Trajectory prediction method, device, vehicle, electronic device and storage medium

Abstract

The present application provides a trajectory prediction method, device, vehicle, electronic device and storage medium, the method comprising: being able to obtain first driving data of a second vehicle, the second vehicle being any vehicle traveling within a preset range of the first vehicle; predicting the driving trajectory of the second vehicle within a preset time period based on the first driving data, and obtaining a first driving trajectory; and controlling the driving state of the first vehicle according to the first driving trajectory. In this way, during the driving process of the first vehicle, the driving state of the first vehicle can be controlled by predicting the driving trajectory of the second vehicle within the preset range, thereby improving the driving safety of the first vehicle.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and in particular, relates to a trajectory prediction method, device, vehicle, electronic equipment and storage medium. Background Art

[0002] With the continuous development of vehicle intelligence, autonomous driving or unmanned driving is the future development trend of existing automotive technology and advanced driver assistance systems. At present, one of the major application scenarios of unmanned vehicles is mixed traffic flow roads, that is, roads where unmanned vehicles and human-driven vehicles coexist. In this scenario, the unmanned vehicle will be affected by the human-driven vehicle.

[0003] Therefore, in order to ensure user safety, driverless cars need to obtain the trajectory of surrounding vehicles and control the driverless car based on this trajectory, so as to ensure that the driverless car can reach the destination safely and efficiently. Summary of the invention

[0004] Embodiments of the present application provide a trajectory prediction method, device, vehicle, electronic device, and storage medium to improve vehicle driving safety.

[0005] In a first aspect, an embodiment of the present application provides a trajectory prediction method, which is applied to a first vehicle, and the method includes:

[0006] Acquire first driving data of a second vehicle, where the second vehicle is any vehicle traveling within a preset range of the first vehicle, and the first driving data is historical driving data of the second vehicle;

[0007] Predicting a driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain a first driving trajectory;

[0008] The first vehicle driving state is controlled according to the first driving trajectory.

[0009] In some embodiments, predicting the driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain a first driving trajectory includes:

[0010] Based on the first driving data, predicting the driving trajectory of the second vehicle within a preset time period to obtain N second driving trajectories;

[0011] Determine the cost value of each of the second driving trajectories according to the preset constraint conditions to obtain N cost values;

[0012] A second driving trajectory corresponding to the smallest cost value among the N cost values ​​is determined as the first driving trajectory.

[0013] In some embodiments, the preset constraints include a jerk constraint, a time constraint for completing the cut-in trajectory, and a terminal lateral distance deviation constraint. The cost value of each second driving trajectory is determined according to the preset constraints, including:

[0014] A cost function is obtained according to the jerk constraint, the cut-in trajectory completion time constraint, and the end point lateral distance deviation constraint;

[0015] According to the cost function, a cost value of each of the second driving trajectories is determined.

[0016] In some embodiments, predicting the driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain N second driving trajectories includes:

[0017] The first driving data is input into a pre-trained vehicle trajectory prediction model to obtain N second driving trajectories.

[0018] In some embodiments, before determining the second driving trajectory corresponding to the smallest cost value among the N cost values ​​as the first driving trajectory, the method further includes:

[0019] For the second driving trajectory corresponding to the minimum cost value, a maximum value check of the transverse and longitudinal acceleration, a maximum value check of the transverse and longitudinal speed, a maximum value check of the trajectory curvature, and a collision check are performed to obtain a check result;

[0020] The step of determining the second driving trajectory corresponding to the smallest cost value among the N cost values ​​as the first driving trajectory includes:

[0021] When the verification result indicates that the second driving trajectory corresponding to the minimum cost value satisfies a verification condition, the second driving trajectory corresponding to the minimum cost value is determined as the first driving trajectory.

[0022] In some embodiments, controlling the first vehicle driving state according to the first driving trajectory includes:

[0023] Predicting a third driving trajectory of the first vehicle within a preset time period based on the driving data of the first vehicle;

[0024] In the first driving trajectory and the third driving trajectory, determining whether there is a collision risk between the first vehicle and the second vehicle;

[0025] In a case where it is determined that there is a risk of collision between the first vehicle and the second vehicle, braking measures are taken on the first vehicle.

