CN116312051A

CN116312051A - Speed prediction method, device, storage medium and electronic equipment

Info

Publication number: CN116312051A
Application number: CN202310279502.1A
Authority: CN
Inventors: 甘鑫; 覃高峰; 陆镱升; 林智桂; 何逸波
Original assignee: SAIC GM Wuling Automobile Co Ltd
Current assignee: SAIC GM Wuling Automobile Co Ltd
Priority date: 2023-03-21
Filing date: 2023-03-21
Publication date: 2023-06-23

Abstract

The embodiment of the invention provides a speed prediction method, a speed prediction device, a storage medium and electronic equipment. In the technical scheme provided by the embodiment of the invention, the method comprises the following steps: acquiring a first coordinate set of at least one obstacle, wherein the first coordinate set comprises first coordinates of a historical track point of a central point of the at least one obstacle, which is detected by an environment sensor of a vehicle, in a first coordinate system; obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of a historical track point of a central point of the at least one obstacle in a second coordinate system; obtaining a track fitting formula of a center point of a dynamic obstacle in the at least one obstacle according to the second coordinate set; and according to the track fitting formula, obtaining the predicted moving speed of the dynamic obstacle, so that the vehicle can detect the obstacle with high precision and low cost.

Description

Speed prediction method, device, storage medium and electronic equipment

[ field of technology ]

The present invention relates to the field of intelligent driving technologies, and in particular, to a speed prediction method, a speed prediction device, a storage medium, and an electronic device.

[ background Art ]

At present, detection of obstacles by vehicles can be divided into independent detection by using a laser radar and a mode of fusion detection by using the laser radar and a camera. The camera is high in resolution ratio, so that the technical difficulties of obstacle clustering, segmentation, type classification, feature tracking and the like can be accurately performed, and the laser radar overcomes the defect that the camera cannot accurately measure the distance due to excellent active ranging capability. The laser radar and camera fusion mode is used for detecting the obstacle, the characteristics of the camera and the laser radar can be considered, and the relatively accurate obstacle recognition and tracking capability improves the capabilities of unmanned vehicles in obstacle path prediction, collision early warning and the like, which are lacking in single laser radar sensors. Because the camera has high resolution, the detection of the obstacle by using the fusion mode of the laser radar and the camera can bring high calculation consumption, is unfavorable for reducing the manufacturing cost of the vehicle and is unfavorable for controlling the energy consumption of the later operation.

Therefore, the detection of obstacles by the current vehicles cannot achieve both high precision and low cost.

[ invention ]

In view of the above, the embodiments of the present invention provide a speed prediction method, apparatus, storage medium, and electronic device, so that a vehicle can detect an obstacle with both high accuracy and low cost.

In a first aspect, an embodiment of the present invention provides a speed prediction method, where the method includes:

acquiring a first coordinate set of at least one obstacle, wherein the first coordinate set comprises first coordinates of a historical track point of a central point of the at least one obstacle, which is detected by an environment sensor of a vehicle, in a first coordinate system;

obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of a historical track point of a central point of the at least one obstacle in a second coordinate system;

obtaining a track fitting formula of a center point of a dynamic obstacle in the at least one obstacle according to the second coordinate set;

and obtaining the predicted moving speed of the dynamic obstacle according to the track fitting formula.

Optionally, the environmental sensor is a single lidar.

Optionally, the obtaining a track fitting formula of the center point of the dynamic obstacle in the at least one obstacle according to the second coordinate set includes:

obtaining a third coordinate set according to the second coordinate set and the nondirectionality characteristic of the adjacent position points, wherein the third coordinate set comprises second coordinates of a dynamic obstacle in the at least one obstacle;

obtaining a target position array of the dynamic barrier according to the filtering condition and the third coordinate set;

and obtaining the track fitting formula according to the target position number sequence.

Optionally, the obtaining a third coordinate set according to the second coordinate set and the characteristic of the anisotropy of the adjacent position points includes:

determining the dynamic and static properties of the at least one obstacle according to the second coordinate set and the nondirectionality characteristic of the adjacent position points;

and filtering the second coordinates of the obstacle with static dynamic and static attributes in the second coordinate set to obtain the third coordinate set.

