Method and apparatus for installing wayside monitoring devices

Abstract

A method and apparatus for installing a wayside monitoring device are disclosed. The method includes receiving at least one set of sensed data from at least one sensor in the wayside monitoring device, each set of sensed data corresponding to a sensing region of a respective one of the at least one sensor, the sensing region comprising a plurality of sub-regions; dividing the set of sensed data into a plurality of subsets of sensed data, each subset of sensed data corresponding to a respective one of the plurality of sub-regions; and obtaining a parameter value for the sub-region based on the subset of sensed data for the sub-region, the parameter value relating to an environmental characteristic within the sub-region.

Description

Technical Field

[0001] This invention relates to traffic environment monitoring technology, and more specifically to methods and apparatus for installing roadside monitoring equipment. Background Technology

[0002] With the rapid development of intelligent transportation systems, there is a need to install a large number of roadside monitoring devices in various road environments to collect traffic environment information. For example, millimeter-wave radar sensors are currently widely used to monitor road users and other road users to provide information such as the location and distance of the monitored objects. In order to collect information within the desired monitoring area, roadside monitoring devices need to be installed in appropriate locations. However, because existing roadside monitoring devices do not provide installers with feedback or instructions to assist in installation, accurately installing such devices is extremely difficult for installers.

[0003] Currently, installers typically rely on empirical data provided by roadside monitoring equipment manufacturers. This empirical data consists of installation parameters, such as installation height and pitch angle, obtained by the manufacturers through measurements in specific test environments. However, actual traffic environments can differ significantly from test environments. For example, infrastructure and tree branches in real traffic environments may obstruct the test signals of roadside monitoring equipment or generate noise interference, making the empirical data used for installing roadside monitoring equipment inapplicable to various traffic conditions.

[0004] To properly install roadside monitoring equipment, some existing technologies require the temporary placement of signal generators at different locations along the actual traffic flow. Installers can adjust the installation position of the roadside monitoring equipment based on the signals emitted by these generators at different locations, as measured by the monitoring device itself. However, this method disrupts normal traffic flow, demands a high level of expertise from installers, and significantly increases the installation cost of the roadside monitoring equipment.

[0005] Therefore, a convenient and efficient technology is needed to assist installers in installing roadside monitoring equipment in suitable locations. Summary of the Invention

[0006] This invention is not intended to identify key or essential features of the protected subject matter, nor is it intended to limit the scope of the protected subject matter. The invention will be further described in the following detailed description.

[0007] In one aspect, this disclosure provides a method for installing a roadside monitoring device according to one embodiment. The roadside monitoring device includes one or more sensors. The method includes: receiving at least one sensing dataset from at least one of the one or more sensors, each sensing dataset corresponding to a sensing area of ​​one of the at least one sensors, the sensing area including a plurality of sub-regions; dividing the sensing dataset into a plurality of sensing data subsets, each sensing data subset corresponding to one of the plurality of sub-regions; and obtaining parameter values ​​for the sub-region based on the sensing data subset of the sub-region, the parameter values ​​being related to environmental characteristics within the sub-region.

[0008] In another aspect, this disclosure also provides an apparatus for installing a roadside monitoring device according to one embodiment. The roadside monitoring device includes one or more sensors. The apparatus includes at least one processor, a memory coupled to the at least one processor, and an input / output interface coupled to the at least one processor. The at least one processor is configured to: receive via the input / output interface at least one sensing dataset from at least one of the one or more sensors, each sensing dataset corresponding to a sensing area of ​​one of the at least one sensors, the sensing area including multiple sub-regions; divide the sensing dataset into multiple sensing data subsets, each sensing data subset corresponding to one of the multiple sub-regions; and obtain parameter values ​​for a sub-region based on the sensing data subset of the sub-region, the parameter values ​​being related to environmental characteristics within the sub-region.

[0009] In another aspect, this disclosure also provides a computer-readable storage medium according to one embodiment. The computer-readable storage medium stores computer-executable code for performing the following methods: receiving at least one sensing dataset from at least one of one or more sensors, each sensing dataset corresponding to a sensing area of ​​one of the at least one sensors, the sensing area comprising a plurality of sub-regions; dividing the sensing dataset into a plurality of sensing data subsets, each sensing data subset corresponding to one of the plurality of sub-regions; and obtaining parameter values ​​for a sub-region based on the sensing data subset of the sub-region, the parameter values ​​being related to environmental features within the sub-region.

[0010] In another aspect, this disclosure also provides a computer program product according to one embodiment. The computer program product includes instructions executable by a processor to implement the following methods: receiving at least one sensing dataset from at least one of one or more sensors, each sensing dataset corresponding to a sensing area of ​​one of the at least one sensors, the sensing area comprising a plurality of sub-regions; dividing the sensing dataset into a plurality of sensing data subsets, each sensing data subset corresponding to one of the plurality of sub-regions; and obtaining parameter values ​​for a sub-region based on the sensing data subset of the sub-region, the parameter values ​​being related to environmental features within the sub-region.

