CN115431695A - Suspension parameter adjusting method and device, electronic equipment and storage medium - Google Patents
Suspension parameter adjusting method and device, electronic equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/016—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input
- B60G17/0165—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/30—Measuring arrangements characterised by the use of optical techniques for measuring roughness or irregularity of surfaces
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
- B60G2400/80—Exterior conditions
- B60G2400/82—Ground surface
- B60G2400/821—Uneven, rough road sensing affecting vehicle body vibration
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
- B60G2400/80—Exterior conditions
- B60G2400/82—Ground surface
- B60G2400/823—Obstacle sensing
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2401/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60G2401/21—Laser
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2800/00—Indexing codes relating to the type of movement or to the condition of the vehicle and to the end result to be achieved by the control action
- B60G2800/90—System Controller type
- B60G2800/91—Suspension Control
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Abstract
The invention discloses a suspension parameter adjusting method and device, electronic equipment and a storage medium. In the running process of a target vehicle, acquiring original point cloud data of an environment to which the target vehicle belongs based on a laser radar deployed on the target vehicle; determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data; when the current flatness information is inconsistent with the preset flatness parameter, determining obstacle information positioned on a driving road surface based on target point cloud data corresponding to the original point cloud data; wherein the obstacle information includes raised obstacles or depressed depressions; based on the obstacle information, suspension parameters deployed in the target vehicle are adjusted to pass through an obstacle corresponding to the obstacle information based on the adjusted suspension parameters. The suspension parameters of the vehicle can be automatically adjusted according to the road conditions, so that the driving experience of a user is improved.
Description
Technical Field
The invention relates to the technical field of automobile suspensions, in particular to a suspension parameter adjusting method and device, electronic equipment and a storage medium.
Background
With the continuous improvement of vehicle intelligent degree and electronic control technology, on the premise of ensuring safety, pursuing higher experience and comfort becomes an increasingly forward and exploration target of people. The suspension is an important component of the vehicle chassis, and influences the driving safety of the vehicle and the riding comfort of a user.
The automobile suspension used by the traditional automobile is a passive suspension, and before the automobile leaves a factory, the automobile suspension is calibrated and debugged at one time according to the expected smoothness requirement of the automobile.
Vehicles using conventional suspensions are not adjustable throughout the vehicle's cycle, and have significant limitations on the ride and comfort of the vehicle.
Disclosure of Invention
The invention provides a suspension parameter adjusting method, a suspension parameter adjusting device, electronic equipment and a storage medium, which are used for adjusting suspension parameters according to a road surface state and further ensuring the safety and comfort of a user in a driving process.
According to an aspect of the present invention, there is provided a suspension parameter adjustment method, including;
in the running process of a target vehicle, acquiring original point cloud data of an environment to which the target vehicle belongs based on a laser radar deployed on the target vehicle;
determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data;
when the current flatness information is inconsistent with the preset flatness parameter, determining obstacle information positioned on a driving road surface based on target point cloud data corresponding to the original point cloud data; wherein the obstacle information includes raised obstacles or depressed potholes;
based on the obstacle information, suspension parameters deployed in the target vehicle are adjusted to pass through an obstacle corresponding to the obstacle information based on the adjusted suspension parameters.
According to another aspect of the present invention, there is provided a suspension parameter adjusting apparatus including:
the data acquisition module is used for acquiring original point cloud data of the environment to which the target vehicle belongs based on a laser radar deployed on the target vehicle in the running process of the target vehicle;
the information determining module is used for determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data;
the obstacle determining module is used for determining obstacle information positioned on a driving road surface based on target point cloud data corresponding to the original point cloud data when the current flatness information is inconsistent with a preset flatness parameter; wherein the obstacle information includes raised obstacles or depressed depressions;
and the suspension parameter adjusting module is used for adjusting the suspension parameters deployed in the target vehicle based on the obstacle information so as to pass through the obstacle corresponding to the obstacle information based on the adjusted suspension parameters.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a suspension parameter adjustment method of any embodiment of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a suspension parameter adjusting method according to any embodiment of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the point cloud data of the road surface is obtained by using the laser radar, the road surface condition is judged according to the point cloud data, and the vehicle suspension is automatically adjusted based on the road surface condition, so that the comfort and the safety of a user in the driving process are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for adjusting suspension parameters according to an embodiment of the present invention;
fig. 2 is a flowchart of a suspension parameter adjustment method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a suspension parameter adjustment method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a suspension parameter adjusting apparatus according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the suspension parameter adjustment method according to the embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
It should be noted that the terms "object," "original," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a suspension parameter adjustment method according to an embodiment of the present invention, where the present embodiment is applicable to a situation that a vehicle needs to perform suspension adjustment, and the method may be performed by a suspension parameter adjustment device, where the suspension parameter adjustment device may be implemented in a form of hardware and/or software, and the suspension parameter adjustment device may be configured in a bottom of a vehicle. As shown in fig. 1, the method includes:
s110, in the running process of the target vehicle, acquiring original point cloud data of the environment to which the target vehicle belongs based on the laser radar deployed on the target vehicle.
