Method, device, equipment, storage medium and program product for trajectory prediction

Abstract

According to embodiments of this disclosure, a method, apparatus, device, storage medium, and program product for trajectory prediction are provided. The method includes: identifying a trailer object and a associated trailer object in a traffic scene based on perception information of an autonomous vehicle; determining first trajectory prediction information of the trailer object within a target time period; determining the towing point of the trailer object and the trailer object; and generating second trajectory prediction information of the trailer object based on the first trajectory prediction information and the towing point. Therefore, embodiments of this disclosure can improve the accuracy of predicting the trajectories of trailer objects and trailer objects. Furthermore, based on such first and second trajectory prediction information, embodiments of this disclosure can subsequently reduce the probability of collisions between the autonomous vehicle and the trailer object / trailer object, thereby improving the safety and reliability of the autonomous vehicle.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of computers, and particularly to methods, apparatus, devices, computer-readable storage media, and computer program products for trajectory prediction. Background Technology

[0002] Autonomous driving is a technology that uses computers to replace or assist human drivers in perceiving the vehicle's surroundings, planning its trajectory, and controlling it to reach a designated destination. During autonomous driving, the vehicle can perceive environmental information and identify other vehicles in its vicinity. Summary of the Invention

[0003] In a first aspect of this disclosure, a method for trajectory prediction is provided. The method includes: identifying a trailer object and a associated trailer object in a traffic scene based on perception information from an autonomous vehicle; determining first trajectory prediction information for the trailer object within a target time period; determining the towing point of the trailer object and the trailer object; and generating second trajectory prediction information for the trailer object based on the first trajectory prediction information and the towing point.

[0004] In a second aspect of this disclosure, an apparatus for trajectory prediction is provided. The apparatus includes: an identification module configured to identify a trailer object and a trailer object associated with the trailer object in a traffic scene based on perception information from an autonomous vehicle; a first determination module configured to determine first trajectory prediction information of the trailer object within a target time period; a second determination module configured to determine the towing point of the trailer object and the trailer object; and a generation module configured to generate second trajectory prediction information of the trailer object based on the first trajectory prediction information and the towing point.

[0005] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0006] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by a processor to implement the method of the first aspect.

[0007] In a fifth aspect of this disclosure, a computer program product is provided. The computer program product includes computer-executable instructions that, when executed by a processor, implement the method of the first aspect.

[0008] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0009] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0011] Figure 2 A schematic diagram illustrating an example process for trajectory prediction according to some embodiments of the present disclosure is shown;

[0012] Figures 3A to 3B A schematic diagram of a trajectory prediction model according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A schematic structural block diagram of an example device for trajectory prediction according to certain embodiments of the present disclosure is shown; and

[0014] Figure 5 A block diagram of an apparatus capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0015] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0016] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0017] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0018] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0019] In this specification and the embodiments, any processing of personal information will be carried out only under the premise of legality (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information other than that necessary for basic functions will not affect the user's use of basic functions.

[0020] As briefly mentioned earlier, autonomous vehicles can perceive their surroundings during autonomous driving. Based on this perceived information, they can identify other vehicles in the environment. To ensure safe operation and avoid collisions, autonomous vehicles can predict the trajectories of other vehicles.

[0021] However, for vehicles with flexible connections, such as trailers, traditional technologies typically predict the trajectories of multiple vehicle segments independently. Since the trailer's movement is influenced by the trailer itself, this method results in significant errors in the predicted trailer trajectory. Conversely, technologies that predict the trajectory of the entire vehicle as a whole cannot accurately determine its center position, leading to a severe discrepancy between the predicted and actual trajectories. In such cases, autonomous vehicles cannot accurately avoid obstacles, resulting in substantial traffic hazards.

[0022] Based on this, embodiments of this disclosure propose a trajectory prediction scheme. According to this scheme, a trailer object and its associated trailer object in a traffic scene can be identified based on the perception information of an autonomous vehicle; further, first trajectory prediction information of the trailer object within a target time period can be determined; further, the towing points of the trailer object and the trailer object can be determined; and second trajectory prediction information of the trailer object is generated based on the first trajectory prediction information and the towing points.

[0023] In this way, the embodiments of this disclosure, during the generation of the second trajectory prediction information for the trailer object, can take into account the first trajectory prediction information of the trailer object within the target time period and the towing point, enabling the trajectory prediction for the trailer object to take into account the influence of the trailer object, thereby improving the accuracy of the trajectory prediction for the trailer object. Furthermore, the embodiments of this disclosure can subsequently utilize such first trajectory prediction information and such second trajectory prediction information to reduce the probability of collisions between the autonomous vehicle and the trailer object, thereby improving the safety and reliability of the autonomous vehicle.

