracy in less relevant frequency ranges.

[0084] In step S60, Huffman coding is performed on the data of the quantized road data profile 570 to generate a compressed road data profile 770, where frequently occurring values ​​of the data of the quantized road data profile 570 are represented by shorter codes than rarely occurring values.

[0085] In step S70, the compressed road data profile 770 is saved and made available for later access and use by the vehicle 20.

[0086] Fig.Figure 3 schematically represents a computer program product 1000 comprising an executable program code 1050 configured to execute the method according to the second aspect of the present invention.

[0087] The system 100 according to the invention offers an efficient solution for processing and storing road profile data relevant for vehicle control by combining several data compression methods. Difference calculation eliminates redundant information by storing at least one complete road profile and the compressed difference values ​​of the other road profiles. Frequency range reduction filters out only the frequencies relevant to vehicle dynamics (0.5 Hz to 25 Hz), while a quantization method adjusts the data accuracy as a function of frequency. Additionally, Huffman coding is used to efficiently compress the remaining data by storing frequent patterns in shorter bit sequences.

[0088] The order of the methods represents a logical sequence for efficiently processing and compressing the road profile data 270: a) Differentiation: By eliminating redundant information at the beginning, the amount of data is reduced from the outset, which is advantageous for the subsequent steps. b) Reduction of the frequency range: Filtering the road profile data 270 after the difference calculation ensures that only the relevant frequencies are retained, which makes the subsequent steps (quantization and coding) more efficient. c) Quantization: This method can best be applied to the data of the already filtered road data profile 470, as only the relevant frequencies are taken into account, thus enabling a targeted reduction of accuracy. d) Huffman coding: The final application of Huffman coding to the data of the quantized road data profile 570 ensures that it is further compressed, thereby minimizing the storage requirement.

[0089] Overall, the combination of these methods leads to a significant reduction in storage requirements and an optimization of data transmission while retaining the information necessary for vehicle control.

