Obstacle position prediction method, device and electronic equipment

Abstract

The application provides an obstacle position prediction method and device, electronic equipment, computer readable storage medium and computer program product; the method comprises the following steps: fitting a trajectory curve according to actual positions of an obstacle corresponding to a plurality of historical moments respectively, and obtaining a first predicted position of the obstacle at a target moment in the future according to the trajectory curve; obtaining a second predicted position of the obstacle at the target moment according to kinematic information of the obstacle at a current moment; performing weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target moment to obtain a third predicted position of the obstacle at the target moment. Through the application, accurate obstacle position prediction can be realized at a low cost.

Description

Technical Field

[0001] The present application relates to vehicle technology, and in particular to an obstacle position prediction method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Autonomous driving is an advanced transportation technology that allows vehicles to complete driving tasks without human intervention. It integrates the achievements of numerous fields, including automatic control, architecture, artificial intelligence, and visual computing. It is the product of highly developed computer science, pattern recognition, and intelligent control technologies.

[0003] Autonomous driving technology typically consists of three key components: perception, prediction, and planning and control. Within the prediction component, obstacle position prediction plays a crucial supporting role. Solutions offered by related technologies typically predict obstacle trajectories based on big data models. However, this approach is heavily dependent on the quality of the training dataset. Furthermore, the scene classification method, the size of the scene library, and the comprehensiveness of the scene library also directly impact the effectiveness of obstacle trajectory prediction. Summary of the Invention

[0004] The present application provides an obstacle position prediction method, device, electronic device, computer-readable storage medium, and computer program product, which can achieve accurate obstacle position prediction at a relatively low cost.

[0005] The technical solution of this application is achieved as follows:

[0006] This application provides an obstacle position prediction method, comprising:

[0007] Fitting a trajectory curve according to the actual positions of the obstacle at multiple historical moments, and predicting a first predicted position of the obstacle at a future target moment based on the trajectory curve;

[0008] Predicting a second predicted position of the obstacle at the target time based on the kinematic information of the obstacle at the current time;

[0009] A weighted summation process is performed on the first predicted position and the second predicted position of the obstacle at the target time to obtain a third predicted position of the obstacle at the target time.

[0010] The present application provides an obstacle position prediction device, comprising:

[0011] A first prediction module is configured to fit a trajectory curve according to the actual positions of the obstacle at multiple historical moments, and predict a first predicted position of the obstacle at a future target moment based on the trajectory curve;

[0012] a second prediction module, configured to predict a second predicted position of the obstacle at the target time according to kinematic information of the obstacle at a current time;

[0013] a weighted sum module, configured to perform weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target time, to obtain a third predicted position of the obstacle at the target time.

[0014] The application provides an electronic device, comprising:

[0015] a memory, configured to store executable instructions;

[0016] a processor, configured to execute the executable instructions stored in the memory, to implement the obstacle position prediction method provided by the application.

[0017] The application provides a computer readable storage medium, which stores executable instructions, and is used to cause the processor to execute, to implement the obstacle position prediction method provided by the application.

[0018] The application provides a computer program product, which comprises executable instructions, and is used to cause the processor to execute, to implement the obstacle position prediction method provided by the application.

[0019] The application has the following beneficial effects:

[0020] The application fits a trajectory curve according to the actual positions of the obstacle at a plurality of historical times respectively, and predicts a first predicted position of the obstacle at a target time in the future according to the trajectory curve; predicts a second predicted position of the obstacle at the target time according to kinematic information of the obstacle at a current time; and performs weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target time, to obtain a third predicted position of the obstacle at the target time. The application comprehensively considers the trajectory curve and the kinematic information, can make the third predicted position of the obstacle at the target time obtained finally more accurate, and is helpful to improve the obstacle avoidance effect in the automatic driving process. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0022] Figure 1 is an architecture schematic diagram of the obstacle position prediction system provided by the embodiments of the application;

[0023] Figure 2 This is a structural diagram of the vehicle-mounted device provided in an embodiment of the present application;

[0024] Figure 3A This is a schematic diagram of the first flow chart of the obstacle position prediction method provided in an embodiment of the present application;

[0025] Figure 3B 2 is a schematic diagram of a second flow chart of the obstacle position prediction method provided in an embodiment of the present application;

[0026] Figure 3C 3 is a schematic diagram of a third flow chart of the obstacle position prediction method provided in an embodiment of the present application;

[0027] Figure 4 This is a fourth flow chart of the obstacle position prediction method provided in an embodiment of the present application;

[0028] Figure 5 This is a schematic diagram of establishing a geodetic coordinate system provided by an embodiment of the present application;

[0029] Figure 6 This is a schematic diagram of recording obstacle information provided by an embodiment of the present application;

[0030] Figure 7 This is a schematic diagram of predicting a position by interpolation fitting provided in an embodiment of the present application;

[0031] Figure 8 This is a schematic diagram of position prediction using a kinematic model provided in an embodiment of the present application;

[0032] Figure 9 This is a schematic diagram of performing weighted sum processing on the first predicted position and the second predicted position to obtain a third predicted position provided by an embodiment of the present application;

[0033] Figure 10 This is a first schematic diagram of the obstacle avoidance decision-making provided by an embodiment of the present application;

[0034] Figure 11 This is a second schematic diagram of the obstacle avoidance decision provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0036] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. In the following description, the term "plurality" refers to at least two.