The target vehicle is a vehicle which needs suspension adjustment at present, that is, any vehicle which can perform suspension adjustment and can use a laser radar. The laser radar is a radar system for detecting the position, speed and other characteristic quantities of a target by emitting a laser beam, and the working principle of the radar system is to emit a detection signal (laser beam) to the target, compare a received signal (target echo) reflected from the target with the emission signal, and obtain relevant information of the target after proper processing, such as parameters of the target, such as the distance, the direction, the height, the speed, the attitude, even the shape and the like. The laser radar is composed of a laser transmitter, an optical receiver, a rotary table, an information processing system and the like, wherein the laser device converts electric pulses into optical pulses to be transmitted out, and the optical receiver restores the optical pulses reflected from a target into electric pulses to be transmitted to a display device. According to the vertical field angle distribution of the laser radar, the proper radar installation height and the proper radar pitch angle are selected, and the radar installation height and the proper radar pitch angle include but are not limited to positions such as a front air grid and a roof of a vehicle. Point cloud data refers to a collection of vectors in a three-dimensional coordinate system. The point cloud data has data such as geometric position and color information. Specifically, the point cloud information acquired by the laser radar is point cloud information acquired by processing and combining parameters such as target distance, azimuth, height, speed, attitude and even shape acquired by the laser radar by utilizing the working principle of the laser radar. The original point cloud data of the environment can be point cloud data of the surrounding environment of the automobile acquired by the laser radar, and it can be understood that the point cloud data of all the environments acquired by the laser radar can be the original point cloud data. In an exemplary embodiment, a road surface detection device for starting a laser radar is used for acquiring point cloud data outside a vehicle according to the working principle of the laser radar, and the condition of the surrounding environment of the vehicle can be acquired in this way.
And S120, determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data.
The running surface is a surface on which the target vehicle can run, and includes unpaved surfaces and paved surfaces. The flatness is a deviation value indicating the amount of unevenness in the longitudinal direction of the road surface. Specifically, the current flatness information may be information on the degree of unevenness of a road surface on which the automobile is traveling, which is acquired. And judging the relevant information of the concave-convex degree on the road surface which is running according to the point cloud data of the surrounding environment obtained by the laser radar in the running process of the target vehicle. In the process of driving an automobile on a road surface, point cloud data of the surrounding environment is obtained according to the working principle of a laser radar, and the condition that the road surface is uneven 20 meters ahead exists is judged and is identified as a pit.
Further, according to the original point cloud data, determining the current flatness information of the road surface on which the target vehicle runs comprises:
eliminating noise points in the original point cloud data to obtain point cloud data to be used;
acquiring target point cloud data corresponding to the region of interest from the point cloud data to be used;
and obtaining the current flatness information of the road surface on which the target vehicle runs by fitting the target point cloud data.
The noise points are point cloud data of discrete points in the original point cloud data, which negatively affects data processing. The noise points are generally distributed in an isolated manner, and can be judged according to the distance between a plurality of points around a mote point, and the point cloud distance is far. The point clouds around the noise points are fewer and more sparse than other point clouds. The noise points are many sources, such as points beyond the scan setting range; points generated due to influence of ambient wind, vibration of ambient objects, and the like; or the influence of moisture in the air, etc. The generated noise points not only increase the data volume of the point cloud, but also influence the accuracy of modeling and information extraction, and the like. The advantage of rejecting noise points is that the computational efficiency can be improved and errors reduced more effectively. Illustratively, large particles in the air are identified by the laser radar as point cloud data, and such large particles are noise points, and the influence can be reduced by filtering the point cloud data of the noise points.