Optionally, the obtaining the target position number sequence of the dynamic obstacle according to the filtering condition and the third coordinate set includes:

obtaining a first position array of the dynamic barrier according to the third coordinate set, wherein the first position array comprises an abscissa position array and an ordinate position array in the third coordinate set;

obtaining the filtering condition according to the first position number sequence and the 3 sigma method;

and filtering the abscissa position array and the ordinate position array according to the filtering condition to obtain a target position array, wherein the target position array comprises a target abscissa position array and a target ordinate position array.

Optionally, the obtaining the track fitting formula according to the target position array includes:

obtaining a horizontal coordinate track fitting formula of the central point of the dynamic barrier according to the target horizontal coordinate position array;

and obtaining an ordinate track fitting formula of the central point of the dynamic barrier according to the target ordinate position array.

Optionally, the obtaining the predicted moving speed of the dynamic obstacle according to the trajectory fitting formula includes:

obtaining the predicted abscissa moving speed of the dynamic barrier according to the abscissa track fitting formula;

obtaining the predicted ordinate moving speed of the dynamic barrier according to the ordinate track fitting formula;

and obtaining the predicted moving speed of the dynamic obstacle according to the predicted abscissa moving speed and the predicted ordinate moving speed.

In another aspect, an embodiment of the present invention provides a speed prediction apparatus, including:

an acquisition module for acquiring a first set of coordinates of at least one obstacle, the first set of coordinates including a first coordinate of a historical track point of a center point of the at least one obstacle in a first coordinate system, the first coordinate being detected by an environmental sensor of a vehicle;

the conversion module is used for obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of the historical track points of the central points of the at least one obstacle in a second coordinate system;

the track fitting module is used for obtaining a track fitting formula of the center point of the dynamic obstacle in the at least one obstacle according to the second coordinate set;

and the prediction module is used for obtaining the predicted moving speed of the dynamic obstacle according to the track fitting formula.

In another aspect, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, and when the program runs, the device on which the storage medium is controlled to execute the above speed prediction method.

In another aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is configured to store information including program instructions, and the processor is configured to control execution of the program instructions, where the program instructions, when loaded and executed by the processor, implement the steps of the speed prediction method described above.

In the technical schemes of the speed prediction method, the device, the storage medium and the electronic equipment provided by the embodiment of the invention, the method comprises the following steps: acquiring a first coordinate set of at least one obstacle, wherein the first coordinate set comprises first coordinates of a historical track point of a central point of the at least one obstacle, which is detected by an environment sensor of a vehicle, in a first coordinate system; obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of a historical track point of a central point of the at least one obstacle in a second coordinate system; obtaining a track fitting formula of a center point of a dynamic obstacle in the at least one obstacle according to the second coordinate set; and according to the track fitting formula, obtaining the predicted moving speed of the dynamic obstacle, so that the vehicle can detect the obstacle with high precision and low cost.

[ description of the drawings ]

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a speed prediction method according to an embodiment of the present invention;

FIG. 2 is a flowchart showing a trajectory fitting formula for obtaining a center point of a dynamic obstacle in at least one obstacle according to the second coordinate set in FIG. 1;

FIG. 3 is a flowchart showing a third coordinate set according to the second coordinate set and the non-directional characteristic of adjacent position points in FIG. 2, wherein the third coordinate set includes the second coordinates of the dynamic obstacle in at least one obstacle;

FIG. 4 is a schematic view of at least one obstruction in an embodiment of the invention;

FIG. 5 is a flowchart showing the specific process of obtaining the target position array of the dynamic barrier according to the filtering condition and the third coordinate set in FIG. 2;

FIG. 6 is a flowchart showing a track fitting formula according to the target position array in FIG. 2;

FIG. 7 is a flowchart showing the predicted moving speed of the dynamic obstacle according to the trajectory fitting formula in FIG. 1;

FIG. 8 is a schematic diagram of a speed predicting apparatus according to an embodiment of the present invention;

fig. 9 is a schematic diagram of an electronic device according to an embodiment of the invention.

[ detailed description ] of the invention

For a better understanding of the technical solution of the present invention, the following detailed description of the embodiments of the present invention refers to the accompanying drawings.

It should be understood that the described embodiments are merely some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be understood that the term "and/or" as used herein is merely one way of describing an association of associated objects, meaning that there may be three relationships, e.g., a and/or b, which may represent: the first and second cases exist separately, and the first and second cases exist separately. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.