[0026] In a second aspect, an embodiment of the present application provides a trajectory prediction device, applied to a first vehicle, the device comprising:

[0027] an acquisition module, configured to acquire first driving data of a second vehicle, wherein the second vehicle is any vehicle traveling within a preset range of the first vehicle, and the first driving data is historical driving data of the second vehicle;

[0028] A prediction module, configured to predict a driving trajectory of the second vehicle within a preset time period based on the first driving data, to obtain a first driving trajectory;

[0029] A control module is used to control the driving state of the first vehicle according to the first driving trajectory.

[0030] In a third aspect, an embodiment of the present application provides a vehicle, the vehicle comprising a vehicle end, wherein the vehicle end is used to implement the method of the first aspect.

[0031] In a fourth aspect, an embodiment of the present application provides an electronic device, the device comprising:

[0032] A processor and a memory storing programs or instructions;

[0033] The above method is implemented when the processor executes the program or instruction.

[0034] In a fifth aspect, an embodiment of the present application provides a machine-readable storage medium, on which a program or instruction is stored, and the program or instruction implements the above method when executed by a processor.

[0035] In a sixth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the above method.

[0036] The trajectory prediction method, device, vehicle, electronic device and readable storage medium of the embodiment of the present application can obtain the first driving data of the second vehicle, where the second vehicle is any vehicle traveling within the preset range of the first vehicle. Based on the first driving data, the driving trajectory of the second vehicle within a preset time period is predicted to obtain the first driving trajectory; and the driving state of the first vehicle is controlled according to the first driving trajectory. In this way, during the driving process of the first vehicle, the driving state of the first vehicle can be controlled by predicting the driving trajectory of the second vehicle within the preset range, thereby improving the driving safety of the first vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 is a flow chart of a trajectory prediction method provided by an embodiment of the present application;

[0039] Figure 2 is a schematic diagram of the structure of a trajectory prediction device provided by another embodiment of the present application;

[0040] Figure 3 It is a structural schematic diagram of an electronic device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0041] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0042] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0043] Trajectory prediction of environmental target vehicles is an important technical component of autonomous vehicles. It is crucial for the planning module and is a direct factor affecting whether autonomous vehicles can plan safe trajectories. Due to the uncertainty of the behavior of environmental target vehicles, loose judgment conditions for whether vehicles in the side lanes cut into the trajectory of the vehicle can easily cause the vehicle to brake incorrectly, while too strict judgment conditions can easily cause rear-end collisions or sudden braking.

[0044] In order to solve the problems in the prior art, the embodiments of the present application provide a trajectory prediction method, a trajectory prediction device, a vehicle, an electronic device and a readable storage medium. The trajectory prediction method provided by the embodiments of the present application is first introduced below.

[0045] Figure 1 FIG. 1 is a flow chart of a trajectory prediction method provided by an embodiment of the present application. Figure 1 As shown, the trajectory prediction method, applied to the first vehicle, may include the following steps 101 to 103:

[0046] Step 101, obtaining first driving data of a second vehicle.

[0047] In step 101, the first vehicle may be any vehicle traveling, the second vehicle may be any vehicle traveling within a preset range of the first vehicle, and the first driving data may be historical driving data of the second vehicle. The first vehicle and the second vehicle may be driverless vehicles, or may be driver-driven vehicles, or may be new energy vehicles, or may be fuel vehicles. Among them, the new energy vehicle may be a pure electric vehicle or a hybrid vehicle. In the embodiment of the present invention, the vehicle is not specifically limited. The preset range of the first vehicle refers to an area composed of a preset radius with the vehicle as the center. The preset radius can be set and changed as needed. In the embodiment of the present disclosure, the preset radius is not specifically limited. In addition, the preset radius can be set based on different road attributes; for example, the preset radius corresponding to a highway may be 100 meters or 200 meters; the preset radius corresponding to an ordinary road may be 2 meters or 3 meters, etc.

[0048] Acquiring the first driving data of the first vehicle may be to obtain the driving data of the second vehicle through the sensing equipment on the first vehicle, for example, the first vehicle may detect the driving data of the second vehicle traveling within a preset range through a radar, or the driving data of the vehicle detected by the second vehicle may be obtained through the Internet of Vehicles, that is, the second vehicle sends the driving data of the vehicle to the Internet of Vehicles, and the first vehicle obtains the first driving data of the second vehicle from the Internet of Vehicles. Optionally, the driving data of the second vehicle may also be obtained through a roadside unit, where the roadside unit may be a road facility, including: a roadside camera, a radar, etc. For example, the radar may be a millimeter-wave radar, and the road facility is arranged on a road in a traffic network, or on the edge of a road. In the embodiment of the present application, the number and distribution density of the road facilities arranged on the road are not limited, and the road facility has a road monitoring function, for example, the road facility monitors the driving data of the second vehicle on the road.