The region of interest is an image region selected from the image, and the region is a focus point concerned by image analysis. Optionally, the region of interest may be divided by using a point cloud segmentation method. The point cloud segmentation aims to cluster points with similar characteristics into uniform regions, and is applied to scene analysis in various aspects according to segmentation results. The target point cloud data is preset point cloud data which is extracted from the original point cloud data and needs to be further calculated or processed. Specifically, target point cloud data corresponding to the region of interest is obtained by further processing from the point cloud data to be used obtained by removing the noise points in the original point cloud data. For example, the range of the road surface area is divided through the region of interest division, and road point cloud information acquired by a driving vehicle can be distinguished from surrounding environment point cloud information and the like. Illustratively, the point cloud information of the road surface in front of the vehicle is extracted, the laser radar point clouds projected within the range of +/-2 cm on the plane of the road surface are the point clouds on the plane of the road surface, the point clouds with the distance larger than +/-2 cm can be regarded as pits or convex hulls influencing the smooth passing of the vehicle, and then the smoothness of the road surface is judged.
Further, rejecting noise points in the original point cloud data to obtain point cloud data to be used, including: and acquiring road point cloud data which is consistent with the driving direction of the target vehicle and is away from the target vehicle by a preset distance threshold value from the point cloud data to be used based on a direct filtering method, and taking the road point cloud data as the target point cloud data of the region of interest.
The straight-through filtering method has the function of filtering out points with values which are not in a given value range in the specified dimension direction. The straight-through filtering method is characterized in that a dimension and a value range under the dimension are designated, each point in the point cloud is traversed, whether the value of the point in the designated dimension is in the value range is judged, the points with the values not in the value range are deleted, the traversal is finished, and the remaining points form the filtered point cloud. The pass-through filtering may be based on attributes of the point cloud including width attributes, length attributes, depth attributes, and color attributes, among others. And setting a range on the attributes of the point cloud, filtering the points, and keeping the points within the range or out of the range. For example: in one data, only data with depth values less than 5 meters are needed to be retained, and point clouds with depth attributes less than 5 meters can be retained through a pass-through filter. The advantage of using the through filtering is that it is simple and efficient, and is suitable for eliminating background, etc. The preset distance threshold may be a preset critical value of a distance range in which the laser radar can acquire the road point cloud information. Specifically, the road point cloud data of the target vehicle from the preset distance threshold may be road point cloud data of a distance between the target vehicle and a pre-calibrated distance, and the road point cloud data of the target vehicle from the preset distance threshold is used as the target point cloud data. The point cloud data of the region of interest is the point cloud data which meets the condition that the target vehicle runs in a consistent direction and is divided according to a preset distance threshold, and the point cloud data needs to be processed in the next step. Illustratively, the width in front of the running vehicle is selected to be 8m (about two lane widths), and the condition of the road surface in front of the self lane can be detected, and meanwhile, the information of the lanes on two sides can also be detected so as to change lanes.
S130, when the current flatness information is inconsistent with the preset flatness parameter, determining obstacle information on the driving road surface based on target point cloud data corresponding to the original point cloud data; wherein the obstacle information includes raised obstacles or depressed depressions.
The obstacle is an object blocking the vehicle from running, and may be an object affecting the normal running of the vehicle, such as a raised obstacle or a depressed place. The obstacle information may include information on the size of the obstacle, the distance from the target vehicle, and the like. The preset flatness parameter may be a parameter calibrated in advance for comparing and judging whether the road surface is flat. The target point cloud data can be pre-calibrated point cloud data which needs to be calculated and processed. Specifically, if the current flatness information is inconsistent with the preset flatness parameter, the information of the obstacle on the driving road surface is determined according to the acquired point cloud data. Illustratively, a pit is arranged on a driving road, and the information such as the size of the pit, the distance between the pit and a target vehicle and the like is judged according to the acquired target point cloud data.
And S140, adjusting the suspension parameters deployed in the target vehicle based on the obstacle information so as to pass through the obstacle corresponding to the obstacle information based on the adjusted suspension parameters.
The suspension parameters can be suspension parameters which can be automatically adjusted, such as caster, camber, toe-in, toe-out, toe-in and the like. Specifically, the adaptive suspension parameters are adjusted according to the distance between the obstacle and the target vehicle and the size information of the obstacle so as to meet the requirements of smoothness and comfort of the vehicle when the obstacle meets a pit or a bump. For example, the obstacle is positioned in front of the vehicle, and whether the suspension parameters of the vehicle are adjusted or not is judged according to the obstacle information, so that the adoption number can be adjusted
Further, when the current flatness information is consistent with a preset flatness parameter, the target vehicle is controlled to continue to run.