[0011] It should be noted that one or more of the above aspects include the detailed description below and the features specifically recited in the claims. The following description and drawings set forth some exemplary features in detail from multiple aspects. These features merely indicate various ways in which the principles of each aspect can be implemented, and this disclosure is intended to include all such aspects and their equivalents. Attached Figure Description

[0012] Figure 1A A flowchart illustrating an exemplary method for installing a roadside monitoring device according to an embodiment of the present disclosure is shown.

[0013] Figure 1B A flowchart illustrating an exemplary method for installing roadside monitoring equipment according to another embodiment of this disclosure is shown.

[0014] Figure 2A A schematic diagram of the sensor's three coordinate axes in a spatial rectangular coordinate system is shown.

[0015] Figure 2B A schematic diagram of one embodiment of dividing the sensing area along the coordinate axes of the sensor is shown.

[0016] Figure 2C A schematic diagram of another embodiment showing the division of the sensing area along the coordinate axes of the sensor is shown.

[0017] Figure 3 A schematic diagram of a point cloud-based method according to an embodiment of the present disclosure is shown.

[0018] Figure 4 A schematic diagram of a roadside monitoring device including one or more sensors is shown.

[0019] Figure 5 A system block diagram for installing roadside monitoring equipment according to an embodiment of the present disclosure is shown. Detailed Implementation

[0020] The various embodiments will now be described in detail with reference to the accompanying drawings. It should be understood that the description of these specific embodiments is merely intended to enable those skilled in the art to better understand and implement the present disclosure, and is not intended to limit the scope of the present disclosure in any way.

[0021] Figure 1A An exemplary method 100 for installing a roadside monitoring device according to an embodiment of this disclosure is illustrated. The roadside monitoring device may include only one monitoring sensor (e.g., millimeter-wave radar, lidar, etc.) or multiple monitoring sensors (e.g., any combination of one or more of millimeter-wave radar, lidar, and cameras, etc.). Through the fusion of multiple sensors, the roadside monitoring device can provide rich and accurate monitoring information in a variety of environments. For example, millimeter-wave radar has a long detection range and is unaffected by weather and light. LiDAR can provide dense point cloud information and higher measurement accuracy. Cameras can provide rich imaging information for identifying object categories through computational analysis. When the roadside monitoring device includes multiple sensors, these sensors can be pre-fixed in a connecting structure for installation as a whole, or they can be moved within the range provided by the connecting structure to adjust their relative positions. These sensors can also be independent of each other and installed directly on both sides of the road by installers.

[0022] Before implementing method 100, the installers first need to roughly determine the installation location of the roadside monitoring equipment based on the target area in the environment to be monitored (e.g., motor vehicle roads, non-motor vehicle roads, etc.), install the roadside monitoring equipment at the initially determined location, and start the roadside monitoring equipment. For example, based on the specific environment, installation specifications, and subjective judgment, the installers can install the monitoring equipment on an overpass or pedestrian bridge directly facing the center of the road, or on a roadside pole bracket specifically designed for installing monitoring equipment on both sides of the road.

[0023] Next, the installer can adjust the installation location of the roadside monitoring device with the assistance of method 100. The roadside monitoring device may include one or more sensors. Adjusting the installation location of the roadside monitoring device includes adjusting the position of one or more sensors in the roadside monitoring device. By using method 100 to install the roadside monitoring device, the roadside monitoring device can more accurately cover the area to be monitored, such as road areas in the traffic environment and other areas where traffic participants may be active, thereby making the function of the roadside monitoring device more efficient. In this document, the area in the environment that needs to be monitored is also referred to as the target area.

[0024] Reference Figure 1AIn block 110, method 100 includes receiving at least one sensing dataset from at least one of one or more sensors. In one embodiment, the roadside monitoring device may include only one sensor, such as a millimeter-wave radar. Method 100 may include receiving a sensing dataset from the millimeter-wave radar. The sensing dataset output by the millimeter-wave radar may be received via an existing software program (e.g., CANape) connected to the millimeter-wave radar. The sensing dataset corresponds to the sensing area of ​​the millimeter-wave radar. The sensing area of ​​the millimeter-wave radar includes a fan-shaped area within the field of view (FOV). Different radars may have different sensing areas. For example, the FOV of a short-range radar may reach 120°, while the FOV of a long-range radar may be only 20°. The sensing dataset of the millimeter-wave radar may include echo intensity, radial velocity, velocity spectrum width, elevation angle, azimuth angle, time, etc., and may also include information such as the location of reflecting points or reflecting objects within its sensing area.

[0025] In another embodiment, the roadside monitoring device may include multiple sensors of the same or different types. For example, the roadside monitoring device may include a millimeter-wave radar and a lidar. In this example, method 100 may receive only the sensing data set from the millimeter-wave radar, only the sensing data set from the lidar, or both the millimeter-wave radar and the lidar. The sensing data set from the millimeter-wave radar corresponds to the sensing area of ​​the millimeter-wave radar, and the sensing data set from the lidar corresponds to the sensing area of ​​the lidar.