[0049] Step 102: predicting a driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain a first driving trajectory.

[0050] In step 102, a first driving trajectory of the second vehicle within a preset time period may be predicted using first driving data, ie, driving data of the second vehicle.

[0051] Here, the first driving trajectory can be the driving trajectory of the second vehicle in the future preset time period, that is, a continuous set of trajectory points in the preset time period. For example: define the state of target i at time t as t1 to t n The driving trajectory is defined as At each time frame, a prediction is made on the future trajectory of the second vehicle based on its historical trajectory.

[0052] In some embodiments, the above step 102 may include the following steps:

[0053] Based on the first driving data, predicting a driving trajectory of the second vehicle within a preset time period, and obtaining N second driving trajectories, where N is an integer greater than 1;

[0054] Determine the cost value of each second driving trajectory according to the preset constraint condition to obtain N cost values;

[0055] The second driving trajectory corresponding to the smallest cost value among the N cost values ​​is determined as the first driving trajectory.

[0056] Specifically, the driving trajectory of the second vehicle is first predicted to obtain N first driving trajectories, and then the driving trajectory with the smallest cost value is determined from the N driving trajectories as the predicted trajectory of the second vehicle (i.e., the first driving trajectory). The cost value is the price that the second vehicle has to pay for selecting the driving trajectory. The probability of the obtained N driving trajectories can be determined by the cost value, thereby selecting the driving trajectory with the highest probability as the driving trajectory of the second vehicle.

[0057] Taking into account the driving habits of the driver of the second vehicle, the riding comfort of the driver and passengers, and the vehicle performance of the second vehicle, some extreme driving trajectories need to be excluded, so constraints need to be added when predicting the driving trajectory of the second vehicle. In some embodiments, the preset constraints include acceleration constraints, completion cut-in trajectory time constraints, and endpoint lateral distance deviation constraints. The cost value of each second driving trajectory is determined according to the preset constraints, including:

[0058] The cost function is obtained according to the jerk constraint, the time constraint for completing the cut-in trajectory, and the end point lateral distance deviation constraint;

[0059] According to the cost function, a cost value of each second driving trajectory is determined.

[0060] Among them, the jerk constraint condition, that is, in the vehicle's driving trajectory, there is a relatively small probability that the jerk will suddenly increase. Therefore, the jerk constraint condition can be determined according to the comfort threshold of the jerk, such as the jerk per unit time is less than a. The time constraint condition for completing the cut-in trajectory, if the driving trajectory of the second vehicle is to switch from the original lane to the lane where the first vehicle is located, then when switching lanes, the time to complete the switch must meet a certain threshold. If the time is less than the threshold, it does not conform to normal driving habits and has low comfort, so it needs to be excluded. The end point lateral distance deviation constraint condition can be determined based on the analysis of the vehicle's historical data to determine the lateral distance constraint condition between the starting point and the end point of the driving trajectory.

[0061] In some embodiments, the cost of the driving trajectory can be calculated by the following formula (1):

[0062] C=k j *J+k t *T+k d *D (1)

[0063] Among them, C is the cost value of the driving trajectory, J is the acceleration constraint, k j is the weight corresponding to J; T is the time constraint for completing the cut-in trajectory, k t is the weight corresponding to T; D is the end point lateral distance deviation constraint, k d is the weight corresponding to D.

[0064] In some embodiments, a predicted trajectory may be generated based on the historical driving trajectory of the vehicle using a deep learning method, so the above step 102 may include the following steps:

[0065] The first driving data is input into a pre-trained vehicle trajectory prediction model to obtain N second driving trajectories.

[0066] Here the vehicle trajectory prediction model can be trained by the following method:

[0067] The historical driving trajectory information of multiple vehicles is obtained. The driving trajectory information may include the driving speed, acceleration, driving direction and position information of the vehicles in each time frame.

[0068] The initial trajectory prediction model is trained according to the historical driving trajectory information of multiple vehicles to obtain a vehicle trajectory prediction model.

[0069] The initial training model may be any training model of a vehicle trajectory, such as a hidden Markov model.

[0070] In this embodiment, the driving trajectory of the second vehicle can be predicted by a deep learning method, making the prediction of the driving trajectory faster and more convenient.