Specifically, if the current flatness information is consistent with the preset flatness parameter, the current running road surface is considered to be a flat road surface, and the vehicle runs normally without adjusting the suspension parameters.
In the embodiment, in the running process of a target vehicle, original point cloud data of the environment to which the target vehicle belongs are acquired based on a laser radar deployed on the target vehicle; determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data; when the current flatness information is inconsistent with the preset flatness parameter, determining obstacle information on the driving road surface based on target point cloud data corresponding to the original point cloud data; wherein the obstacle information includes raised obstacles or depressed depressions; based on the obstacle information, the suspension parameters deployed in the target vehicle are adjusted, and the suspension parameters can be automatically adjusted according to the road surface condition of the vehicle in a mode that the adjusted suspension parameters pass through the obstacle corresponding to the obstacle information, so that the driving experience of a user is improved.
Example two
Fig. 2 is a flowchart of a suspension parameter adjustment method according to a second embodiment of the present invention, where in step S130 of the above embodiment, when the current flatness information is inconsistent with the preset flatness parameter, the method determines the information of the obstacle on the driving road surface based on the target point cloud data corresponding to the original point cloud data, as shown in fig. 2, and the method includes:
s210, clustering the target point cloud data to obtain at least one clustering result;
the clustering is to divide a data set into different classes or clusters according to a certain specific standard, so that the similarity of data objects in the same cluster is as large as possible, and the difference of data objects not in the same cluster is also as large as possible. That is, after clustering, the data of the same class are gathered together as much as possible, and the data of different classes are separated as much as possible. The clustering process may use conventional clustering algorithms such as spectral clustering and mean shift. In this embodiment, the information of the obstacle can be determined from the clustering result. Specifically, at least one clustering result is obtained by clustering target point cloud data in the region of interest, which can indicate that the current flatness is inconsistent with the preset flatness information, that is, a concave or convex object may exist.
Further, the clustering the target point cloud data to obtain at least one clustering result includes: clustering the target point cloud based on preset distance thresholds, and determining a plurality of clustering clusters located in the same clustering center;
wherein each cluster corresponds to a clustering result.
The preset distance thresholds may be the clustered distance differences between the pre-calibrated point clouds. Specifically, after the target point cloud clustering algorithm is processed, a plurality of clustering clusters located in the same clustering center can be obtained, and such clustering clusters can be regarded as an obstacle. And one clustering result corresponding to each clustering cluster can be judged according to a plurality of clustering clusters judged by the clustering algorithm.
S220, determining obstacle information positioned on the driving road surface based on each clustering result;
the obstacle information comprises distance information and obstacle size information within the target vehicle. Specifically, information such as the size of the obstacle on the travel surface and the shape of the obstacle can be obtained from the result of each cluster.
Further, comprising: and for each clustering result, determining each point cloud data in the current clustering result, determining a point cloud distance mean value in the current clustering result and using the point cloud distance mean value as the distance information and the obstacle size information corresponding to the current clustering result.
The point cloud distance average value may be an average value of distances of all point clouds in a certain clustered point cloud set. The point cloud distance average may be calculated by the following formula, or may be calculated by other calculation methods.
Wherein n is the number of points in a certain clustered point cloud set, (x) m ，y m ，z m ) And L is the average distance value of all the points in the clustering point cloud set. A, B, C, D are constants that describe the spatial characteristics of a plane, and the general form of the plane equation in space is: ax + By + Cz + D =0, and the values of a, B, C, D can be solved By knowing the coordinates of 3 points in the plane.
The obstacle size information may be information among obstacle information obtained by calculation according to a clustering algorithm. The calculation formula of the maximum height Hmmax of the obstacle and the width W of the obstacle may be obtained by referring to the following formula, or may be obtained by calculation using another calculation method.
Specifically, for each clustering result, determining each point cloud data in the current clustering result, determining a point cloud distance mean value in the current clustering result and using the point cloud distance mean value as the distance information and the obstacle size information corresponding to the current clustering result. And adjusting the suspension dining and lodging by acquiring the distance from the current vehicle to the obstacle and the size of the obstacle according to the calculated point cloud distance mean value. Specifically, when the average value of the cloud distance of the points is 10m, the width of the obstacle is 0.5m, and the height of the obstacle is 0.3m, the suspension adjusts the comfortable suspension into the sports suspension through adjusting parameters, and the vehicle does not have strong discomfort when meeting a convex road surface.