[0037] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0039] The present invention provides an obstacle location prediction method, apparatus, electronic device, computer-readable storage medium, and computer program product, which can achieve accurate obstacle location prediction at a low cost. The following describes exemplary applications of the electronic device provided in the present invention. The electronic device provided in the present invention can be implemented as various types of terminal devices or as a server.

[0040] See also Figure 1 , Figure 1 This is a schematic diagram of the architecture of the obstacle position prediction system 100 provided in an embodiment of the present application. The vehicle-mounted device 400 is connected to the server 200 via the network 300, wherein the network 300 can be a wide area network or a local area network, or a combination of the two.

[0041] In some embodiments, taking the electronic device as a terminal device as an example, the obstacle position prediction method provided in the embodiment of the present application can be implemented by the terminal device. For example, the vehicle-mounted device 400 deployed in the vehicle obtains the obstacle position through the vehicle's perception module ( Figure 1The actual positions of the obstacles corresponding to multiple historical moments respectively and the kinematics information of the obstacles at the current moment, wherein the perception module can include a single sensor or multiple sensors (such as a camera + radar); the vehicle-mounted device 400 fits a trajectory curve according to the actual positions of the obstacles corresponding to multiple historical moments respectively, and obtains a first predicted position of the obstacles at a target moment in the future according to the trajectory curve; the vehicle-mounted device 400 obtains a second predicted position of the obstacles at the target moment according to the kinematics information of the obstacles at the current moment; the vehicle-mounted device 400 performs weighted sum processing on the first predicted position and the second predicted position of the obstacles at the target moment, and obtains a third predicted position of the obstacles at the target moment. Then, the vehicle-mounted device 400 can plan to obtain a control strategy (for example, whether to brake the vehicle) according to the third predicted position of the obstacles at the target moment, and control the vehicle according to the control strategy. In the above manner, the vehicle-mounted device 400 locally realizes obstacle position prediction, without the need to transmit data to a remote server for processing, which can greatly reduce the time and delay of data transmission, thereby improving the real-time performance of processing and improving the response speed; at the same time, it is not limited by network conditions, which is particularly important for scenarios where the network environment is unstable or cannot be connected to the network, and can ensure the continuity and availability of obstacle position prediction.

[0042] In some embodiments, taking the electronic device as an example, the server, the obstacle position prediction method provided by the embodiments of the present application can be realized by the server. For example, the vehicle-mounted device 400 sends the actual positions of the obstacles corresponding to multiple historical moments respectively and the kinematics information of the obstacles at the current moment to the server 200; the server 200 fits a trajectory curve according to the actual positions of the obstacles corresponding to multiple historical moments respectively, and obtains a first predicted position of the obstacles at a target moment in the future according to the trajectory curve; the server 200 obtains a second predicted position of the obstacles at the target moment according to the kinematics information of the obstacles at the current moment; the server 200 performs weighted sum processing on the first predicted position and the second predicted position of the obstacles at the target moment, and obtains a third predicted position of the obstacles at the target moment. Then, the server 200 can plan to obtain a control strategy according to the third predicted position of the obstacles at the target moment, and send the control strategy to the vehicle-mounted device 400 for execution; or the server 200 can send the third predicted position of the obstacles at the target moment to the vehicle-mounted device 400, so that the vehicle-mounted device 400 plans to obtain a control strategy and executes the control strategy. In the above manner, the server 200 usually has powerful computing capability and storage resources, and can efficiently perform obstacle position prediction; the vehicle-mounted device 400 does not need to bear complex computing tasks, and can reduce the resource consumption of the vehicle-mounted device 400.

[0043] In some embodiments, an electronic device can implement the obstacle location prediction method provided in the embodiments of the present application by running a computer program. For example, the computer program can be a native program or software module in the operating system; it can be a native application (APP), that is, a program that needs to be installed in the operating system to run; it can also be a small program, that is, a program that can be run by simply downloading it into a browser environment; it can also be a small program that can be embedded in any APP, and the small program can be controlled by the user to run or close. In short, the above-mentioned computer program can be any form of application, module or plug-in.