[0071] In some embodiments, before determining the second driving trajectory corresponding to the smallest cost value among the N cost values ​​as the first driving trajectory, the following method may also be included:

[0072] For the second driving trajectory corresponding to the minimum cost value, a maximum value check of the transverse and longitudinal acceleration, a maximum value check of the transverse and longitudinal speed, a maximum value check of the trajectory curvature, and a collision check are performed to obtain a check result;

[0073] Then the above-mentioned determining the second driving trajectory corresponding to the minimum cost value among the N cost values ​​as the first driving trajectory may include:

[0074] When the verification result indicates that the second driving trajectory corresponding to the minimum cost value satisfies the verification condition, the second driving trajectory corresponding to the minimum cost value is determined as the first driving trajectory.

[0075] The maximum values ​​of lateral and longitudinal acceleration, speed, and track curvature are checked to exclude tracks that exceed vehicle performance. Collision check can be performed by determining whether the second driving track poses a risk of collision with other vehicles. If there are obstacles or other vehicles blocking the track, the track needs to be excluded.

[0076] Specifically, after obtaining the second driving trajectory with the minimum cost value, it is also necessary to verify the second driving trajectory to determine whether the driving trajectory conforms to the actual driving situation.

[0077] In some embodiments, if the second driving trajectory corresponding to the minimum cost value does not meet the verification condition, the driving trajectory is excluded and the second driving trajectory corresponding to the second minimum cost value is selected. If two or more consecutive driving trajectories do not meet the condition, a prompt message is displayed for error processing.

[0078] In step 103, the first vehicle driving state is controlled according to the first driving trajectory.

[0079] In some embodiments, the above step 103 may include the following steps:

[0080] Predicting a third driving trajectory of the first vehicle within a preset time period based on the driving data of the first vehicle;

[0081] In the first driving trajectory and the third driving trajectory, determining whether there is a collision risk between the first vehicle and the second vehicle;

[0082] When it is determined that there is a risk of collision between the first vehicle and the second vehicle, braking measures are taken on the first vehicle.

[0083] Specifically, by comparing the driving trajectory of the first vehicle with the driving trajectory of the second vehicle, it is possible to determine whether there is a collision risk between the first vehicle and the second vehicle. If there is a collision risk, braking measures can be taken on the first vehicle in time to ensure driving safety.

[0084] The trajectory prediction method of the embodiment of the present application can obtain the first driving data of the second vehicle, where the second vehicle is any vehicle traveling within the preset range of the first vehicle. Based on the first driving data, the driving trajectory of the second vehicle within a preset time period is predicted to obtain a first driving trajectory; and the driving state of the first vehicle is controlled according to the first driving trajectory. In this way, during the driving process of the first vehicle, the driving state of the first vehicle can be controlled by predicting the driving trajectory of the second vehicle within the preset range, thereby improving the driving safety of the first vehicle.

[0085] Based on the trajectory prediction method provided in the above embodiments, the present application also provides a vehicle-side embodiment.

[0086] Figure 2 A schematic structural diagram of a trajectory prediction device provided in another embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0087] Reference Figure 2 , the trajectory prediction device 200 comprises:

[0088] An acquisition module 201 is used to acquire first driving data of a second vehicle, where the second vehicle is any vehicle traveling within a preset range of the first vehicle, and the first driving data is historical driving data of the second vehicle;

[0089] A prediction module 202, configured to predict a driving trajectory of the second vehicle within a preset time period based on the first driving data, to obtain a first driving trajectory;

[0090] The control module 203 is used to control the driving state of the first vehicle according to the first driving trajectory.

[0091] In some embodiments, the prediction module 202 may also be used to:

[0092] Performing a hash operation on the second key and the first current moment information of the vehicle end to generate a third key;

[0093] synthesizing a verification key based on the third key and the first key;

[0094] A hash operation is performed on the verification key and the first current moment information to generate a real-time key.

[0095] In some embodiments, the prediction module 202 may also be used to:

[0096] Based on the first driving data, predicting a driving trajectory of the second vehicle within a preset time period to obtain N second driving trajectories;

[0097] Determine the cost value of each second driving trajectory according to the preset constraint condition to obtain N cost values;

[0098] The second driving trajectory corresponding to the smallest cost value among the N cost values ​​is determined as the first driving trajectory.