In this embodiment, at least one clustering result is obtained by clustering the target point cloud data; the method for determining the information of the obstacles on the running road surface based on the clustering results can detect the information of the obstacles running at present, and adjust the suspension of the vehicle through the detection information, so that the suspension parameters can be automatically adjusted when the road surface is uneven, and the riding experience of a user in the running process of the vehicle is improved.
EXAMPLE III
As an alternative example of the foregoing embodiment, fig. 3 is a flowchart of a suspension parameter adjustment method provided in a third embodiment of the present invention, and a referential embodiment is provided according to the foregoing embodiment. As shown in fig. 3, the method includes:
the system comprises a laser radar module, a signal acquisition module, a signal processing module, a power supply module and an actuator module. Wherein power module supplies power for laser radar, and the sensor of laser radar module as road surface perception, signal acquisition module carry out the collection of road surface environment, and signal processing module handles road surface information to obtain the road surface information in the region of interest, export for the executor, and then adjust the suspension of vehicle. According to the vertical field angle distribution of the laser radar, the proper installation height and the proper pitch angle are selected, and the installation height and the pitch angle include but are not limited to positions such as a front air grid and a roof of a vehicle.
The original point cloud data obtained by the laser radar is processed mainly through links such as noise point removal, region of interest division, road surface fitting, plane parameter calculation and the like to obtain final road surface information. The noise filtering is to filter some discrete invalid points in the acquired point cloud data, so as to reduce the computation amount of clustering and matching and more accurately identify the information of the uneven road surface. The definition of the invalid point is measured by the distance between the nearest points around a certain point, and the point cloud distribution of the invalid point is more dispersed and more distant. The selection of the number of points and the setting of the distance threshold can be determined according to the required noise removal effect. And dividing the region of interest, namely dividing the range of the original point cloud pavement region before pavement fitting, distinguishing the pavement from the surrounding environment and improving the pavement fitting effect. By adopting a straight-through filtering method in a fixed range, the width of the front of a vehicle is 8m, about two lane widths are selected, lane information on two sides can be detected while the road condition in front of the lane is detected, and the implementation of decisions such as lane changing and the like is facilitated. The road surface fitting is to extract the road surface information in front of the vehicle from the original point cloud, the laser radar point cloud projected within the range of +/-2 cm from the road surface plane is the point cloud of the road surface plane, the point cloud with the distance larger than +/-2 cm can be regarded as a pit or a convex hull influencing the smooth passing of the vehicle, and then the smoothness of the road surface is judged. If the road surface is flat, the road surface parameter information is extracted and directly output to the controller, and the vehicle normally passes through the front road surface. If the road surface is uneven, extracting point cloud parameters of the uneven road surface, carrying out clustering calculation on the obstacles through a clustering algorithm, carrying out clustering processing on the point cloud in the non-plane in a mode of setting a distance threshold, if the distance is smaller than the threshold, judging the point close to the obstacle according to the threshold, and continuing judging the point close to the point according to the threshold until the point close to the threshold is not met. And after clustering is finished, calculating the sizes of pits or convex hulls outside the road surface according to the point cloud set of the obstacles by the following formula, and outputting the sizes to a controller for suspension parameter adjustment.
According to the embodiment, after the point cloud data acquired through the laser radar are processed, the obstacle information can be determined and the suspension parameters can be adjusted, so that the comfort of a user in riding is improved.
Example four
Fig. 4 is a schematic structural diagram of a suspension parameter adjusting apparatus according to an embodiment of the present invention. As shown in fig. 4, the apparatus includes:
the data acquisition module 41 is used for acquiring original point cloud data of an environment to which a target vehicle belongs based on a laser radar deployed on the target vehicle in the running process of the target vehicle;
an information determining module 42, configured to determine, according to the original point cloud data, current flatness information of a road surface on which the target vehicle travels;
an obstacle determining module 43, configured to determine obstacle information located on the driving road surface based on target point cloud data corresponding to the original point cloud data when the current flatness information is inconsistent with a preset flatness parameter; wherein the obstacle information includes raised obstacles or depressed depressions;
a suspension parameter adjustment module 44 configured to adjust suspension parameters deployed in the target vehicle based on the obstacle information to pass through an obstacle corresponding to the obstacle information based on the adjusted suspension parameters.