[0026] In this invention, the sensing area of ​​each sensor may include multiple sub-regions. The sensing area of ​​the sensor can be divided into multiple sub-regions along one or more coordinate axes. The one or more coordinate axes used to divide the sub-regions may be directions that will generate noise or interference. These one or more coordinate axes may also be set by the installer. In one embodiment, these one or more coordinate axes may be one or more coordinate axes of the sensor corresponding to the sensing area. The following will combine... Figure 2A-2C The division of the sensing area is explained in detail.

[0027] Figure 2A The sensor's three coordinate axes in a Cartesian coordinate system are shown. (Example) Figure 2AAs shown, with the center of the sensor's front surface as the origin of the coordinate axes, the sensor's three coordinate axes correspond to the horizontal axis (x-axis), vertical axis (y-axis), and z-axis in the coordinate system, respectively. The x-axis and z-axis lie on the front plane of the sensor. Accordingly, the sensor can move left or right along the x-axis, forward or backward along the y-axis, or up or down along the z-axis. Furthermore, the sensor can also rotate (roll) along the x-axis, (pitch) along the y-axis, or (yaw) along the z-axis. In other words, the sensor can have six degrees of freedom.

[0028] Figure 2B A schematic diagram of one embodiment showing the division of the sensing region along the coordinate axes of the sensor is shown. Figure 2B As shown, the area formed by angle AOB is the sensor's sensing area. In this embodiment, the sensor's sensing area is divided into three sub-regions (sub-region 1, sub-region 2, and sub-region 3) along the sensor's x-axis using parallel lines perpendicular to the x-axis (as shown by the dashed lines in the figure). The sensor's sensing area can also be divided into other numbers of sub-regions. Installers can set the number of sub-regions according to the specific traffic environment.

[0029] Figure 2C A schematic diagram of another embodiment showing the division of the sensing area along the coordinate axes of the sensor is shown. Figure 2C As shown, the area formed by angle AOB is the sensor's sensing area. In this embodiment, the sensor's sensing area is divided into three sub-regions (sub-region 1, sub-region 2, and sub-region 3) along the sensor's x-axis using a ray with the origin O as its endpoint (as shown by the dashed line in the figure). The sensor's sensing area can also be divided into other numbers of sub-regions. The installer can set the number of sub-regions according to the specific traffic environment.

[0030] Since the sensing area of ​​the sensor can be a three-dimensional space, in other embodiments, the sensing area can be divided into multiple sub-regions along multiple coordinate axes of the sensor. Different coordinate axes can be used for different purposes, such as... Figure 2B Alternatively, the area can be divided as shown in 2C. For example, in traffic environments including multi-level roads such as overpasses, the sensing area can be divided along the sensor's x-axis and z-axis. On the x-axis, it can be divided according to... Figure 2C The illustrated embodiment divides the sensing area into three sub-regions, and on the z-axis, it can be based on... Figure 2B The illustrated embodiment divides the sensing area into two sub-regions, thereby dividing the entire sensing area of ​​the sensor into six sub-regions. The technical solutions disclosed herein are not limited to the specific sub-region division examples described. Installers can set the coordinate axes used to divide the sensing area and the number of sub-regions based on various factors such as traffic environment, installation conditions, and adjustment needs.

[0031] Back Figure 1A In block 120, method 100 includes dividing each received sensing dataset into multiple sensing data subsets. Each sensing data subset corresponds to each sub-region of the multiple sub-regions. In one embodiment, the received sensing dataset may include a first sensing dataset from a first sensor (e.g., millimeter-wave radar) and a second sensing dataset from a second sensor (e.g., lidar). Method 100 includes dividing the first sensing dataset into multiple sensing data subsets based on multiple sub-regions included in the first sensing region of the first sensor, each sensing data subset corresponding to one sub-region of the first sensing region. For example, in... Figure 2B In the embodiment shown in 2C, the sensing dataset corresponding to the sensing region can be divided into three sensing data subsets 1-3. Sensing data subset 1 includes sensing data for sub-region 1, sensing data subset 2 includes sensing data for sub-region 2, and sensing data subset 3 includes sensing data for sub-region 3. Method 100 further includes dividing the second sensing dataset into multiple sensing data subsets based on the multiple sub-regions included in the second sensing region of the second sensor, with each sensing data subset corresponding to one sub-region in the second sensing region.

[0032] The partitioning of the sensing dataset may include converting the position information of each reflection point or reflecting object in the sensing dataset into coordinates in a coordinate system used to partition the sensing area, and partitioning the sensing data into corresponding sub-regions based on the converted coordinates of the sensing data and the boundary coordinates of each sub-region, thereby generating a subset of sensing data corresponding to each sub-region.

