Device, system and method for detecting speed bumps and potholes on roads

Abstract

A sensing device (103) for detecting speed bumps or potholes on a road is disclosed. The sensing device (103) includes a telemetry sensing system (107) for collecting a plurality of position vectors by telemetry sensing of a road surface, wherein each position vector extends from a common origin to a corresponding point on the road surface, and each position vector has a length and a direction. The sensing device also includes a processing circuit (105) for detecting road roughness anomalies by evaluating the lengths of the position vectors. Thus, an improved device (103) for detecting road irregularities such as speed bumps or potholes on a road is provided. The sensing device (103) remains functional in low light conditions (e.g., at night) and in the presence of vehicle reverberation.

Description

Technical Field

[0001] The present invention relates to perception technology. More specifically, the present invention relates to an apparatus, system, and method for detecting road irregularities, such as speed bumps or potholes on a road. Background Art

[0002] Advanced driver assistance systems and autonomous driving systems are being deployed in an increasing number of vehicles. These systems use telemetry systems and sensors embedded in the vehicle to implement various functions, such as cameras, radar sensors, lidar sensors, and global positioning system (GPS) sensors.

[0003] One challenge facing advanced driver assistance systems and autonomous driving systems is detecting road irregularities (e.g., speed bumps or potholes on the road ahead of the vehicle) so that appropriate action can be taken, such as slowing the vehicle. Attempts have been made to detect and recognize traffic indicators, such as road signs and traffic lights, as well as other moving vehicles, using image processing-based advanced driver assistance systems or autonomous driving systems. However, in addition to these easily identifiable and recognizable objects on or near the road, roads often present irregularities (e.g., speed bumps or potholes) that are not easily recognized and can pose a potential hazard to vehicles.

[0004] For example, various conventional methods for speed bump detection, such as those disclosed in US2015 / 0291177, JP2019021028, KR101491238, and KR101517695, rely to some extent on image data and image processing techniques, specifically the processing of image data captured by one or more cameras in a vehicle. A factor in the performance of techniques that rely on image data and image processing is the quality of the image data. Therefore, these techniques may fail, for example, during nighttime driving or when there is reverberation on the road. Summary of the Invention

[0005] An object of the present invention is to provide an improved apparatus, system and method for detecting road irregularities, such as speed bumps or potholes on the road.

[0006] The above and other objects are achieved by the subject matter claimed in the independent claims. Other implementations are apparent from the dependent claims, the description and the drawings.

[0007] According to a first aspect, a sensing device for detecting speed bumps or potholes on a road is provided. The sensing device includes a telemetry sensing system configured to collect a plurality of position vectors through telemetry sensing of a road surface, wherein each position vector extends from a common origin to a corresponding point on the road surface, and each position vector has a length (i.e., its norm) and a direction. The sensing device also includes processing circuitry configured to detect road roughness anomalies by evaluating the lengths of the position vectors. Thus, an improved device for detecting road irregularities, such as speed bumps or potholes, is provided because, unlike conventional methods that rely on image data and process such image data, the performance of the sensing device according to the first aspect is not negatively impacted by conditions that result in poor image quality, such as those encountered during nighttime driving or in the presence of reverberation on the road. In one implementation, the telemetry sensing system may include one or more radar sensors and / or one or more lidar sensors that collect the plurality of position vectors using radar and / or lidar measurements.

[0008] In another possible implementation manner of the first aspect, the direction of each position vector includes a polar angle, that is, an elevation angle and an azimuth angle.

[0009] In another possible implementation of the first aspect, the processing circuit is used to evaluate the length of the position vector by (a) evaluating the deviation of the length of the position vector relative to a reference length corresponding to an assumed flat road surface, or (b) evaluating the change in the length of the position vector.

[0010] In another possible implementation manner of the first aspect, evaluating the change in the length of the position vector includes: evaluating a directional derivative of a length function, where the length function is an interpolation of the length of the position vector in the angular domain.

[0011] In another possible implementation of the first aspect, evaluating the change in the length of the position vector includes: evaluating the mathematical expression

[0012]

[0013] Where ρ represents the length function, θ represents the azimuth angle, and φ represents the polar angle.

[0014] In another possible implementation manner of the first aspect, collecting the multiple position vectors includes: converting each of the position vectors from Cartesian coordinates to spherical coordinates.