[0044] Taking the electronic device provided in the embodiment of the present application as an example, which is a vehicle-mounted device, it can be understood that, for the case where the electronic device is a server, Figure 2 Parts of the structure shown in FIG (such as the user interface, the presentation module, and the input processing module) may be omitted. Figure 2 , Figure 2 is a structural diagram of the vehicle-mounted device 400 provided in an embodiment of the present application, Figure 2 The vehicle-mounted device 400 shown includes: at least one processor 410, a memory 450, at least one network interface 420, and a user interface 430. The various components in the vehicle-mounted device 400 are coupled together via a bus system 440. It is understood that the bus system 440 is used to achieve connection and communication between these components. In addition to including a data bus, the bus system 440 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 440 is not shown in FIG. Figure 2 Various buses are labeled as bus system 440 .

[0045] The processor 410 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0046] The user interface 430 includes one or more output devices 431 that enable presentation of media content, including one or more speakers and / or one or more visual display screens. The user interface 430 also includes one or more input devices 432, including user interface components that facilitate user input, such as a microphone, a touch screen display, a camera, other input buttons and controls.

[0047] The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical drives, etc. The memory 450 may optionally include one or more storage devices that are physically remote from the processor 410.

[0048] The memory 450 includes volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 450 described in the embodiments of the present application is intended to include any suitable type of memory.

[0049] In some embodiments, the memory 450 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplified below.

[0050] Operating system 451, including system programs for processing various basic system services and performing hardware-related tasks, such as the framework layer, core library layer, and driver layer, which are used to implement various basic services and process hardware-based tasks;

[0051] A network communication module 452 is used to reach other electronic devices via one or more (wired or wireless) network interfaces 420. Exemplary network interfaces 420 include Bluetooth, Wi-Fi, and Universal Serial Bus (USB).

[0052] a presentation module 453 for enabling presentation of information via one or more output devices 431 (e.g., a display screen, a speaker, etc.) associated with the user interface 430 (e.g., a user interface for operating peripheral devices and displaying content and information);

[0053] The input processing module 454 is configured to detect one or more user inputs or interactions from one of the one or more input devices 432 and to translate the detected inputs or interactions.

[0054] In some embodiments, the obstacle position prediction device provided in the embodiments of the present application can be implemented in software. Figure 2The obstacle position prediction device 455 stored in the memory 450 is shown, which can be software in the form of programs and plug-ins, etc., including the following software modules: a first prediction module 4551, a second prediction module 4552, and a weighted summation module 4553, which are logical, and thus can be combined or further split according to the implemented functions. The functions of the respective modules will be described below.

[0055] The obstacle position prediction method provided in the embodiments of the present application will be described in combination with the exemplary application and implementation of the electronic device provided in the embodiments of the present application.

[0056] Referring to Figure 3A , Figure 3A is a flowchart of the obstacle position prediction method provided in the embodiments of the present application, which will be described in combination with the steps shown in Figure 3A .

[0057] In step 101, a trajectory curve is fitted according to the actual positions of the obstacle corresponding to a plurality of historical time points, respectively, and a first predicted position of the obstacle at a target time point in the future is predicted according to the trajectory curve.

[0058] Here, the actual positions of the obstacle corresponding to a plurality of historical time points, respectively, are obtained, and in the case of a vehicle scenario, the actual positions of the obstacle corresponding to a plurality of historical time points, respectively, can be obtained through a perception module of the vehicle, wherein the perception module can include a single sensor or a plurality of sensors (such as a camera + radar).

[0059] Then, a trajectory curve is fitted according to the actual positions of the obstacle corresponding to a plurality of historical time points, respectively, that is, a continuous curve that can best approximate a series of discrete data points (the actual positions of the obstacle corresponding to a plurality of historical time points, respectively) is found through a mathematical method to describe or predict the potential relationship or trend between the data points. The way to fit the trajectory curve is not limited, for example, polynomial fitting can be used. The actual positions of the obstacle corresponding to a plurality of historical time points, respectively, are described in the same coordinate system, so as to ensure the fitting effect.

[0060] The fitted prediction curve describes the relationship between time and the position of the obstacle, and thus the predicted position of the obstacle at a target time point in the future can be predicted according to the trajectory curve (in order to distinguish, the predicted position obtained here is named as a first predicted position).

[0061] It is worth noting that the historical time points referred to in the embodiments of the present application refer to time points that have been experienced, and the most recent one of the plurality of historical time points can be the current time point.

[0062] In step 102, a second predicted position of the obstacle at a target time is predicted based on the kinematic information of the obstacle at the current time.

[0063] Here, the kinematic information of the obstacle at the current moment is obtained. For example, in a vehicle scenario, the kinematic information of the obstacle at the current moment can also be obtained through the vehicle's perception module. It is worth noting that kinematic information refers to physical quantities that describe the obstacle's motion state and motion characteristics, such as velocity and acceleration.

[0064] Since the kinematic information of the obstacle at the current moment is known, the predicted position of the obstacle at the target moment can be predicted by the kinematic model (for the sake of distinction, the predicted position obtained here is named the second predicted position).