[0033] In box 130, method 100 further includes obtaining parameter values ​​for each sub-region based on a subset of sensed data for each sub-region. These parameter values ​​are related to environmental features within each sub-region and can be used to determine whether each sub-region belongs to the target region. These environmental features can be traffic environment features or any other features of the environment in which the monitoring equipment is installed. These environmental features can reflect environmental noise in the traffic environment or the categories of various objects in the traffic environment. For example, traffic environment features can be used to broadly distinguish traffic participants, traffic infrastructure, buildings, trees, etc. Therefore, environmental features can include the motion characteristics, reflectivity, or a combination thereof of objects in the environment. For example, traffic participants on a road typically move at speeds within a certain range, while infrastructure and trees are essentially stationary. As another example, the materials of motor vehicles on the road typically have strong reflectivity, while the reflectivity of roadside trees and buildings is relatively weak.

[0034] Since the target area typically refers to a traffic road area with traffic participants, and traffic environment characteristics can reflect whether traffic participants are detected, the obtained parameter values ​​related to the traffic environment characteristics within a sub-region can be used to determine whether that sub-region belongs to the target area. For example, parameter values ​​include one or more of the following: the number of reflection points within the sub-region; the intensity of the reflection points within the sub-region; and the distance between reflection points within the sub-region. The technical solutions disclosed herein are not limited to the characteristics and parameter values ​​of the sensing object described herein. Sensing information related to traffic environment characteristics provided by various types of sensors is applicable to the technical solutions disclosed herein.

[0035] In one embodiment, the parameter values ​​for each sub-region obtained by method 100 can be used by the installer to adjust the installation position of the roadside monitoring device. For example, if the obtained parameter values ​​determine that a sub-region defined along the x-axis by the sensing area of ​​a sensor in the roadside monitoring device does not belong to the target monitoring area, the installer can avoid that sub-region by translating or rotating the sensor individually or the roadside monitoring device including the sensor as a whole in the x-axis direction. The installer can repeatedly adjust the parameter values ​​along different coordinate axes based on various factors such as traffic environment, installation conditions, and adjustment needs, in order to install the roadside monitoring device in a suitable location with minimal environmental noise.

[0036] Figure 1B An exemplary method 200 for installing roadside monitoring equipment according to another embodiment of this disclosure is illustrated. As described above, the parameter values ​​for each sub-region obtained in exemplary method 100 are directly used by the installer to install the roadside monitoring equipment. This requires the installer to have certain professional skills to understand the meaning of various parameter values ​​in order to determine whether the corresponding sub-region belongs to the target area, and is also subject to the influence of the installer's subjectivity to some extent, thus making it difficult to provide stable and reliable installation results. Figure 1B As shown in box 240, the main difference between exemplary method 200 and exemplary method 100 is that method 200 also includes providing an indication of whether each sub-region belongs to the target region. This indication can directly indicate to the installer whether the corresponding sub-region belongs to the target region, and can even directly indicate to the installer how to adjust the position of the sensor along the coordinate axis that divides the sub-region (e.g., move left, move right, rotate left, or rotate right, etc.). Figure 1B The boxes 210, 220, and 230 shown correspond to respectively Figure 1A Boxes 110, 120, and 130 are shown above. (The above refers to...) Figure 1A The description also applies to Figure 1B The embodiments shown will not be described in detail here.

[0037] According to some embodiments of this disclosure, method 200 may determine whether each sub-region belongs to the target region based on a comparison of the parameter value of each sub-region obtained in block 230 with a threshold or a comparison of the parameter values ​​of each sub-region with each other.

[0038] In one embodiment, method 200 may obtain, in block 230, the number of reflection points and / or statistical values ​​of reflected signal intensity in each sub-region based on a subset of sensing data for each sub-region, or the number of sensed objects and / or attributes of sensed objects (such as signal strength, motion speed, etc.) in the sub-region. For sensors with more advanced functions, reflection points may also refer to sensed objects or objects within the sub-region. Figure 3 As shown, the number of reflection points, signal strength, and reflection area of ​​each sub-region can be counted based on the point cloud map. This point cloud map can be generated using existing software tools, such as those provided by Robot Operating System (ROS) or CANape software. Figure 3 In the illustrated embodiment, the sensing area AOB is divided into three sub-regions along the x-axis. Method 100 can correspondingly divide the radar position point cloud data into three point cloud data subsets corresponding to each sub-region, and obtain parameter values ​​for each sub-region based on the point cloud data subsets, such as the number of point clouds, point cloud intensity, and point cloud reflective area. The point cloud intensity can be a mathematical statistical value (e.g., average) of the reflected signal intensity at each reflecting point in the sub-region, and the point cloud reflective area can be a mathematical statistical value (e.g., average) of the reflective area at each reflecting point in the sub-region. Table 1 below shows... Figure 3 The parameter values ​​for each sub-region obtained in the embodiment.

[0039]

[0040]

[0041] Table-1

[0042] Method 200 can determine whether a corresponding sub-region belongs to the target region based on the parameter values ​​in Table 1. For example, it can be determined that sub-region 1 does not belong to the target region based on the fact that the number of point clouds in sub-region 1 is below a predetermined threshold or a threshold configured by the installer. This is because if the number of reflection points in a sub-region is small, it means that there are fewer objects to be monitored in that sub-region, and therefore monitoring of that sub-region is unnecessary.