Intelligent driving is a topic that is discussed more widely in current social development. The intelligent driving vehicle means that the vehicle has certain intelligent capability and can have certain capability of autonomously handling problems on a road, such as autonomous cruising, autonomous lane changing, autonomous braking reduction and the like. Different intelligent driving capabilities may have different levels of intelligence. The highest-level intelligent driving capability is an unmanned technology, the vehicle can fully and autonomously sense external factors such as positions, environments, obstacles and the like, has autonomous behavior decision-making capability, route planning capability and the like, and can autonomously complete specific task targets.

The existing technology accumulates the unmanned capability of vehicles on specific road sections, but because of the complex social roads and the multiple participation road roles, unnecessary personnel injury is easily caused. In addition to the limitations of social laws and regulations, vehicles using unmanned technology on public roads are limited.

The unmanned logistics vehicle has the characteristics of more standard traffic environment, relatively closed road environment rules, fixed point-to-point route, high strength, high-frequency use requirement and the like due to the specificity of the factory running environment, and is rapidly developed in unmanned aspect.

The unmanned logistics vehicle is generally provided with sensors and computing equipment such as a laser radar, a camera, a positioning system, a domain controller and the like, and can meet functions necessary for autonomous positioning, environment perception, behavior decision and autonomous operation such as route planning and control of the vehicle. Among them, the judgment and tracking of the obstacle by the vehicle are extremely important for the safety of the vehicle operation.

At present, unmanned logistics vehicles can be used for detecting obstacles in a mode of independently detecting the obstacles by using a laser radar and detecting the obstacles by using fusion of the laser radar and a camera. The camera is high in resolution ratio, so that the technical difficulties of obstacle clustering, segmentation, type classification, feature tracking and the like can be accurately performed, and the laser radar overcomes the defect that the camera cannot accurately measure the distance due to excellent active ranging capability. The relatively accurate obstacle recognition and tracking capability improves the capability of unmanned vehicles in obstacle path prediction, collision early warning and the like, which are lacking in single laser radar sensors.

However, the layout of the multiple sensors requires higher assembly accuracy of the unmanned vehicle in the manufacturing and mounting processes, and each vehicle needs to be subjected to operations such as sensor calibration and calibration, so that the manufacturing cost is high, the process is complex, and the maintenance is difficult. The logistics vehicle has the advantages that the use frequency is high, the working environment is complex, the working environments such as dragging, vibration and heavy load are more, the use of the sensor is reduced, the safe operation mileage of the vehicle can be improved, the maintenance time of the vehicle is shortened, and the operation efficiency and the utilization rate of the vehicle are improved.

While the visual perception system relying on the camera has high resolution, the high computational consumption required for high resolution images is brought. The vision sensing system needs to use a high-power-consumption computing chip for matching computation, which is not beneficial to reducing the manufacturing cost of the unmanned vehicle and is also not beneficial to the energy consumption control of the later operation.

In summary, the detection of obstacles by the existing vehicles cannot achieve both high precision and low cost.

Based on the technical problems, the embodiment of the invention provides a speed prediction method, a speed prediction device, a storage medium and electronic equipment, so that a vehicle can detect obstacles with high precision and low cost.

Fig. 1 is a flowchart of a speed prediction method according to an embodiment of the present invention, as shown in fig. 1, where the method includes:

step 101, a first coordinate set of at least one obstacle is obtained, wherein the first coordinate set comprises first coordinates of a historical track point of a central point of the at least one obstacle detected by an environment sensor of a vehicle in a first coordinate system.

The environmental sensor is a single lidar, such as a 16-line lidar.

Vehicles use a single lidar to detect obstacles in the environment. Because of the low laser radar point cloud density, the vehicle contour cannot be accurately segmented, and then obstacle tracking cannot be prepared, namely, the obstacle confirmed at the moment and the obstacle confirmed at the next moment cannot be accurately confirmed as one obstacle, particularly in an irregular production environment with more obstacles. It is therefore necessary to determine the obstacle speed using a design vehicle position point determination and speed prediction method.