[0015] In another possible implementation manner of the first aspect, the telemetry sensing system is used to collect multiple position vectors, so that multiple subsets of the multiple position vectors have the same polar angle.

[0016] In another possible implementation of the first aspect, the processing circuit is further configured to determine lengths of the plurality of interpolation vectors based on an interpolation of lengths of one or more position vectors having larger polar angles and lengths of one or more position vectors having smaller polar angles.

[0017] In another possible implementation of the first aspect, the processing circuit is used to generate a two-dimensional data array, i.e., an image representation of the data, based on multiple position vectors and multiple interpolation vectors, wherein the dimension of each element of the data array corresponds to the azimuth and polar angle of the corresponding position vector or interpolation vector, and wherein the value of each element of the data array is associated with a change in a length function of the corresponding azimuth and polar angle.

[0018] In another possible implementation manner of the first aspect, the processing circuit is further configured to perform grayscale conversion on the two-dimensional data array.

[0019] In another possible implementation of the first aspect, the processing circuit is configured to detect the road roughness anomaly based on a contour detection algorithm for detecting contours in a two-dimensional data array.

[0020] According to a second aspect, an advanced driver assistance system for a vehicle is provided. The advanced driver assistance system according to the second aspect comprises the perception device according to the first aspect, wherein the advanced driver assistance system is configured to generate a warning message or signal when the perception device detects a speed bump or pothole on a road ahead of the vehicle.

[0021] According to a third aspect, a perception method for detecting speed bumps or potholes on a road is provided. The perception method comprises the step of collecting a plurality of position vectors by telemetric perception of the road surface, wherein each position vector extends from a common origin to a corresponding point on the road surface and each position vector has a length and a direction. Furthermore, the perception method comprises the step of detecting anomalies in the flatness of the road by evaluating the length of the position vector. Thus, an improved method for detecting road irregularities such as speed bumps or potholes on a road is provided, because, unlike conventional methods that rely on image data and processing such image data, the performance of the perception device according to the first aspect is not negatively affected by situations that lead to poor image quality, such as those encountered when driving at night or when there is reverberation on the road.

[0022] The perception method according to the third aspect of the present invention can be performed by the perception device according to the first aspect of the present invention and the advanced driver assistance system according to the second aspect of the present invention. Therefore, the other features of the perception method according to the third aspect of the present invention are directly derived from the functionality of the perception device according to the first aspect of the present invention and / or the advanced driver assistance system according to the second aspect of the present invention, as well as their different implementations described above and below.

[0023] One or more embodiments will be described in detail in the accompanying drawings and the following description. Other features, objects, and advantages will be apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Figure 1 is a schematic diagram of a perception device according to an embodiment, the perception device being implemented as part of an advanced driver assistance system of a vehicle.

[0026] Figure 2 is a schematic diagram of a processing block implemented by a sensing device according to an embodiment.

[0027] Figure 3a FIG. 4 shows a plurality of position vectors perceived by a perception device according to an embodiment in the form of a point cloud.

[0028] Figure 3b Shown for obtaining Figure 3a An exemplary road scene with multiple position vectors is shown in FIG.

[0029] Figure 3c Shown Figure 3a A more detailed view of the multiple position vectors in .

[0030] Figure 4 The relationship between Cartesian coordinates and spherical coordinates or polar coordinates used by a perception device according to an embodiment is shown.

[0031] Figure 5a shows the use of Cartesian coordinates Figure 3c A more detailed view of the data shown.

[0032] Figure 5b shows the use of spherical coordinates Figure 3c A more detailed view of the data shown.

[0033] Figure 5c Shown Figure 5b A more detailed view of the data shown, in which signatures of exemplary road irregularities identified by a perception device according to an embodiment have been marked.

[0034] Figure 6a An intensity map based on multiple position vectors acquired by multiple radar and / or lidar sensors of a perception device operating in a rotational mode according to an embodiment is shown.

[0035] Figure 6b The sensing device according to the embodiment is shown Figure 6a The intensity map of is obtained using interpolation.

[0036] Figure 7a Shown based on Figure 6b The intensity map of the first directional derivative of the intensity map.

[0037] Figure 7b Shown based on Figure 6b The intensity map of the second directional derivative of the intensity map.

[0038] Figure 7c Shown based on Figure 7a The intensity map and Figure 7b The intensity map of the combination of the intensity maps.