[0043] For example, it can be determined that sub-region 1 does not belong to the target area based on the point cloud intensity of sub-region 1 being lower than a predetermined threshold or a threshold configured by the installer. This is because motor vehicles, as major participants in traffic, typically have strong reflective properties, while the reflective properties of tree branches and leaves on both sides of the road are usually low. If the reflective point intensity in a sub-region is lower than the threshold, it means that the monitored object in that sub-region may not be a traffic participant, and therefore monitoring of that sub-region is unnecessary.

[0044] For example, it can be determined that sub-region 1 does not belong to the target area based on the significant difference between one or more parameter values ​​in sub-region 1 in Table 1 and the corresponding parameter values ​​in other sub-regions. This is because the installation location initially determined by the installer should ensure that the front of the sensor essentially faces the target area to be monitored. In other words, in the monitoring area of ​​the sensor in the roadside monitoring equipment after a preliminary rough installation, most of the monitoring area should belong to the target area, and only a small portion may be unsuitable for monitoring.

[0045] In this embodiment, if the indicated sub-region 1 does not belong to the target region, it can be along the z-axis (not shown, because...). Figure 3 (For a two-dimensional planar graph) rotated in the x-axis direction, so that sub-region 1 is no longer monitored. Figure 3 The point cloud map shown resembles a scene in an urban traffic environment where trees line the roads and vehicles are positioned in the middle. From... Figure 3 As can be seen, the current sensor installation position is biased to the left, causing trees and other objects on the left side of the road to be sensed within the middle sub-region 2. By adjusting the sensor position according to the instructions provided in this disclosure, such as rotating the sensor to the right in this example, the sensor can be adjusted to a suitable position, where the middle sensing area faces the road area precisely.

[0046] In another embodiment, method 200 may obtain statistical parameter values ​​related to the distance between reflection points within each sub-region based on a subset of sensing data for each sub-region, as shown in block 230. For example, these parameter values ​​could be the average, variance, etc., of the distance between the sensor and each reflection point within the sub-region. For sensors with more advanced capabilities, a reflection point could also refer to a sensed object or feature within the sub-region. Some statistical values ​​can be generated using existing software tools such as CANape. Table 2 below shows the distance-related parameter values ​​for each sub-region obtained in one embodiment.

[0047] Average value (m) Variance (m) Subregion 1 8.5 1.0 Subregion 2 3.0 15.2 Subregion 3 2.2 16.6

[0048] Table-2

[0049] Method 200 can determine whether a corresponding sub-region belongs to the target region based on the parameter values ​​in Table 2. For example, it can be determined that sub-region 1 does not belong to the target region based on the fact that the average distance of the reflection points of sub-region 1 is higher than a predetermined upper threshold or an upper threshold configured by the installer. Similarly, it can also be determined that sub-region 1 does not belong to the target region based on the fact that the average distance of the reflection points of a certain sub-region is lower than a predetermined lower threshold or a lower threshold configured by the installer. This is because roadside monitoring devices typically have their own specific uses. If a sensor in a roadside monitoring device is intended to monitor traffic participants within a specific distance range, and the average distance of the reflection points of a sub-region exceeds the threshold range, it means that the object being sensed is not within the specific distance range that is intended to be monitored, or that there is no object being sensed within the specific distance range of that sub-region, i.e., monitoring of that sub-region is unnecessary.

[0050] For example, sub-region 1 can be determined not to belong to the target area based on the variance of the distance between its reflection points and the target area being monitored being less than a predetermined threshold or a threshold configured by the installer. This is because traffic participants on the road being monitored are typically in motion and can be located at various points along the road. Therefore, the variance of the distance between traffic participants and the roadside monitoring equipment is usually large, while obstacles or noise sources that act as sensors in the traffic environment may be concentrated in a specific location. If the variance of the distance between the reflection points and the target area of ​​a sub-region is small, it means that the objects to be sensed within that sub-region are stationary, and monitoring of that sub-region is unnecessary.

[0051] For example, it can be determined that sub-region 1 does not belong to the target area based on the significant difference between one or more parameter values ​​in sub-region 1 in Table 2 and the corresponding parameter values ​​in other sub-regions. This is because the installation location initially determined by the installer should ensure that the front of the sensor essentially faces the target area to be monitored. In other words, in the monitoring area of ​​the sensor in the roadside monitoring equipment after a preliminary rough installation, most of the monitoring area should belong to the target area, and only a small portion may be unsuitable for monitoring.

[0052] According to other embodiments of this disclosure, method 200 can directly use the parameter values ​​of each sub-region obtained in block 230 as an indication to be provided to the installer. That is, the parameter values ​​obtained in block 230 by method 200 are an indication of whether each sub-region belongs to the target area. For example, method 200 can utilize artificial intelligence (AI) technology to directly obtain an indication of whether each sub-region belongs to the target area based on a subset of sensing data from each sub-region. To achieve this, a suitable machine learning model (e.g., a model suitable for anomaly detection) needs to be determined first, and the machine learning model is trained using a large amount of sensing data from the target area to obtain a trained machine learning model. This machine learning model can be used to determine whether the sensing data of a sub-region conforms to the traffic environment characteristics of the target area. In this example, method 200 can input the subset of sensing data from each sub-region into the trained machine learning model in block 230. The machine learning model can output an indication of whether the sub-region belongs to the target area. This disclosure is not limited to the specific embodiments described above. Based on the content of this disclosure, those skilled in the art can use other methods to determine whether a sub-region belongs to the target area.