For example, a 16-line laser radar is used as a vehicle environment detection sensor, the laser radar can judge the point cloud position of an obstacle in the surrounding environment of a vehicle through 360-degree rotation scanning and calculation of reflection information of the emitted laser beam, and then the point position calculation is carried out according to the laser radar point cloud obtained by laser radar detection, so that which part of the point cloud is an independent object is comprehensively obtained.

Step 102, obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of the historical track points of the central points of at least one obstacle in a second coordinate system.

In some possible embodiments, step 102 specifically includes: and converting the first coordinates in the first coordinate set into the second coordinates according to the conversion relation between the first coordinate system and the second coordinate system to obtain a second coordinate set.

The first coordinate system is a vehicle coordinate system, and the second coordinate system is a world coordinate system.

In order to obtain enough obstacle samples, the embodiment of the invention performs coordinate transformation on the historical track point coordinates of all the detected obstacle center points to obtain the positions of the historical track points of the obstacle center points in a world coordinate system. The obstacle center point position at each moment is then recorded in the data memory based on the obstacle id identification as a basis for classification.

And 103, obtaining a track fitting formula of the center point of the dynamic obstacle in the at least one obstacle according to the second coordinate set.

In some possible embodiments, as shown in fig. 2, step 103 specifically includes:

step 1031, obtaining a third coordinate set according to the second coordinate set and the non-directional characteristic of the adjacent position points, wherein the third coordinate set comprises the second coordinates of the dynamic obstacle in the at least one obstacle.

The key to single lidar detection of obstacle speed is the distinction of whether an obstacle is a dynamic obstacle or a static obstacle. Because the laser radar point cloud density causes, the outer contour of the obstacle cannot be accurately identified, the central positions of the same obstacle in different time periods cannot be accurately distinguished, and if the central positions are directly used for calculating the obstacle speed, the static obstacle is likely to generate a large obstacle speed due to the jumping of the central points. Therefore, the embodiment of the invention needs to screen out dynamic obstacles in the environment.

In some possible embodiments, as shown in fig. 3, step 1031 specifically includes:

and step 1a, determining the dynamic and static properties of at least one obstacle according to the second coordinate set and the nondirectional property of the adjacent position points.

The static obstacle distribution conforms to a general probability distribution, i.e. the center points of the static obstacles all fall within a small range of positions, and the direction between adjacent position points is isotropic. The embodiment of the invention uses the characteristic of the non-directionality of the adjacent position points to determine the dynamic and static properties of the barrier according to the second coordinate set. Fig. 4 is a schematic diagram of at least one obstacle according to an embodiment of the present invention, and as shown in fig. 4, a static obstacle and a dynamic obstacle in the at least one obstacle are determined in step 1 a.

And step 1b, filtering the second coordinates of the obstacle with static dynamic and static attribute in the second coordinate set to obtain a third coordinate set.

After determining the dynamic and static properties of at least one obstacle, the embodiment of the invention firstly filters the second coordinates of the static obstacle in the second coordinate set.

Step 1032, obtaining the target position array of the dynamic barrier according to the filtering condition and the third coordinate set.

The second coordinates in the third coordinate set are all the coordinates of the historical track points of the center point of the dynamic obstacle, and the center position points of the dynamic obstacle are distributed linearly, but the positions of the center position points of the dynamic obstacle are also dithered and scattered, so that the center position points of the dynamic obstacle need to be filtered again.

In some possible embodiments, as shown in fig. 5, step 1032 specifically includes:

and 2a, obtaining a first position array of the dynamic barrier according to the third coordinate set, wherein the first position array comprises an abscissa position array and an ordinate position array in the third coordinate set.

Firstly, the abscissa and the ordinate, namely the X-coordinate and the Y-coordinate, of the central position point of the dynamic barrier in the third coordinate set are stored separately, and two position number columns, namely an abscissa position number column X and an ordinate position number column Y, are obtained as follows:

X＝[x ₀ ，x ₁ ，x ₂ ，…，x _n-1 ，x _n ]

Y＝[y ₀ ，y ₁ ，y ₂ ，…，y _n-1 ，y _n ]

and 2b, obtaining the filtering condition according to the first position array and the 3 sigma method.