[0039] Figure 8a Shown Figure 7c Gray-converted version of the intensity map.

[0040] Figure 8b Shown Figure 8a and an intensity map of the image, and contours identified therein by a perception device according to an embodiment.

[0041] Figure 9 is a schematic diagram of a processing block implemented by a sensing device according to an embodiment.

[0042] Figures 10a to 10c Various aspects of sensing devices utilizing one or more reference lengths according to embodiments are shown.

[0043] Figure 11 is a schematic diagram of a processing block implemented by a sensing device according to an embodiment.

[0044] Figure 12 is a flow chart of different steps of a perception method according to an embodiment.

[0045] In the following figures, identical reference numerals denote identical or at least functionally equivalent features. DETAILED DESCRIPTION

[0046] In the following description, reference is made to the accompanying drawings which form a part of the present invention, which illustrate, by way of illustration, specific aspects of embodiments of the present invention or specific aspects in which embodiments of the present invention may be used. It is understood that embodiments of the present invention may be used in other aspects and include structural or logical variations not shown in the accompanying drawings. Therefore, the following detailed description is not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims.

[0047] For example, it should be understood that the disclosure relating to a described method also applies to a corresponding device or system for performing the method, and vice versa. For example, if one or more specific method steps are described, the corresponding device may include one or more units, such as functional units for performing the one or more method steps described (e.g., a unit that performs one or more steps; or multiple units, each of which performs one or more of the multiple steps), even if such one or more units are not explicitly described or shown in the accompanying drawings. In addition, for example, if one or more units (e.g., functional units) are used to describe a specific device, the corresponding method may include a step for performing the function of the one or more units (e.g., a step that performs the function of the one or more units; or multiple steps, each of which performs the function of one or more of the multiple units), even if such one or more steps are not explicitly described or shown in the accompanying drawings. In addition, it should be understood that unless otherwise expressly stated, the features of the various exemplary embodiments and / or aspects described herein may be combined with each other.

[0048] Figure 1 is a schematic diagram of a perception device 103 implemented as part of an advanced driver assistance system 101 of a vehicle 100, according to an embodiment. As will be described in detail below, the perception device 103 is used to detect speed bumps, potholes, or other types of road irregularities on a road on which the vehicle 100 is traveling. To this end, the perception device 103 includes processing circuitry 105, such as one or more processors 105, and a telemetry perception system 107. In one embodiment, the telemetry perception system 107 may include one or more radar sensors 107 and / or one or more lidar sensors 107 for acquiring radar and / or lidar measurements of the vehicle's 100 environment.

[0049] As will be described in detail below, the telemetry sensing system 107 (e.g., one or more radar and / or lidar sensors 107) is used to collect a plurality of position vectors of the road surface on which the vehicle 100 is traveling through telemetry sensing (e.g., radar and / or lidar sensing). Each position vector extends from a common origin defined by the telemetry sensing system 107 (e.g., the location of one or more radar and / or lidar sensors 107 on the vehicle 100) to a corresponding point on the road surface, wherein each position vector has a length and a direction. Based on these position vectors collected by the telemetry sensing system 107, the processing circuit 105 of the sensing device 103 is used to detect road roughness anomalies by evaluating the length of the position vector, which will be detected in the Figure 2In one embodiment, the processing circuit 105 of the perception device 103 is configured to estimate the length of the position vector by (a) evaluating a deviation of the length of the position vector from a reference length corresponding to an assumed flat road surface, or (b) evaluating a change in the length of the position vector.

[0050] Figure 2 is a schematic diagram showing a processing block implemented by the sensing device 103 according to an embodiment. Figure 2 In the exemplary embodiment shown, the telemetry sensing system 107 includes one or more lidar sensors 107 .

[0051] exist Figure 2 In block 201, one or more lidar sensors 107 collect multiple position vectors by performing lidar sensing on the road on which the vehicle 100 is traveling. Figure 3a As shown, multiple position vectors can be represented as a point cloud for Figure 3b An exemplary road scene is shown. Figure 3c Shown Figure 3a A more detailed view of multiple position vectors shown as a point cloud.