[0065] In step 103, a weighted sum process is performed on the first predicted position and the second predicted position of the obstacle at the target time to obtain a third predicted position of the obstacle at the target time.

[0066] Here, a weighted summation is performed on the first and second predicted positions of the obstacle at the target time to obtain the third predicted position of the obstacle at the target time. It is worth noting that the sum of the weights corresponding to the first and second predicted positions is 1. There is no specific limitation on the weights corresponding to the first and second predicted positions and they can be pre-set, for example, to 0.5, or calculated based on historical data.

[0067] It is worth noting that the embodiments of the present application can be applied to any subject that needs to predict the position of obstacles, such as vehicles, robots, drones, etc.; the obstacles involved in the embodiments of the present application generally refer to any objects that may hinder the movement of the above-mentioned subjects.

[0068] In some embodiments, an obstacle position prediction method is applied to a vehicle; after performing weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target time to obtain the third predicted position of the obstacle at the target time, the obstacle position prediction method further includes: controlling the vehicle to avoid the obstacle based on the third predicted position of the obstacle at the target time.

[0069] Taking a vehicle scenario as an example, after obtaining the third predicted position of an obstacle at the target time, the vehicle can be controlled to avoid the obstacle. For example, a control strategy is planned based on the vehicle's current actual position and the third predicted position of the obstacle at the target time. The vehicle is then controlled to avoid the obstacle according to the control strategy. The planning process requires reference to the obstacle avoidance strategy, which can be pre-set. This approach enables accurate obstacle avoidance and improves the safety of autonomous driving.

[0070] like Figure 3A As shown, the embodiment of the present application fits a trajectory curve based on the actual positions of the obstacle at multiple historical moments, and predicts the first predicted position of the obstacle at a future target moment based on the trajectory curve; predicts the second predicted position of the obstacle at the target moment based on the kinematic information of the obstacle at the current moment; and performs a weighted summation of the first and second predicted positions of the obstacle at the target moment to obtain a third predicted position of the obstacle at the target moment. By comprehensively considering the trajectory curve and kinematic information, the embodiment of the present application can make the final predicted third position of the obstacle at the target moment more accurate, helping to improve the obstacle avoidance effect during the autonomous driving process.

[0071] In some embodiments, see Figure 3B , Figure 3B This is a flow chart of the obstacle position prediction method provided by the embodiment of the present application, based on Figure 3A , after step 102, step 201 may also be performed.

[0072] In step 201, weight correction processing is performed based on the actual position of the obstacle at the historical moment, the first predicted position and the second predicted position to obtain the historical weight corresponding to the historical moment; wherein the historical weight corresponding to the historical moment is used to perform weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target moment.

[0073] For example, for any one of the multiple historical moments that appear in step 101 (referred to as historical moment T0 for ease of distinction), a weight correction process is performed based on the actual position, first predicted position, and second predicted position of the obstacle at historical moment T0 to obtain the historical weight corresponding to historical moment T0, so that after performing a weighted sum process on the first predicted position and the second predicted position of the obstacle at historical moment T0 based on the historical weight corresponding to historical moment T0, the actual position of the obstacle at historical moment T0 can be obtained.

[0074] The historical weights corresponding to the historical moment T0 include the weight corresponding to the first predicted position and the weight corresponding to the second predicted position. Based on this, the first predicted position and the second predicted position of the obstacle at the target moment are weighted and summed to obtain the third predicted position of the obstacle at the target moment.

[0075] In some embodiments, the obstacle position prediction method is applied to a vehicle; the obstacle position prediction method also includes: determining the activity level of the obstacle based on the historical weight corresponding to the historical moment; determining an obstacle avoidance strategy for the obstacle based on the activity level of the obstacle; and controlling the vehicle to avoid the obstacle based on the obstacle avoidance strategy for the obstacle and the third predicted position of the obstacle at the target moment.

[0076] Here, the activity level of the obstacle is determined based on the historical weight corresponding to the historical moment, wherein the activity level of the obstacle is positively correlated with the weight corresponding to the second predicted position in the historical weight corresponding to the historical moment, that is, the larger the weight corresponding to the second predicted position, the greater the impact of the obstacle's movement on the actual position of the obstacle, and therefore the higher the activity level of the obstacle (the more active it is).

[0077] Then, an obstacle avoidance strategy is determined based on the obstacle's activity level. For each activity level, a corresponding obstacle avoidance strategy can be pre-set. For example, if the obstacle avoidance strategy includes pre-braking distance, the pre-braking distance is positively correlated with the obstacle's activity level; if the obstacle avoidance strategy includes obstacle avoidance distance, the obstacle avoidance distance is positively correlated with the obstacle's activity level.

[0078] After determining the obstacle avoidance strategy for the obstacle, a control strategy is planned based on the obstacle avoidance strategy for the obstacle and the third predicted position of the obstacle at the target time, and the vehicle is controlled according to the control strategy to achieve obstacle avoidance processing.