The embodiment of the invention uses a 3 sigma method to filter the coordinates of the dynamic obstacle. Assuming that the frame position difference before and after the obstacle center point position conforms to the normal distribution, then the obstacle position (x _T ,y _T ) And the obstacle position (x) of period T-1 _T-1 ,y _T-1 ) The probability that the distance difference is less than three times the variance is 98%, so the vehicle history track is circularly calculated, and the position points which do not meet the condition are filtered, wherein the filtering condition is as follows:

△X _T >3σ _△x

or

△Y _T >3σ _△Y

wherein sigma _△x X-seat for adjacent position point of obstacleVariance, sigma of target difference _△y Is the variance of the difference in y-coordinates of points adjacent to the obstacle.

And 2c, filtering the abscissa position array and the ordinate position array according to the filtering condition to obtain a target position array, wherein the target position array comprises a target abscissa position array and a target ordinate position array.

According to the embodiment of the invention, the abscissa position array and the ordinate position array are filtered according to the filtering condition to obtain the target position array.

And 1033, obtaining a track fitting formula according to the target position number sequence.

After the target position array is obtained, obstacle track fitting can be performed, and a track fitting formula is obtained.

The trajectory fitting formula includes an abscissa trajectory fitting formula and an ordinate trajectory fitting formula.

In some possible embodiments, as shown in fig. 6, step 1033 specifically includes:

and 3a, obtaining a horizontal coordinate track fitting formula of the central point of the dynamic obstacle according to the target horizontal coordinate position array.

And 3b, obtaining a ordinate track fitting formula of the central point of the dynamic barrier according to the ordinate position array of the target.

According to the embodiment of the invention, the abscissa track and the ordinate track of the obstacle are fitted independently according to the abscissa position array and the ordinate position array respectively, so that the functional relation between the abscissa and the time of the center point of the dynamic obstacle is obtained, namely, the abscissa track fitting formula is as follows:

X＝a _x T ³ +b _x T ² +c _x T+d _x

and obtaining a functional relation between the ordinate of the center point of the dynamic obstacle and time, namely, a ordinate track fitting formula is as follows:

Y＝a _y T ³ +b _y T ² +c _y T+d _y

and 104, obtaining the predicted moving speed of the dynamic obstacle according to a track fitting formula.

In some possible embodiments, as shown in fig. 7, step 104 specifically includes:

step 1041, obtaining a predicted abscissa moving speed of the dynamic obstacle according to an abscissa track fitting formula.

Step 1042, obtaining the predicted ordinate moving speed of the dynamic obstacle according to the ordinate track fitting formula.

Step 1043, obtaining a predicted moving speed of the dynamic obstacle according to the predicted abscissa moving speed and the predicted ordinate moving speed.

For example, the time after one second in the future is brought into an abscissa track fitting formula and an ordinate track fitting formula to calculate, so that the coordinates of the center point of the obstacle can be obtained one second after the current moment, and the predicted abscissa moving speed and the predicted ordinate moving speed of the dynamic obstacle can be calculated by calculating the position deviation of the center point of the obstacle within 1 second as follows:

combining the predicted abscissa moving speed and the predicted ordinate moving speed to obtain the speed of the dynamic obstacle as follows:

according to the embodiment of the invention, the 16-line laser radar is used as an environmental sensor, so that the dependence of a vehicle on a camera is reduced, and the manufacturing cost of the vehicle is reduced; the multi-sensor fusion calibration is not needed, and the vehicle manufacturing and remanufacturing speed is accelerated; the vehicle environment adaptability is improved, and the problem that the vehicle cannot be used due to sensor faults is solved; the obstacle speed prediction sensing is carried out by only adopting laser radar data, so that the calculation load on the master control chip of the unmanned logistics system is small.

The embodiment of the invention performs basic point cloud clustering and tracking operation on the detected obstacle point cloud; storing the obstacle information obtained by detection; performing bad point removing operation on the historical track points of the obstacle; fitting the obstacle track points, and judging the fitting effect; and obtaining the final movement trend of the vehicle according to the fitting effect, obtaining the vehicle speed, reducing the calculation pressure of a vehicle control system and reducing the manufacturing and purchasing costs of the central controller. And (3) recording and fitting the historical track points of the obstacle, and filtering and correcting the estimated speed and position of the obstacle by considering the position information of the historical track of the obstacle, so that the estimated accuracy of the speed of the obstacle is improved. The use of the vehicle-mounted camera is canceled, the calculation load of the system is reduced, and the running efficiency of the program is improved while the equivalent precision can be achieved.