On the basis of the above technical solutions, the information determining module 42 includes a noise point removing sub-module, an interested region dividing sub-module, and a data fitting sub-module.
The noise point removing submodule is used for removing noise points in the original point cloud data to obtain point cloud data to be used;
the region-of-interest division submodule is used for acquiring target point cloud data corresponding to a region of interest from the point cloud data to be used, and acquiring road point cloud data which is consistent with the driving direction of the target vehicle and is away from the target vehicle by a preset distance threshold value from the point cloud data to be used on the basis of a straight-through filtering method to serve as the target point cloud data of the region of interest;
and the data fitting submodule is used for fitting the target point cloud data to obtain the current flatness information of the road surface on which the target vehicle runs.
On the basis of the above technical solutions, the obstacle determining module 43 includes a clustering sub-module and an obstacle information determining sub-module.
The clustering processing sub-module is used for clustering the target point cloud data to obtain at least one clustering result;
the obstacle information determination submodule is used for determining the obstacle information positioned on the driving road surface based on each clustering result; the obstacle information comprises distance information and obstacle size information within the target vehicle.
On the basis of the technical schemes, the clustering processing sub-module comprises a clustering determining unit.
A cluster determining unit, configured to perform cluster processing on the target point cloud based on preset distance thresholds, and determine multiple clusters located in the same cluster center; wherein each cluster corresponds to a clustering result.
On the basis of the technical solutions, the obstacle information determination submodule includes a size distance determination unit.
And the size distance determining unit is used for determining each point cloud data in the current clustering result for each clustering result, determining a point cloud distance mean value in the current clustering result and taking the point cloud distance mean value as the distance information and the obstacle size information corresponding to the current clustering result.
The suspension parameter adjusting device provided by the embodiment of the invention can execute the suspension parameter adjusting method provided by any embodiment of the invention, so that the vehicle can automatically adjust the suspension parameters according to the road surface condition, and the driving experience of a user is improved.
It should be noted that, the units and modules included in the apparatus are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the embodiments of the present disclosure.
EXAMPLE five
FIG. 5 is a block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the suspension parameter adjustment method.
In some embodiments, the suspension parameter adjustment method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. One or more steps of a suspension parameter adjustment method described above may be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, the processor 11 may be configured to perform the suspension parameter adjustment method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A suspension parameter adjustment method is characterized by comprising the following steps:
in the running process of a target vehicle, acquiring original point cloud data of an environment to which the target vehicle belongs based on a laser radar deployed on the target vehicle;
determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data;
when the current flatness information is inconsistent with a preset flatness parameter, determining obstacle information positioned on the driving road surface based on target point cloud data corresponding to the original point cloud data; wherein the obstacle information includes raised obstacles or depressed potholes;
adjusting suspension parameters deployed in the target vehicle based on the obstacle information to pass through an obstacle corresponding to the obstacle information based on the adjusted suspension parameters.
2. The method of claim 1, wherein determining current flatness information of a road surface on which the target vehicle is traveling from the raw point cloud data comprises:
eliminating noise points in the original point cloud data to obtain point cloud data to be used;
acquiring target point cloud data corresponding to an area of interest from the point cloud data to be used;
and obtaining the current flatness information of the road surface on which the target vehicle runs by fitting the target point cloud data.
3. The method of claim 2, wherein the obtaining target point cloud data corresponding to a region of interest from the point cloud data to be used comprises:
and acquiring road point cloud data which is consistent with the driving direction of the target vehicle and is away from the target vehicle by a preset distance threshold value from the point cloud data to be used based on a direct filtering method, and taking the road point cloud data as the target point cloud data of the region of interest.
4. The method of claim 1, wherein determining obstacle information located on the travel surface based on target point cloud data corresponding to the raw point cloud data comprises:
clustering the target point cloud data to obtain at least one clustering result;
determining obstacle information located on the driving road surface based on each clustering result;
the obstacle information comprises distance information and obstacle size information within the target vehicle.
5. The method of claim 4, wherein the obtaining at least one clustering result by clustering the target point cloud data comprises:
clustering the target point cloud based on preset distance thresholds, and determining a plurality of clustering clusters located in the same clustering center;
wherein each cluster corresponds to a clustering result.