[0079] In the above manner, an appropriate obstacle avoidance strategy is determined according to the activity level of the obstacle to thereby implement obstacle avoidance processing, thereby avoiding resource waste caused by excessive obstacle avoidance and safety issues caused by insufficient obstacle avoidance.

[0080] In some embodiments, the obstacle avoidance strategy for obstacles includes an advance braking distance; the above-mentioned obstacle avoidance strategy for obstacles and the third predicted position of the obstacle at the target time can be achieved in the following way: when the distance between the actual position of the vehicle at the current moment and the third predicted position of the obstacle at the target moment is less than the advance braking distance, the vehicle is braked.

[0081] Here, the planned control strategy might be to brake the vehicle when the distance between the vehicle's current actual position and the obstacle's third predicted position at the target time is less than the advance braking distance (included in the determined obstacle avoidance strategy). This prevents collisions with obstacles through timely braking, improving the safety of autonomous driving.

[0082] like Figure 3BAs shown, the embodiment of the present application corrects the weights with reference to the actual position of the obstacle at the historical moment, the first predicted position, and the second predicted position, so that the weights used in the weighted summation of the first predicted position and the second predicted position of the obstacle at the target moment are more reasonable, thereby making the final third predicted position of the obstacle at the target moment more accurate.

[0083] In some embodiments, see Figure 3C , Figure 3C This is a flow chart of the obstacle position prediction method provided by the embodiment of the present application, based on Figure 3B After step 201, step 301 may also be performed.

[0084] In step 301, a weighted moving average process is performed on the historical weight sequence to obtain an average historical weight; wherein the historical weight sequence includes historical weights corresponding to multiple historical moments; wherein the average historical weight is used to perform a weighted summation process on the first predicted position and the second predicted position of the obstacle at the target moment.

[0085] Here, a historical weight sequence can be constructed based on the historical weights corresponding to multiple historical moments, and a weighted moving average process can be performed on the historical weight sequence to obtain the average historical weight. The principle of weighted moving average processing is that more recent data is assigned a higher weight, thereby better reflecting recent changes in the data. In other words, the closer a historical moment is to the current moment, the greater the weight assigned to the historical weight corresponding to that historical moment in the weighted moving average process.

[0086] The average historical weight includes the weight corresponding to the first predicted position and the weight corresponding to the second predicted position. Based on this, the first predicted position and the second predicted position of the obstacle at the target time are weighted and summed to obtain the third predicted position of the obstacle at the target time.

[0087] In some embodiments, the length of the historical weight sequence is fixed. For example, as time passes, historical weights corresponding to new historical moments are continuously added to the historical weight sequence, while historical weights corresponding to the oldest historical moments are continuously deleted to ensure that the length of the historical weight sequence is fixed. In this way, the historical weight sequence is updated in real time, and the least relevant historical weights are promptly deleted, which can improve the overall reference value of the historical weight sequence.

[0088] In some embodiments, the obstacle position prediction method is applied to a vehicle; the obstacle position prediction method also includes: determining the activity level of the obstacle based on at least one of the historical weight sequence and the average historical weight; determining an obstacle avoidance strategy for the obstacle based on the activity level of the obstacle; and controlling the vehicle to avoid the obstacle based on the obstacle avoidance strategy for the obstacle and the third predicted position of the obstacle at the target time.

[0089] Here, an obstacle activity level is determined based on at least one of a historical weight sequence and an average historical weight. The obstacle activity level is positively correlated with the weight corresponding to the second predicted position in the historical weight sequence, and the obstacle activity level is positively correlated with the weight corresponding to the second predicted position in the average historical weight. An obstacle avoidance strategy is then determined based on the obstacle activity level. Based on the obstacle avoidance strategy and the third predicted position of the obstacle at the target time, the vehicle is controlled to avoid the obstacle.

[0090] In the above method, the historical weight sequence includes historical weights corresponding to multiple historical moments, and the average historical weight is obtained by performing weighted moving average processing on the historical weight sequence. Therefore, the information referenced when determining the activity level of the obstacle is more comprehensive, which can improve the accuracy of the determined activity level of the obstacle; determining the appropriate obstacle avoidance strategy based on the activity level of the obstacle and then implementing obstacle avoidance processing can avoid resource waste caused by excessive obstacle avoidance and safety issues caused by insufficient obstacle avoidance.

[0091] In some embodiments, the obstacle avoidance strategy for obstacles includes an advance braking distance; the above-mentioned obstacle avoidance strategy for obstacles and the third predicted position of the obstacle at the target time can be achieved in the following way: when the distance between the actual position of the vehicle at the current moment and the third predicted position of the obstacle at the target moment is less than the advance braking distance, the vehicle is braked.