In the technical scheme of the speed prediction method provided by the embodiment of the invention, the method comprises the following steps: acquiring a first coordinate set of at least one obstacle, wherein the first coordinate set comprises first coordinates of a historical track point of a central point of the at least one obstacle detected by an environment sensor of a vehicle in a first coordinate system; obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of the historical track points of the central points of at least one obstacle in a second coordinate system; obtaining a track fitting formula of a center point of the dynamic obstacle in at least one obstacle according to the second coordinate set; and according to a track fitting formula, the predicted moving speed of the dynamic obstacle is obtained, so that the vehicle can detect the obstacle with high precision and low cost.

Fig. 8 is a schematic structural diagram of a speed predicting apparatus according to an embodiment of the present invention, as shown in fig. 8, where the apparatus includes: an acquisition module 41, a conversion module 42, a trajectory fitting module 43 and a prediction module 44.

An acquisition module 41 for acquiring a first coordinate set of at least one obstacle, the first coordinate set including a first coordinate of a history track point of a center point of the at least one obstacle in a first coordinate system detected by an environmental sensor of a vehicle;

a conversion module 42, configured to obtain a second coordinate set according to the first coordinate set, where the second coordinate set includes second coordinates of a historical track point of a center point of the at least one obstacle in a second coordinate system;

the track fitting module 43 is configured to obtain a track fitting formula of a center point of the dynamic obstacle in the at least one obstacle according to the second coordinate set;

and the prediction module 44 is configured to obtain a predicted moving speed of the dynamic obstacle according to the trajectory fitting formula.

Alternatively, the environmental sensor is a single lidar.

Optionally, the track fitting module 43 is specifically configured to obtain a third coordinate set according to the second coordinate set and the characteristic of the anisotropy of the adjacent position points, where the third coordinate set includes a second coordinate of a dynamic obstacle in the at least one obstacle; obtaining a target position array of the dynamic barrier according to the filtering condition and the third coordinate set; and obtaining the track fitting formula according to the target position number sequence.

Optionally, the track fitting module 43 is specifically configured to determine a dynamic-static attribute of the at least one obstacle according to the second coordinate set and the characteristic of the anisotropy of the adjacent position points; and filtering the second coordinates of the obstacle with static dynamic and static attributes in the second coordinate set to obtain the third coordinate set.

Optionally, the track fitting module 43 is specifically configured to obtain a first position sequence of the dynamic obstacle according to the third coordinate set, where the first position sequence includes an abscissa position sequence and an ordinate position sequence in the third coordinate set; obtaining the filtering condition according to the first position number sequence and the 3 sigma method; and filtering the abscissa position array and the ordinate position array according to the filtering condition to obtain a target position array, wherein the target position array comprises a target abscissa position array and a target ordinate position array.

Optionally, the track fitting module 43 is specifically configured to obtain an abscissa track fitting formula of the center point of the dynamic obstacle according to the target abscissa position array; and obtaining an ordinate track fitting formula of the central point of the dynamic barrier according to the target ordinate position array.

Optionally, the prediction module 44 is specifically configured to obtain a predicted abscissa moving speed of the dynamic obstacle according to the abscissa track fitting formula; obtaining the predicted ordinate moving speed of the dynamic barrier according to the ordinate track fitting formula; and obtaining the predicted moving speed of the dynamic obstacle according to the predicted abscissa moving speed and the predicted ordinate moving speed.

The speed prediction apparatus provided in the embodiment of the present invention may be used to implement the speed prediction methods in fig. 1 to 2, and the specific description may refer to the embodiments of the speed prediction methods, and the description will not be repeated here.

In the technical scheme of the speed prediction device provided by the embodiment of the invention, a first coordinate set of at least one obstacle is obtained, wherein the first coordinate set comprises first coordinates of a historical track point of a central point of the at least one obstacle detected by an environment sensor of a vehicle in a first coordinate system; obtaining a second coordinate set according to the first coordinate set, wherein the second coordinate set comprises second coordinates of a historical track point of a central point of the at least one obstacle in a second coordinate system; obtaining a track fitting formula of a center point of a dynamic obstacle in the at least one obstacle according to the second coordinate set; and according to the track fitting formula, obtaining the predicted moving speed of the dynamic obstacle, so that the vehicle can detect the obstacle with high precision and low cost.