[0052] exist Figure 2 In the illustrated embodiment, it is assumed that one or more lidar sensors 107 collect multiple position vectors in Cartesian coordinates, i.e., the endpoints of each position vector are defined by its coordinates x, y and z relative to a common origin, for example, the common origin is defined by the position of one or more lidar sensors 107 on the vehicle 100. Figure 3a and Figure 3c The LiDAR data shown is given in a Cartesian coordinate system. Applying a zoom to the center portion of the data, i.e. focusing on the immediate vicinity of the vehicle 100 (i.e. where the LiDAR sensor 107 is placed), can be Figure 3a and 3c Circular limitations corresponding to the natural scanning process performed by the lidar sensor 107 are observed in FIG. In free space at each elevation angle value, in the absence of obstacles or road irregularities, that is, if the road is perfectly flat, the lidar sensor 107 will return a regular circle or regular arcs corresponding to a perfect road. However, if obstacles are placed along the line of sight of the lidar sensor 107, or if there are irregular structures on the road (such as speed bumps or potholes), these arcs will no longer be regular. Distortions in the center of the circle formed by the arcs correspond to bumps (obstacles), while distortions in the other direction (outside the regular circle) will represent potholes.

[0053] In one embodiment, the sensing device 103 is configured to convert each of the plurality of position vectors from Cartesian coordinates to spherical coordinates ( Figure 2 203). Figure 4 The general relationship between Cartesian coordinates and spherical or polar coordinates used by the perception device 100 according to an embodiment is shown. Thus, after conversion to spherical coordinates, each of the plurality of position vectors can be described by a radial distance ρ and a direction defined by a polar angle (i.e., an elevation angle φ and an azimuth angle θ).

[0054] Figures 5a to 5c The advantageous effect of converting a plurality of position vectors from Cartesian coordinates to spherical coordinates is shown. Figure 5a shows the use of Cartesian coordinates Figure 3c A more detailed view of the data shown, while Figure 5b A more detailed view of the same data using spherical coordinates is shown. Figure 5b A more detailed view of the data shown is in Figure 5c , where the locations of four exemplary road irregularities (e.g., speed bumps or potholes) that can be identified by the perception device 103 according to an embodiment have been marked. As will be understood, in one embodiment, the perception device 103 according to an embodiment advantageously utilizes the discovery that these road irregularities are not detected using spherical coordinates (i.e., Figure 5b and 5c ) is more accurate than in the Cartesian coordinate representation (i.e. Figure 5a ) is more prominent in the expression.

[0055] Return Reference Figure 2 As shown in blocks 205a and 205b, in order to detect road roughness anomalies by evaluating the changes in the lengths of the plurality of position vectors, the processing circuit 105 of the sensing device 103 is configured to determine two entropy-related quantities, denoted herein as and respectively, calculated based on the two dimensions θ and φ. and In one embodiment, these entropy-related quantities may be defined as follows: and in, and are the partial derivatives of ρ(θ,φ) with respect to φ and θ, respectively, as will be shown below Figure 9 Detailed description in the context of .

[0056] exist Figure 2 In block 207, in order to evaluate the change in the lengths of the plurality of position vectors, the processing circuit 105 of the sensing device 103 is configured to combine the lengths of the plurality of position vectors by calculating Figure 2 The entropy-related function determined in blocks 205a and 205b is and The result of the function To determine the entropy change level of each position vector. In one embodiment, as will be described below Figure 9As described in detail in the context of , the processing circuit 105 of the sensing device 103 is used to determine the following function:

[0057]

[0058] Among them, || represents the absolute value, and this function is used to merge Figure 2 The entropy-related function determined in blocks 205a and 205b is and

[0059] In one embodiment, the one or more radar and / or lidar sensors 107 of the telemetry perception system 107 may be configured to collect the plurality of position vectors in a rotational mode of operation, i.e., wherein the one or more radar and / or lidar sensors 107 scan the entire range of azimuth angles θ at a plurality of fixed polar angles (i.e., elevation angles φ). In other words, in one embodiment, the one or more radar and / or lidar sensors 107 may be configured to collect the plurality of position vectors such that a plurality of subsets of the plurality of position vectors have the same polar angle.