[0092] Here, the planned control strategy might be to brake the vehicle when the distance between the vehicle's current actual position and the obstacle's third predicted position at the target time is less than the advance braking distance (included in the determined obstacle avoidance strategy). This prevents collisions with obstacles through timely braking, improving the safety of autonomous driving.

[0093] like Figure 3CAs shown, the embodiment of the present application performs weighted moving average processing on the historical weight sequence to obtain an average historical weight, and uses the average historical weight for weighted summation processing on the first predicted position and the second predicted position of the obstacle at the target time. In this way, more information can be referred to to determine the weight used in the weighted summation processing, and the accuracy of the third predicted position of the obstacle at the target time obtained finally can be improved.

[0094] In the following, an exemplary application of the embodiment of the present application in an actual application scenario will be described. As an example, the embodiment of the present application provides a flowchart of an obstacle position prediction method as shown in Figure 4 The embodiment of the present application will be described by means of steps in combination with Figure 4 .

[0095] Step 1) After starting the obstacle position prediction function, a geodetic coordinate system is established with the geometric center of the vehicle (i.e. ego vehicle) at the starting time T0 as the origin, as shown in Figure 5 .

[0096] Step 2) During the process of driving of the vehicle from the time T0 to the time T1, the upstream perception module of the vehicle records the actual position and kinematic information of the obstacle according to the operation period, Figure 4 the actual position and kinematic information of the obstacle are collectively referred to as obstacle information in Figure 6 , the actual positions of the obstacle at different times, i.e. O1, O2, …, are shown. The time length from the time T0 to the time T1 is regarded as a prediction period, which can be set to 50ms-100ms, for example. The number n of obstacle points collected from the time T0 to the time T1 can be 3-5, of course, which does not constitute a limitation on the embodiment of the present application.

[0097] Step 3) The actual position of the vehicle, the kinematic information of the vehicle, the actual position of the obstacle, and the kinematic information of the obstacle are all converted into the geodetic coordinate system established in step 1).

[0098] Step 4) At the time T1, a trajectory curve is fitted in a polynomial interpolation manner, and the position On+1 of the obstacle at the future time TΔ is predicted according to the trajectory curve, wherein the time difference between the time TΔ and the time T1 is Δt, as shown in Figure 7 . Taking n=3 as an example, the following polynomial formula is shown:

[0099] x(t0) = a0 + t0a1 + t0 2 a2 + t0 3 a3

[0100] y(t0) = b0 + t0b1 + t0 2 b2 + t0 3 b3

[0101] x(t1)=a0+t1a1+t1 2 a2+t1 3 a3

[0102] y(t1)=b0+t1b1+t1 2 b2+t1 3 b3

[0103] x(t2)=a0+t2a1+t2 2 a2+t2 3 a3

[0104] y(t2)=b0+t2b1+t2 2 b2+t2 3 b3

[0105] The aforementioned time t0 refers to the time corresponding to when the obstacle is at position O1, x(t0) represents the horizontal coordinate at position O1, y(t0) represents the vertical coordinate at position O1, and so on. Since t0, x(t0), y(t0), t1, x(t1), y(t1), t2, x(t2), and y(t2) are all known quantities, the coefficients a0, a1, a2, a3, b0, b1, b2, and b3 in the polynomial formula can be solved. Of course, the above polynomial formula is only an example and does not constitute a limitation of the embodiments of the present application.

[0106] Then, substitute the time TΔ into the above polynomial formula to obtain the first predicted position of the obstacle at the time TΔ (x a ,y a ).

[0107] Step 5) Based on the kinematic information of the obstacle at the actual position On, a kinematic model is used to predict the second predicted position (x b ,y b ).like Figure 8 As shown, the kinematic information of the obstacle at the actual position On includes the vehicle speed v and the acceleration a. The vehicle speed v and the acceleration a are projected to the x and y directions respectively, and the second predicted position (x b ,y b ), the formula is as follows:

[0108]

[0109] Wherein, x0 represents the abscissa of the actual position of the obstacle at time T1, and y0 represents the ordinate of the actual position of the obstacle at time T1.

[0110] Step 6) The first predicted position (x a,y a ) and the second predicted position (x b ,y b ) is weighted summed to obtain the third predicted position (x c ,y c ),like Figure 9 The weighted summation process is as follows:

[0111] x c =x a (1-w)+x b w

[0112] y c =y a (1-w)+y b w

[0113] Among them, (1-w) represents the weight corresponding to the first predicted position, and w represents the weight corresponding to the second predicted position.