[0099] In some embodiments, the preset constraints include a jerk constraint, a time constraint for completing the cut-in trajectory, and a lateral distance deviation constraint for the end point. The prediction module 202 may also be used to: obtain a cost function according to the jerk constraint, the time constraint for completing the cut-in trajectory, and the lateral distance deviation constraint for the end point;

[0100] According to the cost function, a cost value of each second driving trajectory is determined.

[0101] In some embodiments, the prediction module 202 may also be used to:

[0102] The first driving data is input into a pre-trained vehicle trajectory prediction model to obtain N second driving trajectories.

[0103] In some embodiments, the prediction module 202 may also be used to:

[0104] For the second driving trajectory corresponding to the minimum cost value, a maximum value check of the transverse and longitudinal acceleration, a maximum value check of the transverse and longitudinal speed, a maximum value check of the trajectory curvature, and a collision check are performed to obtain a check result;

[0105] When the verification result indicates that the second driving trajectory corresponding to the minimum cost value satisfies the verification condition, the second driving trajectory corresponding to the minimum cost value is determined as the first driving trajectory.

[0106] In some embodiments, the generation module 302 may also be used to:

[0107] A real-time key is generated according to the first key, the second key, the first current time information of the vehicle end and the error time information.

[0108] In some embodiments, the control module 203 may also be used to: predict a third driving trajectory of the first vehicle within a preset time period based on the driving data of the first vehicle;

[0109] In the first driving trajectory and the third driving trajectory, determining whether there is a collision risk between the first vehicle and the second vehicle;

[0110] When it is determined that there is a risk of collision between the first vehicle and the second vehicle, braking measures are taken on the first vehicle.

[0111] The trajectory prediction device of the embodiment of the present application can obtain the first driving data of the second vehicle, where the second vehicle is any vehicle traveling within the preset range of the first vehicle. Based on the first driving data, the driving trajectory of the second vehicle within a preset time period is predicted to obtain the first driving trajectory; and the driving state of the first vehicle is controlled according to the first driving trajectory. In this way, during the driving process of the first vehicle, the driving state of the first vehicle can be controlled by predicting the driving trajectory of the second vehicle within the preset range, thereby improving the driving safety of the first vehicle.

[0112] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application, and are devices corresponding to the above-mentioned trajectory prediction method. All implementation methods in the above-mentioned method embodiment are applicable to the embodiments of the device. Its specific functions and technical effects can be found in the method embodiment part, which will not be repeated here.

[0113] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0114] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of the present application is shown.

[0115] The device may include a processor 301 and a memory 302 storing programs or instructions.

[0116] When the processor 301 executes the program, the steps in any of the above method embodiments are implemented.

[0117] Exemplarily, the program may be divided into one or more modules / units, one or more modules / units are stored in the memory 302, and executed by the processor 301 to complete the present application. One or more modules / units may be a series of program instruction segments capable of completing a specific function, and the instruction segments are used to describe the execution process of the program in the device.

[0118] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0119] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0120] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) machine-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0121] The processor 301 implements any one of the methods in the above embodiments by reading and executing the program or instruction stored in the memory 302 .

[0122] In one example, the electronic device may further include a communication interface 303 and a bus 304. The processor 301, the memory 302, and the communication interface 303 are connected via the bus 304 and communicate with each other.

[0123] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0124] Bus 304 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 304 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0125] In addition, in combination with the method in the above embodiment, the embodiment of the present application may provide a machine-readable storage medium for implementation. The machine-readable storage medium stores a program or instruction; when the program or instruction is executed by a processor, any one of the methods in the above embodiment is implemented. The machine-readable storage medium can be read by a machine such as a computer.

[0126] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0127] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0128] An embodiment of the present application provides a computer program product, which is stored in a machine-readable storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0129] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0130] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. Code segments can be downloaded via computer grids such as the Internet, intranets, etc.