[0053] Furthermore, method 200 may also include: receiving monitoring data from a camera to obtain the parameter values ​​or to determine the indication. In one embodiment, the monitoring data from the camera may be provided directly to the installer as an image via a display screen to determine whether the current monitoring area is suitable. Alternatively, the data collected by the camera may be analyzed using video or image processing methods to assist the installer's operation. In another embodiment, the monitoring data from the camera may be combined with sensing data from other sensors, such as millimeter-wave radar, to obtain parameter values ​​for each sub-region of the millimeter-wave radar, or to determine an indication regarding whether each sub-region of the millimeter-wave radar belongs to a target area. In yet another embodiment, intelligent image recognition technology may be used to determine an indication regarding whether each monitoring sub-region of the camera belongs to a target area based on the monitoring data from the camera.

[0054] After determining whether a sub-area belongs to the target area, method 200 can provide this indication to the installer for installing the roadside monitoring equipment. If the roadside monitoring equipment has appropriate mechanical construction to support automatic adjustment of the sensor's physical position and angle, method 200 can also provide this indication to the roadside monitoring equipment to automate and intelligentize the installation process.

[0055] In this disclosure, installing roadside monitoring equipment includes not only adjusting the location of the roadside monitoring equipment based on a pre-determined location, but also individually adjusting the location of one or more sensors in the roadside monitoring equipment.

[0056] Figure 4 A schematic diagram of a roadside monitoring device 400 including one or more sensors is shown. In one embodiment, such as Figure 4 As shown, the roadside monitoring device 400 may include a millimeter-wave radar 410, a lidar 420, and a camera 430. In another embodiment, the roadside monitoring device 400 may include a millimeter-wave radar 410 and a camera 430. In another embodiment, the roadside monitoring device 400 may include a lidar 420 and a camera 430. In another embodiment, the roadside monitoring device 400 may include a millimeter-wave radar 410 and a lidar 420. In other embodiments, the roadside monitoring device 400 may also include other types of sensors, multiple millimeter-wave radars, multiple lidars, and any combination of multiple cameras. The multiple sensors in the roadside monitoring device 400 may be jointly calibrated so that the coordinates of the sensing areas of each sensor are connected to each other. These sensors can be connected together via a connection structure 440.

[0057] In one embodiment, the sensors can be fixed to the connection structure 440, such that the positional relationship between the multiple sensors is fixed. The installer can only install and adjust the roadside monitoring device 400 as a whole. In this embodiment, method 200 can receive only the sensing data set from one sensor, divide the sensing data set into multiple subsets of sensing data based on multiple sub-regions of the sensor's sensing area, obtain parameter values ​​for each sub-region, and provide an indication of whether each sub-region belongs to the target area. That is, multiple sensors in the roadside monitoring device can be adjusted as a whole based on the sensing data of one sensor in the roadside monitoring device. Method 200 can also receive sensing data sets from multiple sensors and provide an indication for each sensor, respectively, of whether each of its sub-regions belongs to the target area. In this case, the position of the roadside monitoring device can be adjusted based on a combination of these indications. For example, for sub-regions that substantially overlap between multiple sensors, when the number of indications indicating that the sub-region belongs to the target area is less than the number of indications indicating that the sub-region does not belong to the target area, the roadside monitoring device can be adjusted according to the indications that the sub-region does not belong to the target area. The installer can also utilize multiple indications based on multiple sensors according to other strategies. The technical solutions disclosed herein are not limited to specific combination strategies based on multiple indicators from multiple sensors.

[0058] In another embodiment, multiple sensors in the roadside monitoring device 400 can be moved within the range provided by the connection structure 440 to adjust their relative positional relationships. For example... Figure 4 As shown, the connection structure 440 can support one or more of the millimeter-wave radar 410, lidar 420, and camera 430 in the following configuration: Figure 2A The sensor shown can be translated left and right along the x-axis. Connection structure 440 can also support each sensor along... Figure 2A The x, y, and z axes shown are rotated to adjust the corresponding angles. The connection structure 440 may also include other mechanisms to further support one or more of the millimeter-wave radar 410, lidar 420, and camera 430 in applications such as... Figure 2A The sensor shown is translated vertically along the z-axis. In this embodiment, the position of each sensor can be adjusted individually according to the indication provided by method 200 regarding whether each sub-region belongs to the target region. For example, the mounting position of the millimeter-wave radar 410 can be adjusted according to the indication of each sub-region of the millimeter-wave radar 410, and the mounting position of the lidar 420 can be adjusted according to the indication of each sub-region of the lidar 420.