[0060] Figure 6a An exemplary intensity map based on multiple position vectors is shown, the multiple position vectors being acquired by one or more radar and / or lidar sensors 107 of perception device 103 operating in a rotational mode according to an embodiment. To process the multiple position vectors acquired using the rotational mode of operation, processing circuitry 105 of perception device 103 may be configured to determine additional position vectors by interpolating the lengths of the position vectors acquired by one or more radar and / or lidar sensors 107. In one embodiment, processing circuitry 105 is configured to determine the lengths of the multiple interpolated vectors, i.e., the interpolated position vectors, based on an interpolation of the lengths of one or more position vectors having a larger polar angle and the lengths of one or more position vectors having a smaller polar angle. Figure 6b The sensing device 100 according to the embodiment is shown Figure 6a The intensity map of is obtained using interpolation, i.e. a two-dimensional data array. As will be understood, in Figure 6a and 6b In the intensity map shown, the x-axis corresponds to the azimuth angle, the y-axis corresponds to the polar angle of each position vector, and the intensity value is associated with the radial distance of the position vector extending in the direction defined by the azimuth angle and the polar angle.

[0061] In one embodiment, evaluating the change in the length of the position vector includes evaluating a directional derivative of a length function, wherein the length function is an interpolation of the length of the position vector over the angular domain.

[0062] Figure 7a Shown Figure 2 The sensing device 103 in the processing block 205a is based on Figure 6bThe intensity map shown generates an intensity map, while Figure 7b Shown Figure 2 The sensing device 103 in the processing block 205b is based on Figure 6b More specifically, in this embodiment, the processing circuit 105 is used to calculate the function That is, use the first directional derivative to generate Figure 7a The intensity map shown and the function is calculated That is, use the second directional derivative to generate Figure 7b Intensity map shown. Figure 7c Shown based on Figure 7a The intensity map and Figure 7b The intensity map of the combination of the intensity maps of the position vectors is a function calculated by the processing circuit 105 of the perception device 103 for evaluating the change in the length of the plurality of position vectors.

[0063] Return Reference Figure 2 In block 209, the processing circuit 105 of the sensing device 103 is used to Figure 7c The two-dimensional intensity map shown performs a grayscale conversion. Figure 8a Shown Figure 7c Gray-converted version of the intensity map.

[0064] exist Figure 2 In block 211 of FIG. 1 , the processing circuit 105 of the sensing device 103 is configured to detect a road roughness anomaly by (a) evaluating a deviation of the length of the position vector from a reference length corresponding to an assumed flat road surface, or (b) evaluating a change in the length of the position vector. In one embodiment, the processing circuit 105 is configured to detect a road roughness anomaly based on a known contour detection algorithm, which is configured to detect a road roughness anomaly. Figure 8a Contours in the 2D intensity map are shown. Figure 8b Shown Figure 8a and the contours identified therein by the perception device 100 according to an embodiment.

[0065] exist Figure 2 In block 213 , the processing circuit 105 of the perception device 103 may convert the position of any detected road irregularity from spherical coordinates back to Cartesian coordinates.

[0066] exist Figure 2In box 215, the perception device 103 can trigger the advanced driver assistance system 101 to prompt the driver of the vehicle 100 about the detected road irregularity. For example, when the perception device 103 detects a speed bump or pothole on the road ahead of the vehicle, a prompt message or signal is generated so that the driver of the vehicle 100 and / or the advanced driver assistance system 101 can take appropriate measures to avoid the road irregularity, such as slowing down the vehicle 100.

[0067] Figure 9 is a schematic diagram of a processing block implemented by the sensing device 100 according to an embodiment of the present invention. Figure 2 Therefore, in the following, only the embodiments shown will be described in detail. Figure 2 and Figure 9 The difference between the embodiments shown, namely Figure 9 Processing blocks 905a, 905b, and 907 are provided.

[0068] exist Figure 9 In blocks 905a and 905b of FIG. 1 , in order to detect road roughness anomalies by evaluating changes in the lengths of a plurality of position vectors, the processing circuit 105 of the sensing device 103 uses the following entropy-related quantities: and in, and are the partial derivatives of ρ(θ,φ) with respect to φ and θ, respectively.

[0069] exist Figure 9 In block 907 , to evaluate the change in length of the plurality of position vectors, the processing circuit 105 of the sensing device 103 is configured to determine an entropy change level for each of the position vectors by calculating the following function:

[0070]

[0071] Its merger Figure 9 The entropy-related function determined in blocks 905a and 905b of FIG.