[0131] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0132] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by a computer program or instruction. These programs or instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0133] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A trajectory prediction method, characterized in that: Applied to a first vehicle, the method comprises: Acquire first driving data of a second vehicle, where the second vehicle is any vehicle traveling within a preset range of the first vehicle, and the first driving data is historical driving data of the second vehicle; Predicting a driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain a first driving trajectory; controlling the driving state of the first vehicle according to the first driving trajectory; The step of predicting the driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain a first driving trajectory includes: Based on the first driving data, predicting the driving trajectory of the second vehicle within a preset time period to obtain N second driving trajectories, where N is an integer greater than 1; Determine the cost value of each of the second driving trajectories according to the preset constraint conditions to obtain N cost values; Determining a second driving trajectory corresponding to a minimum cost value among the N cost values ​​as the first driving trajectory; The preset constraints include a jerk constraint, a time constraint for completing the cut-in trajectory, and a lateral distance deviation constraint for the end point. The cost value of each second driving trajectory is determined according to the preset constraints, including: A cost function is obtained according to the jerk constraint, the cut-in trajectory completion time constraint, and the endpoint lateral distance deviation constraint; Determining a cost value of each of the second driving trajectories according to the cost function; The step of predicting the driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain N second driving trajectories includes: Inputting the first driving data into a pre-trained vehicle trajectory prediction model to obtain N second driving trajectories; Before determining the second driving trajectory corresponding to the smallest cost value among the N cost values ​​as the first driving trajectory, the method further includes: For the second driving trajectory corresponding to the minimum cost value, a maximum value check of the transverse and longitudinal acceleration, a maximum value check of the transverse and longitudinal speed, a maximum value check of the trajectory curvature, and a collision check are performed to obtain a check result; The step of determining the second driving trajectory corresponding to the smallest cost value among the N cost values ​​as the first driving trajectory includes: When the verification result indicates that the second driving trajectory corresponding to the minimum cost value satisfies a verification condition, the second driving trajectory corresponding to the minimum cost value is determined as the first driving trajectory.

2. The method according to claim 1, characterized in that Controlling the first vehicle driving state according to the first driving trajectory includes: Predicting a third driving trajectory of the first vehicle within a preset time period based on the driving data of the first vehicle; In the first driving trajectory and the third driving trajectory, determining whether there is a collision risk between the first vehicle and the second vehicle; In a case where it is determined that there is a risk of collision between the first vehicle and the second vehicle, braking measures are taken on the first vehicle.

3. A trajectory prediction device, characterized in that: Applied to a first vehicle, the device comprises: an acquisition module, configured to acquire first driving data of a second vehicle, wherein the second vehicle is any vehicle traveling within a preset range of the first vehicle, and the first driving data is historical driving data of the second vehicle; A prediction module, configured to predict a driving trajectory of the second vehicle within a preset time period based on the first driving data, to obtain a first driving trajectory; a control module, configured to control a driving state of the first vehicle according to the first driving trajectory; Wherein, the prediction module is specifically used for: Based on the first driving data, predicting the driving trajectory of the second vehicle within a preset time period to obtain N second driving trajectories, where N is an integer greater than 1; Determine the cost value of each of the second driving trajectories according to the preset constraint conditions to obtain N cost values; Determining a second driving trajectory corresponding to a minimum cost value among the N cost values ​​as the first driving trajectory; The preset constraints include a jerk constraint, a time constraint for completing the cut-in trajectory, and a lateral distance deviation constraint for the end point. The prediction module determines the cost value of each second driving trajectory according to the preset constraints, including: A cost function is obtained according to the jerk constraint, the cut-in trajectory completion time constraint, and the endpoint lateral distance deviation constraint; Determining a cost value of each of the second driving trajectories according to the cost function; The prediction module predicts the driving trajectory of the second vehicle within a preset time period based on the first driving data to obtain N second driving trajectories, including: Inputting the first driving data into a pre-trained vehicle trajectory prediction model to obtain N second driving trajectories; The prediction module is further configured to, before determining the second driving trajectory corresponding to the smallest cost value among the N cost values ​​as the first driving trajectory, perform a maximum transverse and longitudinal acceleration check, a maximum transverse and longitudinal speed check, a maximum trajectory curvature check, and a collision check on the second driving trajectory corresponding to the smallest cost value to obtain a check result; The prediction module determines the second driving trajectory corresponding to the minimum cost value among the N cost values ​​as the first driving trajectory, including: When the verification result indicates that the second driving trajectory corresponding to the minimum cost value satisfies a verification condition, the second driving trajectory corresponding to the minimum cost value is determined as the first driving trajectory.

4. A vehicle, characterized in that: The vehicle comprises: a trajectory prediction device, and the trajectory prediction device is used to implement the method according to any one of claims 1-2.

5. An electronic device, characterized in that: The device comprises: a processor and a memory storing programs or instructions; When the processor executes the program or instruction, the method according to any one of claims 1 to 2 is implemented.

6. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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