[0059] Furthermore, in this embodiment, similar to the embodiments described above, for sub-regions that substantially overlap between multiple sensors, the positions of these multiple sensors can be adjusted individually based on a combination of multiple indications from these sensors regarding that sub-region. For example, for millimeter-wave radar 410, if an indication based on a subset of sensing data from one of its sub-regions indicates that the sub-region belongs to the target region, while indications from multiple other sensors indicate that a sub-region substantially overlapping with that sub-region does not belong to the target region, then the mounting position of millimeter-wave radar 410 can also be adjusted according to the sub-region not belonging to the target region. In summary, multiple indications for substantially overlapping sub-regions of multiple sensors can be utilized according to various different combination strategies. These different combination strategies are all within the scope of this disclosure.

[0060] In this disclosure, installing roadside monitoring equipment includes not only adjusting the installation location of the roadside monitoring equipment, but also adjusting the sensing area of ​​each sensor by configuring the operating parameters of one or more sensors in the roadside monitoring equipment. In one embodiment, the method of this disclosure may further include providing configuration information for the at least one sensor based on the obtained parameter values ​​of each sub-region. Taking millimeter-wave radar as an example, if it can be determined that a sub-region does not belong to a target area based on the obtained parameter values ​​of a sub-region of the millimeter-wave radar, its beam scanning range can be adjusted by providing configuration information to the millimeter-wave radar. This configuration information may be a desired beam adjustment angle, a suggested transmission power, or specific parameters such as the phase or amplitude of the antenna array. The parameter values ​​can be optimized automatically by the sensor's internal optimization algorithm or operated by the installer through an open interface.

[0061] It should be understood that all operations in the methods described above are merely exemplary, and this disclosure is not limited to any operation in the methods or the order of such operations, but should cover all other equivalent transformations under the same or similar concept.

[0062] Figure 5 A system block diagram for installing a roadside monitoring device according to an embodiment of the present disclosure is shown. The system includes at least a roadside monitoring device 600, a device 500 for installing the roadside monitoring device, and an installer 700. The roadside monitoring device 600 may include one or more sensors 610. The sensors 610 may be millimeter-wave radar, active radar, or cameras, etc. After the installer installs the roadside monitoring device 600 in a initially determined location, the installer can use the device 500 to adjust the position of the roadside monitoring device 600 and the sensors 610 therein to install the roadside monitoring device 600 in a suitable location. The device 500 may also directly provide instructions and / or configuration information to the roadside monitoring device 600 to automatically adjust the position and / or operating parameters of the sensors 610.

[0063] like Figure 5 As shown, device 500 may include at least one processor 510, memory 520, and input / output interface 530. Memory 520 and input / output interface 530 may be coupled to processor 510 via bus 540. Processor 510 may be configured to implement the above-described... Figure 1A and 1BThe method described. In one embodiment, the processor 510 may be configured to: receive, via an input / output interface 530, at least one sensing dataset from at least one of one or more sensors, each sensing dataset corresponding to a sensing area of ​​each of the at least one sensor, the sensing area comprising a plurality of sub-regions; divide each sensing dataset into a plurality of sensing data subsets, each sensing data subset corresponding to each sub-region within the plurality of sub-regions; and obtain parameter values ​​for each sub-region based on the sensing data subset of each sub-region, the parameter values ​​being related to traffic environment characteristics within each sub-region, for determining whether each sub-region belongs to a target region.

[0064] These processors can be implemented using electronic hardware, computer software, or any combination thereof. Whether these processors are implemented as hardware or software will depend on the specific application and the overall design constraints imposed on the system. As an example, the processors, any portions of processors, or any combination of processors given in this disclosure can be implemented as microprocessors, microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gate logic, discrete hardware circuits, and other suitable processing units configured to perform the various functions described in this disclosure. The functionality of the processors, any portions of processors, or any combination of processors given in this disclosure can be implemented as software executed by a microprocessor, microcontroller, DSP, or other suitable platform.

[0065] Input / output interface 530 may include a keyboard, mouse, monitor, USB interface, Ethernet interface, Type-C interface, CANFD interface, Pod interface, etc. Device 500 can receive configuration information from installer 700 via input / output interface 530, such as which coordinate axes to divide the sensing area along, how many sub-areas to divide the sensing area into, what parameter values ​​to obtain for each sub-area, and threshold values ​​to determine whether a sub-area belongs to the target area, etc. Device 500 can provide the installer 700 with the parameter values ​​of each sub-area it has obtained, or its determined indications regarding whether each sub-area belongs to the target area, via input / output interface 530. Device 500 can receive sensing data sets from one or more sensors 610 from roadside monitoring device 600 via input / output interface 530, and provide the roadside monitoring device 600 with instructions and configuration information for automatically adjusting installation location and operating parameters via input / output interface 530.