6. The method according to claim 4, wherein the determining obstacle information located on the traveling surface based on each clustering result includes:
and for each clustering result, determining each point cloud data in the current clustering result, determining a point cloud distance mean value in the current clustering result and using the point cloud distance mean value as the distance information and the obstacle size information corresponding to the current clustering result.
7. The method of claim 1, further comprising:
and when the current flatness information is consistent with a preset flatness parameter, controlling the target vehicle to continuously run.
8. A suspension parameter adjustment device, comprising:
the data acquisition module is used for acquiring original point cloud data of the environment to which a target vehicle belongs based on a laser radar deployed on the target vehicle in the running process of the target vehicle;
the information determining module is used for determining the current flatness information of the road surface on which the target vehicle runs according to the original point cloud data;
the obstacle determining module is used for determining obstacle information positioned on the driving road surface based on target point cloud data corresponding to the original point cloud data when the current flatness information is inconsistent with a preset flatness parameter; wherein the obstacle information includes raised obstacles or depressed potholes;
a suspension parameter adjustment module to adjust suspension parameters deployed in the target vehicle based on the obstacle information to pass through an obstacle corresponding to the obstacle information based on the adjusted suspension parameters.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the suspension parameter adjustment method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the suspension parameter adjustment method of any one of claims 1-7 when executed.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05213033A (en) * | 1992-02-06 | 1993-08-24 | Mitsubishi Motors Corp | Camber angle control device for vehicle |
| CN107064955A (en) * | 2017-04-19 | 2017-08-18 | 北京汽车集团有限公司 | barrier clustering method and device |
| CN108256577A (en) * | 2018-01-18 | 2018-07-06 | 东南大学 | A kind of barrier clustering method based on multi-line laser radar |
| CN111860321A (en) * | 2020-07-20 | 2020-10-30 | 浙江光珀智能科技有限公司 | Obstacle identification method and system |
| CN112784799A (en) * | 2021-02-01 | 2021-05-11 | 三一机器人科技有限公司 | AGV (automatic guided vehicle) backward pallet and obstacle identification method and device and AGV |
| US20210178845A1 (en) * | 2019-12-13 | 2021-06-17 | Hyundai Motor Company | Method and apparatus for controlling electronic control suspension |
| CN113361423A (en) * | 2021-06-11 | 2021-09-07 | 上海追势科技有限公司 | Active suspension adjusting method |
| CN113409467A (en) * | 2021-07-09 | 2021-09-17 | 华南农业大学 | Method, device, system, medium and equipment for detecting road surface unevenness |
| CN114619825A (en) * | 2020-12-10 | 2022-06-14 | 比亚迪股份有限公司 | Vehicle obstacle avoidance method and vehicle |
| CN114743181A (en) * | 2022-04-29 | 2022-07-12 | 重庆长安汽车股份有限公司 | Road vehicle target detection method and system, electronic device and storage medium |
-
2022
- 2022-09-09 CN CN202211103069.8A patent/CN115431695A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH05213033A (en) * | 1992-02-06 | 1993-08-24 | Mitsubishi Motors Corp | Camber angle control device for vehicle |
| CN107064955A (en) * | 2017-04-19 | 2017-08-18 | 北京汽车集团有限公司 | barrier clustering method and device |
| CN108256577A (en) * | 2018-01-18 | 2018-07-06 | 东南大学 | A kind of barrier clustering method based on multi-line laser radar |
| US20210178845A1 (en) * | 2019-12-13 | 2021-06-17 | Hyundai Motor Company | Method and apparatus for controlling electronic control suspension |
| CN111860321A (en) * | 2020-07-20 | 2020-10-30 | 浙江光珀智能科技有限公司 | Obstacle identification method and system |
| CN114619825A (en) * | 2020-12-10 | 2022-06-14 | 比亚迪股份有限公司 | Vehicle obstacle avoidance method and vehicle |
| CN112784799A (en) * | 2021-02-01 | 2021-05-11 | 三一机器人科技有限公司 | AGV (automatic guided vehicle) backward pallet and obstacle identification method and device and AGV |
| CN113361423A (en) * | 2021-06-11 | 2021-09-07 | 上海追势科技有限公司 | Active suspension adjusting method |
| CN113409467A (en) * | 2021-07-09 | 2021-09-17 | 华南农业大学 | Method, device, system, medium and equipment for detecting road surface unevenness |
| CN114743181A (en) * | 2022-04-29 | 2022-07-12 | 重庆长安汽车股份有限公司 | Road vehicle target detection method and system, electronic device and storage medium |
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