FIG. 9 is a schematic diagram of an electronic device according to an embodiment of the present invention, where the electronic device may include at least one processor as shown in FIG. 9; and at least one memory communicatively coupled to the processor, wherein: the memory stores program instructions executable by a processor that invokes the program instructions to perform the speed prediction methods provided in the embodiments of fig. 1-7 of the present specification.

Fig. 9 shows a block diagram of an exemplary electronic device suitable for use in implementing embodiments of the present description. The electronic device shown in fig. 9 is only an example, and should not be construed as limiting the functionality and scope of use of the embodiments herein.

As shown in fig. 9, the electronic device is in the form of a general purpose computing device. Components of an electronic device may include, but are not limited to: one or more processors 21, a memory 23, and a communication bus 24 that connects the various system components, including the memory 23 and the processing unit 21.

Communication bus 24 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry Standard architecture (Industry Standard Architecture; hereinafter ISA) bus, micro channel architecture (Micro Channel Architecture; hereinafter MAC) bus, enhanced ISA bus, video electronics standards Association (Video Electronics Standards Association; hereinafter VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnection; hereinafter PCI) bus.

Electronic devices typically include a variety of computer system readable media. Such media can be any available media that can be accessed by the electronic device and includes both volatile and nonvolatile media, removable and non-removable media.

The memory 23 may include computer system readable media in the form of volatile memory, such as random access memory (Random Access Memory; hereinafter: RAM) and/or cache memory. The electronic device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. The memory 23 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the present description.

A program/utility having a set (at least one) of program modules may be stored in the memory 23, such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules typically carry out the functions and/or methods of the embodiments described herein.

The processor 21 executes a program stored in the memory 23 to thereby perform various functional applications and data processing, for example, to realize the speed prediction method provided in the embodiment shown in fig. 1 to 7 of the present specification.

The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute the speed prediction methods provided by the embodiments shown in fig. 1 to 7 of the present description.

The non-transitory computer readable storage media described above may employ any combination of one or more computer readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having 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 (Erasable Programmable Read Only Memory; EPROM) or flash Memory, an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for the present specification may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (Local Area Network; hereinafter: LAN) or a wide area network (Wide Area Network; hereinafter: WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present specification, the meaning of "plurality" means at least two, for example, two, three, etc., unless explicitly defined otherwise.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present specification in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present specification.

Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.

It should be noted that, the terminals in the embodiments of the present disclosure may include, but are not limited to, a personal Computer (Personal Computer; hereinafter referred to as a PC), a personal digital assistant (Personal Digital Assistant; hereinafter referred to as a PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a mobile phone, an MP3 player, an MP4 player, and the like.

In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

In addition, each functional unit in each embodiment of the present specification may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.

The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a Processor (Processor) to perform part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (hereinafter referred to as ROM), a random access Memory (Random Access Memory) and various media capable of storing program codes such as a magnetic disk or an optical disk.

The foregoing description of the preferred embodiments is provided for the purpose of illustration only, and is not intended to limit the scope of the disclosure, since any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the disclosure are intended to be included within the scope of the disclosure.

Claims

1. A method of speed prediction, the method comprising:

2. The method of claim 1, wherein the environmental sensor is a single lidar.

3. The method of claim 1, wherein the deriving a trajectory fitting formula for the center point of the dynamic obstacle in the at least one obstacle from the second set of coordinates comprises:

4. A method according to claim 3, wherein said obtaining a third coordinate set based on said second coordinate set and the property of being non-directional of adjacent location points comprises:

5. A method according to claim 3, wherein said deriving a target position array of said dynamic obstacle from said filtering condition and said third set of coordinates comprises:

6. The method of claim 5, wherein said deriving the trajectory fitting formula from the array of target locations comprises:

7. The method of claim 6, wherein deriving the predicted movement velocity of the dynamic obstacle from the trajectory fitting formula comprises:

8. A speed prediction apparatus, the apparatus comprising:

9. A storage medium comprising a stored program, wherein the program, when run, controls a device in which the storage medium is located to perform the method of any one of claims 1-7.

10. An electronic device comprising a memory for storing information including program instructions and a processor for controlling execution of the program instructions, wherein the program instructions, when loaded and executed by the processor, implement the steps of the method of any one of claims 1-7.