[0072] As described above, the processing circuit 105 of the perception device 103 can be configured to estimate the length of the position vector by (a) evaluating the deviation of the length of the position vector from a reference length corresponding to an assumed flat road surface, or (b) evaluating the change in the length of the position vector. While the above-described embodiment describes the processing circuit 105 of the perception device 103 as being configured to evaluate the change in the length of the position vector for detecting road irregularities, the following embodiment will describe the case where the processing circuit 105 of the perception device 103 is configured to evaluate the deviation of the length of the position vector from a reference length corresponding to an assumed flat road surface for detecting road irregularities.

[0073] In one embodiment, the reference length may be based on the horizontal plane formed by the wheels of the vehicle (ie Figure 10a The reference length can be the position vector on the assumed flat road surface, that is, Figure 10a The points correspond to the intersections of the ray generated at (θ, φ) with the xy plane (i.e., a hypothetical flat road surface). To acquire these reference points, perception device 103 can be operated in vehicle 100 while it is traveling or stationary on a flat, level road. Averaging over several passes will help reduce measurement noise. These reference points can then be stored in the memory of perception device 103.

[0074] According to another embodiment, the sensing device 103 itself is used to generate the reference points. In fact, as long as the arrangement of one or more lidar sensors 107 on the vehicle 100 is known, it is possible to determine the measurement values ​​when the vehicle is driving or stopping on a hypothetical flat road. For example, these reference points can be stored in the memory of the sensing device 103 in the form of a three-dimensional matrix that associates a reference radial distance with each direction (i.e. (θ, φ)). In a Cartesian coordinate system, multiple reference points will be defined as follows Figure 10a , whereas in spherical coordinates, multiple reference points would define a straight horizontal line, such as Figure 10b (front view) and Figure 10c (Side view) shown.

[0075] Figure 11 is a schematic diagram of a processing block implemented by the perception device 103 according to another embodiment, wherein, when reference points are available, the reference points may be considered to detect speed bumps and / or potholes on the road.

[0076] exist Figure 11 In block 1101 of , measurements (ie, a plurality of position vectors) are provided in spherical coordinates.

[0077] exist Figure 11 In block 1103 , the processing circuit 105 of the sensing device 103 determines whether any suitable reference points are available.

[0078] If this is the case, the sensing device 103 will use the stored reference value ( Figure 11 1105) to evaluate the deviation of the length of the measured position vector relative to the reference length ( Figure 11 Step 1107).

[0079] If the reference data is not available, the perception device 103 determines the change in the length of the measured position vector for detecting the road irregularity ( Figure 11, as described in detail in the context of the embodiments described above.

[0080] exist Figure 11 In block 1111 , the perception device 103 determines the location of any road irregularities based on the output provided by block 1107 or block 1109 .

[0081] Figure 12 1 is a flow chart illustrating different steps of a perception method 1200 according to an embodiment. The perception method 1200 includes the steps of collecting (1201) a plurality of position vectors by telemetric perception of a road surface, wherein each position vector extends from a common origin to a corresponding point on the road surface and each position vector has a length and a direction; and detecting (1203) road roughness anomalies by evaluating the lengths of the position vectors.

[0082] Those skilled in the art will understand that the "boxes" ("units") of the various figures (methods and devices) represent or describe the functions of an embodiment of the present invention (and not necessarily a single "unit" in hardware or software), thereby equivalently describing the functions or features of the device embodiments and the method embodiments (unit = step).

[0083] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the embodiments of the devices described are merely exemplary. For example, the unit division is merely a logical function division, and in actual implementation, it may be another division. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not performed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be achieved by using some interfaces. The direct coupling or communication connection between devices or units can be achieved by electronic, mechanical or other means.

[0084] Units described as discrete components may or may not be physically separate, and components shown as units may or may not be physical units, and may be located in one location or distributed across multiple network units. Some or all of the units may be selected based on actual needs to achieve the objectives of the embodiments.

[0085] In addition, the functional units in the embodiments of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit.

Claims

1. A sensing device for detecting bumps or potholes on a road, characterized in that: The sensing device comprises: a telemetry sensing system for collecting a plurality of position vectors by telemetry sensing of a road surface, each position vector extending from a common origin to a corresponding point on the road surface, each position vector having a length and a direction; processing circuitry for detecting a road roughness anomaly by evaluating a length of the position vector; The evaluating the length of the position vector includes evaluating a deviation of the length of the position vector relative to a reference length corresponding to an assumed flat road surface, or evaluating a change in the length of the position vector.