[0114] Step 7) When the time TΔ is reached, the weight w is modified. Specifically, the actual position of the obstacle at the time TΔ is collected. And the weight w (corresponding to the historical weight above) is corrected by calculating the Euclidean distance. The formula is as follows:

[0115]

[0116] Step 8) In the process of repeatedly executing steps 4) to 7), the historical weight sequence w0,…w can be obtained after multiple prediction cycles. n , where 0 to n represent the time sequence, w0 represents the oldest historical weight, and w n Represents the latest historical weights, w0,…w n The number of is consistent with the number of obstacle points n, and w0,…w n The initial value of is set to 0.5. In addition, each time a historical weight w is obtained through step 7), it is added to the historical weight sequence as the latest historical weight, and the oldest historical weight in the historical weight sequence is deleted to ensure that the length of the historical weight sequence is fixed.

[0117] Perform weighted moving average processing on the obtained historical weight sequence to obtain the average historical weight. The formula is as follows:

[0118] w t =a n w n +a n-1 w n-1 +…+a0w0

[0119] Among them, a n +an-1 +…+a0=1, and arranged in descending order.

[0120] The obtained average historical weight is used to implement weighted summation processing in the above step 6).

[0121] Step 9) Determine the obstacle's activity level. The larger the weight corresponding to the second predicted position, the greater the weight of the kinematic model's prediction, and the higher the obstacle's activity level. Therefore, the number N of historical weights greater than 0.5 in the historical weight sequence and the average historical weight can be provided to the downstream obstacle avoidance decision module, allowing it to determine the obstacle's activity level and, based on the obstacle's activity level, determine the obstacle avoidance strategy.

[0122] The decision-making method of the obstacle avoidance strategy is shown in Table 1.

[0123] Table 1 Obstacle avoidance strategy decision table

[0124]

[0125]

[0126] It is worth noting that the embodiments of the present application do not limit the specific form of the obstacle avoidance strategy, including but not limited to braking, detour, parking, etc. Taking the obstacle avoidance strategy as an example, in the obstacle avoidance decision process, for the same third predicted position, different advance braking distances can be adopted according to different activity levels. The higher the activity level, the greater the advance braking distance. For example, under the premise that the third predicted position is the same, if the obstacle is a basketball (such as Figure 10 As shown in the figure, since the basketball does not have the ability to move independently, its activity level is usually low, so the determined early braking distance is small, that is, the vehicle will brake when it is close to the basketball, which can ensure safety and avoid hindering the normal driving of the vehicle as much as possible; if the obstacle is a puppy (such as Figure 11 As shown in the figure, since puppies have the ability to move independently and their activity level is usually high, the determined advance braking distance is large, that is, the vehicle brakes when it is far away from the puppy. This can maximize safety and avoid accidents.

[0127] The embodiments of the present application have at least the following technical effects:

[0128] 1) Comprehensively considering the trajectory curve and kinematic information to predict the obstacle position can improve the position prediction accuracy while reducing the implementation cost.

[0129] 2) The obstacle position prediction accuracy is further improved by continuously iterating the historical weight sequence online.

[0130] 3) The activity level of obstacles is determined based on the historical weight sequence and the average historical weight, which facilitates the determination of appropriate obstacle avoidance strategies based on the activity level of obstacles during autonomous driving, taking into account both driving efficiency and safety.

[0131] The following continues to describe the exemplary structure of the obstacle position prediction device 455 provided in the embodiment of the present application implemented as a software module. In some embodiments, such as Figure 2 As shown, the software modules stored in the obstacle position prediction device 455 of the memory 450 may include: a first prediction module 4551, which is used to fit a trajectory curve according to the actual positions of the obstacle corresponding to multiple historical moments, and predict the first predicted position of the obstacle at a future target moment based on the trajectory curve; a second prediction module 4552, which is used to predict the second predicted position of the obstacle at the target moment based on the kinematic information of the obstacle at the current moment; and a weighted summation module 4553, which is used to perform weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target moment to obtain a third predicted position of the obstacle at the target moment.

[0132] In some embodiments, the obstacle position prediction device 455 also includes a weight correction module, which is used to: perform weight correction processing based on the actual position of the obstacle at the historical moment, the first predicted position and the second predicted position to obtain the historical weight corresponding to the historical moment; wherein the historical weight corresponding to the historical moment is used to perform weighted summation processing on the first predicted position and the second predicted position of the obstacle at the target moment.

[0133] In some embodiments, the weight correction module is also used to: perform weighted moving average processing on the historical weight sequence to obtain an average historical weight; wherein the historical weight sequence includes historical weights corresponding to multiple historical moments; wherein the average historical weight is used to perform weighted summation processing on the first predicted position and the second predicted position of the obstacle at the target moment.

[0134] In some embodiments, the obstacle position prediction device 455 is applied to a vehicle; the obstacle position prediction device 455 also includes an activity level determination module, which is used to: determine the activity level of the obstacle based on at least one of the historical weight sequence and the average historical weight; the obstacle position prediction device 455 also includes an obstacle avoidance strategy determination module, which is used to: determine the obstacle avoidance strategy for the obstacle based on the activity level of the obstacle; the obstacle position prediction device 455 also includes an obstacle avoidance module, which is used to: control the vehicle to avoid the obstacle based on the obstacle avoidance strategy for the obstacle and the third predicted position of the obstacle at the target time.