Claims

[1] Method for compressing the file size of road profile data of a road profile, comprising the following steps: - Acquisition (S10) of road profile data (270), wherein the road profile data (270) represent the height (z) above the road surface and are acquired by vehicle sensors (250) of at least one first front wheel (30) and at least one first rear wheel (40) of a vehicle (20), wherein the vehicle sensors (250) measure the movements of the wheels (30, 40) and the vehicle body, from which the height (z) is calculated by modeling; - Storing (S20) the recorded road profile data (270), wherein the first road profile (70) recorded by the first front wheel (30) and the second road profile (80) recorded by the first rear wheel (40) are fully stored; - Calculating (S30) and storing deviations (370) between the profile information of the first front wheel (30) and the associated first rear wheel (40), wherein the deviations (370) are defined as the difference between the recorded data points of the first road profile (70) of the first front wheel (30) and the synchronously recorded data points of the second road profile (80) of the associated first rear wheel (40); - Applying (S40) a high-pass filter (430) and a low-pass filter (450) to the stored road profile data (270) of the first road profile (70) to generate a filtered road data profile (470) with a limitation to a relevant frequency range; - Performing (S50) a quantization of the filtered road data profile (470) to generate a quantized road data profile (570), reducing the data accuracy in less relevant frequency ranges; - Performing (S60) Huffman coding on the data of the quantized road data profile (570) to produce a compressed road data profile (770), where frequently occurring values ​​are represented by shorter codes than rarely occurring values; - Saving (S70) the compressed road data profile (770) and making it available for later access and use by the vehicle (20). [2] Method according to claim 1, wherein the vehicle sensors (250) for capturing the road profile data (270) comprise at least acceleration sensors, laser scanners or camera sensors, each designed to detect changes in elevation and surface condition of the roadway. [3] Method according to claim 2, wherein the road profile data (270) acquired by the different sensor types such as accelerometers, laser scanners and cameras are fused to enable a more detailed and accurate detection of the road surface. [4] Method according to any one of claims 1 to 3, wherein the relevant frequency range, which is limited by the high-pass filter (430) and the low-pass filter (450), comprises frequencies between 0.5 Hz and 25 Hz to detect the road irregularities essential for vehicle control. [5] Method according to claim 4, wherein low frequencies below 0.5 Hz are removed to filter out slow altitude changes which are of minor importance for vehicle dynamics and vehicle control. [6] Method according to claim 4 or 5, wherein high frequencies above 25 Hz are removed to filter out fast and subtle changes in altitude which are of minor importance for vehicle dynamics and vehicle control. [7] Method according to any one of claims 1 to 6, wherein the compressed road data profile (770) and the deviations (370) are downloaded from a storage module (800), which is in particular integrated into a cloud computing infrastructure (900), as required and temporarily stored in a cache memory (50) of the vehicle (20) to ensure fast availability of the data for real-time vehicle control. [8] Method according to claim 7, wherein the downloading of the compressed road data profile (770) and the deviations (370) into the cache memory (50) of the vehicle (20) is carried out continuously to provide current and relevant road data for predictive driving control. [9] System (100) for compressing the file size of road profile data (270) of a road profile, comprising a data acquisition module (200) with vehicle sensors (250) configured to acquire road profile data (270), wherein the road profile data (270) represent the height (z) above the road surface and are acquired by vehicle sensors (250) of at least one first front wheel (30) and at least one first rear wheel (40) of a vehicle (20), wherein the vehicle sensors (250) measure the movements of the wheels (30, 40) and the vehicle body, from which the height (z) is calculated by modeling;a data processing module (300) configured to store the captured road profile data (270), wherein the first road profile (70) captured by the first front wheel (30) and the second road profile (80) captured by the first rear wheel (40) are fully stored, and to calculate and store deviations (370) between the profile information of the first front wheel (30) and the associated first rear wheel (40), wherein the deviations (370) are defined as the difference between the captured data points of the first road profile (70) of the first front wheel (30) and the synchronously captured data points of the second road profile (80) of the associated first rear wheel (40);a filtering module (400) with a high-pass filter (430) and a low-pass filter (450) configured to apply the high-pass filter (430) and the low-pass filter (450) to the stored road profile data (270) of the first road profile (70) to produce a filtered road data profile (470) limited to a relevant frequency range; a quantization module (500) configured to perform quantization of the filtered road data profile (470) to produce a quantized road data profile (570), reducing the data accuracy in less relevant frequency ranges; and a coding module (700) configured to perform Huffman coding on the quantized road data profile (570) to produce a compressed road data profile (770), with frequently occurring values ​​represented by shorter codes than rarely occurring values;and a storage module (800) configured to store the compressed road data profile (770) and make it available for later access and use by the vehicle (20). [10] System (100) according to claim 9, wherein the vehicle sensors (250) for acquiring the road profile data (270) comprise at least acceleration sensors, laser scanners or camera sensors, each designed to detect changes in elevation and surface characteristics of the roadway; and wherein the road profile data (270) acquired by the different sensor types are fused to enable a more detailed and accurate detection of the road surface. [11] System (100) according to claim 9 or 10, wherein the relevant frequency range, which is limited by the high-pass filter (430) and the low-pass filter (450), comprises frequencies between 0.5 Hz and 25 Hz to detect the road irregularities essential for vehicle control. [12] System (100) according to claim 11, wherein low frequencies below 0.5 Hz are removed to filter out slow changes in altitude which are of little importance for vehicle dynamics and vehicle control; and wherein high frequencies above 25 Hz are removed to filter out fast and subtle changes in altitude which are of little importance for vehicle dynamics and vehicle control. [13] System (100) according to one of claims 9 to 12, wherein the storage module (800) is integrated into a cloud computing infrastructure (900), and wherein the compressed road data profile (770) and the deviations (370) stored in the cloud computing infrastructure (900) can be downloaded and temporarily stored in a cache memory (50) of the vehicle (20) to ensure fast availability of the compressed road data profile (770) for real-time vehicle control. [14] System (100) according to claim 13, wherein the downloading of the compressed road data profile (770) and the deviations (370) into the cache memory (50) of the vehicle (20) is carried out continuously to provide current and relevant road data for predictive driving control. [15] Computer program product (1000) comprising an executable program code (1050) configured to perform the method according to any one of claims 1 to 8 when executed.

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