2. The sensing device according to claim 1, characterized in that The direction of each position vector includes a polar angle and an azimuth angle.

3. The sensing device according to claim 1 or 2, characterized in that: Evaluating the change in the length of the position vector includes evaluating a directional derivative of a length function, the length function being an interpolation of the length of the position vector over an angular domain.

4. The sensing device according to claim 3, characterized in that: Evaluating the change in the length of the position vector includes evaluating ， in, represents the length function, represents the azimuth, Indicates the polar angle.

5. The sensing device according to claim 1, characterized in that: Collecting the plurality of position vectors includes converting each of the position vectors from Cartesian coordinates to spherical coordinates.

6. The sensing device according to claim 3, characterized in that: The telemetry sensing system is used to collect the multiple position vectors so that multiple subsets of the multiple position vectors have the same polar angle.

7. The sensing device according to claim 6, characterized in that: The processing circuit is further configured to determine lengths of a plurality of interpolated vectors based on an interpolation of lengths of one or more position vectors having a larger polar angle and lengths of one or more position vectors having a smaller polar angle.

8. The sensing device according to claim 7, characterized in that: The processing circuit is used to generate a two-dimensional data array based on the multiple position vectors and the multiple interpolation vectors, wherein the dimension of each element of the data array corresponds to the azimuth and polar angle of the corresponding position vector or interpolation vector, and wherein the value of each element of the data array is associated with the change of the length function of the corresponding azimuth and polar angle.

9. The sensing device according to claim 8, characterized in that: The processing circuit is further configured to perform grayscale conversion on the two-dimensional data array.

10. The sensing device according to claim 8 or 9, characterized in that: The processing circuit is configured to detect the road roughness anomaly based on a contour detection algorithm for detecting contours in the two-dimensional data array.

11. The sensing device according to any one of claims 1, 2 or 4 to 9, characterized in that: The telemetry perception system includes one or more radar sensors or one or more lidar sensors.

12. An advanced driver assistance system for a vehicle, characterized in that The advanced driver assistance system comprises a perception device according to any one of the preceding claims, the advanced driver assistance system being configured to generate a warning message or signal if the perception device detects a speed bump or a pothole on the road in front of the vehicle.

13. A sensing method for detecting bumps or potholes on a road, characterized in that: The sensing method includes: collecting a plurality of position vectors by telemetrically sensing a road surface, each position vector extending from a common origin to a corresponding point on the road surface, each position vector having a length and a direction; detecting road roughness anomalies by evaluating the length of the position vector; The evaluating the length of the position vector includes evaluating a deviation of the length of the position vector relative to a reference length corresponding to an assumed flat road surface, or evaluating a change in the length of the position vector.

14. The sensing method according to claim 13, characterized in that: The direction of each position vector includes a polar angle and an azimuth angle.

15. The sensing method according to claim 13 or 14, characterized in that: Evaluating the change in the length of the position vector includes evaluating a directional derivative of a length function, the length function being an interpolation of the length of the position vector over an angular domain.

16. The sensing method according to claim 15, characterized in that: Evaluating the change in the length of the position vector includes evaluating ， in, represents the length function, represents the azimuth, Indicates the polar angle.

17. The sensing method according to claim 13, characterized in that: Collecting the plurality of position vectors includes converting each of the position vectors from Cartesian coordinates to spherical coordinates.

18. The sensing method according to claim 15, characterized in that: A plurality of subsets of the plurality of position vectors have the same polar angle.

19. The sensing method according to claim 18, characterized in that: The lengths of the plurality of interpolation vectors are determined based on interpolation of the lengths of one or more position vectors having a larger polar angle and the lengths of one or more position vectors having a smaller polar angle.

20. The sensing method according to claim 19, characterized in that: A two-dimensional data array is generated based on the multiple position vectors and the multiple interpolation vectors, wherein the dimension of each element of the data array corresponds to the azimuth and polar angle of the corresponding position vector or interpolation vector, and wherein the value of each element of the data array is associated with the change of the length function of the corresponding azimuth and polar angle.

21. The sensing method according to claim 20, characterized in that: Grayscale conversion is performed on the two-dimensional data array.

22. The sensing method according to any one of claims 20 or 21, characterized in that: The road roughness anomaly is detected based on a contour detection algorithm for detecting contours in the two-dimensional data array.

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