[0135] In some embodiments, the obstacle avoidance strategy for obstacles includes an advance braking distance; the obstacle avoidance module is also used to: brake the vehicle when the distance between the actual position of the vehicle at the current moment and the third predicted position of the obstacle at the target moment is less than the advance braking distance.

[0136] In some embodiments, the obstacle position prediction device 455 is applied to a vehicle; the obstacle position prediction device 455 also includes an obstacle avoidance module for controlling the vehicle to avoid the obstacle based on the third predicted position of the obstacle at the target time.

[0137] The present invention provides a computer program product or computer program, which includes executable instructions stored in a computer-readable storage medium. A processor of an electronic device reads the executable instructions from the computer-readable storage medium and executes the executable instructions, causing the electronic device to implement the obstacle location prediction method described in the present invention.

[0138] An embodiment of the present application provides a computer-readable storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will implement the obstacle position prediction method provided by the embodiment of the present application.

[0139] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface storage, optical disk, or CD-ROM; or various devices including one or any combination of the above memories.

[0140] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0141] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions).

[0142] As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0143] The above are merely examples of the present application and are not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, and improvements made within the spirit and scope of the present application are included in the scope of protection of the present application.

Claims

1. A method for predicting obstacle positions, characterized in that: include: Fitting a trajectory curve according to the actual positions of the obstacle at multiple historical moments, and predicting a first predicted position of the obstacle at a future target moment based on the trajectory curve; Predicting a second predicted position of the obstacle at the target time based on the kinematic information of the obstacle at the current time; performing weight correction processing based on the actual position, the first predicted position, and the second predicted position of the obstacle at the historical moment to obtain a historical weight corresponding to the historical moment; wherein the historical weight corresponding to the historical moment is used to perform weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target moment; A weighted summation process is performed on the first predicted position and the second predicted position of the obstacle at the target time to obtain a third predicted position of the obstacle at the target time.

2. The method according to claim 1, characterized in that After performing weight correction processing based on the actual position of the obstacle at the historical moment, the first predicted position, and the second predicted position to obtain the historical weight corresponding to the historical moment, the method further includes: Performing weighted moving average processing on the historical weight sequence to obtain an average historical weight; wherein the historical weight sequence includes historical weights corresponding to multiple historical moments; The average historical weight is used to perform weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target moment.

3. The method according to claim 2, characterized in that The method is applied to a vehicle; the method further comprises: determining an activity level of the obstacle based on at least one of the historical weight sequence and the average historical weight; determining an obstacle avoidance strategy for the obstacle according to the activity level of the obstacle; The vehicle is controlled to perform obstacle avoidance processing on the obstacle according to the obstacle avoidance strategy for the obstacle and the third predicted position of the obstacle at the target time.

4. The method according to claim 3, characterized in that The obstacle avoidance strategy for the obstacle includes an advance braking distance; and controlling the vehicle to avoid the obstacle based on the obstacle avoidance strategy for the obstacle and the third predicted position of the obstacle at the target time includes: When the distance between the actual position of the vehicle at the current moment and the third predicted position of the obstacle at the target moment is less than the advance braking distance, braking processing is performed on the vehicle.

5. The method according to claim 1, wherein The method is applied to a vehicle; after performing weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target time to obtain a third predicted position of the obstacle at the target time, the method further includes: The vehicle is controlled to perform obstacle avoidance processing on the obstacle according to the third predicted position of the obstacle at the target time.

6. An obstacle position prediction device, characterized in that: include: A first prediction module is configured to fit a trajectory curve according to the actual positions of the obstacle at multiple historical moments, and predict a first predicted position of the obstacle at a future target moment based on the trajectory curve; a second prediction module, configured to predict a second predicted position of the obstacle at the target moment based on the kinematic information of the obstacle at the current moment; a weighted summation module, configured to perform weight correction processing based on the actual position of the obstacle at the historical moment, the first predicted position, and the second predicted position to obtain a historical weight corresponding to the historical moment; wherein the historical weight corresponding to the historical moment is used to perform weighted sum processing on the first predicted position and the second predicted position of the obstacle at the target moment; A weighted summation process is performed on the first predicted position and the second predicted position of the obstacle at the target time to obtain a third predicted position of the obstacle at the target time.

7. An electronic device, characterized in that: include: a memory for storing executable instructions; A processor, configured to implement the method according to any one of claims 1 to 5 when executing the executable instructions stored in the memory.

8. A computer-readable storage medium, characterized in that Executable instructions are stored, and when executed by a processor, they are used to implement the method described in any one of claims 1 to 5.

9. A computer program product, characterized in that The method comprises executable instructions for implementing the method according to any one of claims 1 to 5 when executed by a processor.

Images