[0066] According to another embodiment, this disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable code for performing the following methods: receiving at least one sensing dataset from at least one of one or more sensors, each sensing dataset corresponding to a sensing area of ​​each of the at least one sensor, the sensing area comprising a plurality of sub-regions; dividing each sensing dataset into a plurality of sensing data subsets, each sensing data subset corresponding to each sub-region within the plurality of sub-regions; and obtaining parameter values ​​for each sub-region based on the sensing data subset of each sub-region, the parameter values ​​being related to traffic environment characteristics within each sub-region, for determining whether each sub-region belongs to a target region. Furthermore, the technical content described above with respect to the methods and apparatus according to various embodiments of this disclosure is also applicable to this embodiment.

[0067] According to another embodiment, this disclosure also provides a computer program product. The computer program product includes instructions executable by a processor to implement the following method: receiving at least one sensing dataset from at least one of one or more sensors, each sensing dataset corresponding to a sensing area of ​​each of the at least one sensor, the sensing area comprising a plurality of sub-regions; dividing each sensing dataset into a plurality of sensing data subsets, each sensing data subset corresponding to each sub-region within the plurality of sub-regions; and obtaining parameter values ​​for each sub-region based on the sensing data subset of each sub-region, the parameter values ​​being related to traffic environment characteristics within each sub-region for determining whether each sub-region belongs to a target region. Furthermore, the technical content described above with respect to the methods and apparatus according to various embodiments of this disclosure is also applicable to this embodiment.

[0068] Those skilled in the art should understand that the various embodiments disclosed above can be modified and varied in various ways without departing from the spirit of the invention. All such modifications and variations should fall within the protection scope of the present invention, and the protection scope of the present invention should be defined by the claims.

Claims

1. A method for installing roadside monitoring equipment, the roadside monitoring equipment comprising one or more sensors, characterized in that, The method includes: Receive at least one sensing dataset from at least one of the one or more sensors, each sensing dataset corresponding to a sensing area of ​​one of the at least one sensors, the sensing area comprising multiple sub-regions; The sensing dataset is divided into multiple sensing data subsets, and each sensing data subset corresponds to one of the multiple sub-regions; Parameter values ​​for the sub-region are obtained based on a subset of sensed data from the sub-region, and these parameter values ​​are related to environmental features within the sub-region; and The parameter value provides an indication of whether the sub-region belongs to the target region.

2. The method according to claim 1, wherein, The multiple sub-regions are divided along one or more coordinate axes of the sensor corresponding to the sensing region.

3. The method according to claim 1, wherein, The instruction is determined based on at least one of the following two: The comparison of parameter values ​​in the sub-region with the threshold, and Comparison of parameter values ​​among multiple sub-regions. Furthermore, the threshold is predetermined or configurable.

4. The method according to claim 1, wherein, The instruction is used to adjust a corresponding sensor among the at least one sensor individually, or to adjust the one or more sensors as a whole.

5. The method according to claim 1, wherein, The at least one sensor includes at least one of millimeter-wave radar and lidar.

6. The method according to claim 5, wherein, The environmental characteristics include the motion characteristics, reflection characteristics, or a combination thereof of the objects being sensed in the environment.

7. The method according to claim 5, wherein, The parameter value includes one or more of the following: The number of reflection points within the sub-region; The intensity of reflection points within the sub-region; The reflection area of ​​the reflection point within the sub-region; The distance between the reflection point and the sub-region.

8. The method according to claim 5, wherein, The roadside monitoring device includes a camera, and the method further includes: Receive monitoring data from the camera to obtain the parameter value or to determine the indication.

9. The method according to claim 1, further comprising: Configuration information for the at least one sensor is provided based on the parameter values.

10. An apparatus for installing roadside monitoring equipment, the roadside monitoring equipment including one or more sensors, the apparatus including at least one processor, a memory coupled to the at least one processor, and an input / output interface coupled to the at least one processor, characterized in that, The at least one processor is configured to: The system receives at least one sensing dataset from at least one of the one or more sensors via the input / output interface, each sensing dataset corresponding to a sensing area of ​​one of the at least one sensors, the sensing area comprising multiple sub-regions; The sensing dataset is divided into multiple sensing data subsets, and each sensing data subset corresponds to one of the multiple sub-regions; The parameter values ​​of the sub-region are obtained based on a subset of the sensing data of the sub-region, and the parameter values ​​are related to the environmental features within the sub-region. as well as The parameter value provides an indication of whether the sub-region belongs to the target region.

11. The apparatus according to claim 10, wherein, The instruction is determined based on at least one of the following two: The comparison of parameter values ​​in the sub-region with the threshold, and Comparison of parameter values ​​among multiple sub-regions. Furthermore, the threshold is predetermined or configurable.

12. The apparatus according to claim 10, wherein, The environmental characteristics include the motion characteristics, reflection characteristics, or a combination thereof of the objects being sensed in the environment.

13. The apparatus according to claim 10, wherein, The parameter value includes one or more of the following: The number of reflection points within the sub-region; The intensity of reflection points within the sub-region; The reflection area of ​​the reflection point within the sub-region; The distance between the reflection point and the sub-region.

14. A computer-readable storage medium having stored thereon computer-executable code for performing the method according to claims 1-9.

15. A computer program product comprising instructions executable by a processor to implement the method according to claims 1-9.

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