[0024] Therefore, embodiments of this disclosure can improve the accuracy of predicting the trajectories of trailers and trolleys. Furthermore, based on such first trajectory prediction information and such second trajectory prediction information, embodiments of this disclosure can subsequently reduce the probability of collisions between the autonomous vehicle and the trailer / trolley, thereby improving the safety and reliability of the autonomous vehicle.

[0025] The following section provides a detailed description of various example implementations of this scheme, with reference to the accompanying drawings.

[0026] Example Environment

[0027] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. As shown, environment 100 may include vehicle 110. Environment 100 can be applied to autonomous driving scenarios, assisted driving scenarios, intelligent transportation scenarios, etc. Vehicle 110 may be an autonomous vehicle, etc. An autonomous vehicle is a means of transportation with autonomous driving capability (or driverless driving capability), also known as a driverless car, autonomous driving vehicle, etc. For ease of understanding, this disclosure uses vehicle 110 as an example for explanation and illustration, but is not limited thereto.

[0028] In some scenarios, users can access transportation services provided by vehicle 110 through ride-hailing apps. In other scenarios, vehicle 110 may also be referred to as a driverless taxi or robotaxi. During the process of vehicle 110 providing transportation services to users, vehicle 110 may be equipped with a safety operator. The safety operator can, for example, take over vehicle 110 in an emergency. Alternatively, vehicle 110 may also be in an unmanned state. Vehicle 110 may also be equipped with one or more display devices inside the vehicle to provide human-machine interaction functions.

[0029] In some scenarios, at least one other vehicle may be present in the surrounding environment of vehicle 110. To ensure the safe operation of vehicle 110, vehicle 110 can sense its surrounding environment and generate perception information 120. Based on this perception information 120, electronic device 130 can predict the trajectories of other vehicles to generate trajectory prediction information. For example, there may be a trailer object 150 and a trailer object 160 that are associated with the trailer object 150, such as through a soft connection, around vehicle 110. Therefore, electronic device 130 can predict the trajectories of the trailer object 150 and the trailer object 160 to ensure the safe operation of vehicle 110. Such electronic device 130 may include a terminal and / or a server.

[0030] Such a terminal can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, electronic device 130 may also support any type of user-facing interface (such as "wearable" circuitry).

[0031] Such servers can be standalone physical servers, server clusters or distributed systems composed of multiple physical servers, or cloud servers providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. Servers can include, for example, computing systems / servers such as mainframes, edge computing nodes, and computing devices in cloud environments, etc.

[0032] The electronic device 130 can be installed in the vehicle 110, or deployed in any electronic unit of the vehicle 110 (such as sensors, control units, etc.), or it can exist independently of the vehicle 110.

[0033] When the electronic device 130 and the vehicle 110 exist independently, a communication connection can be established between the vehicle 110 and the electronic device 130. This communication connection can be established via wired or wireless means. The communication connection may include, but is not limited to, Bluetooth connections, mobile network connections, Universal Serial Bus connections, and Wi-Fi connections; the embodiments of this disclosure are not limited in this respect. Based on this, the vehicle 110 and the electronic device 130 can achieve signaling interaction through their communication connection.

[0034] It should be understood that the structure and function of environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0035] Example process

[0036] Figure 2 A flowchart of an example process 200 for trajectory prediction according to some embodiments of the present disclosure is shown. Process 200 can be implemented at electronic device 130. References are made below. Figure 1 Describe the process 200.

[0037] like Figure 2 As shown in box 210, electronic device 130 identifies trailer objects and associated trailer objects in a traffic scene based on the perception information of the autonomous vehicle.

[0038] Taking an autonomous vehicle 110 as an example, the perception information of vehicle 110 includes, but is not limited to, image information, point cloud information, etc. Based on the perception information of vehicle 110, electronic device 130 can identify a trailer object 150 and a trailer object 160 associated with the trailer object in a traffic scene. The trailer object and the trailer object can be linked through a towing point; this disclosure does not limit the specific connection structure.

[0039] Therefore, the electronic device 130 can predict the driving trajectories of the trailer object 150 and the trailer object 160 to avoid collisions with the trailer object 150 and the trailer object 160.

[0040] In frame 220, electronic device 130 determines the first trajectory prediction information of the trailer object within the target time period.

[0041] As examples, the target time period can be a period of time from the current moment until a future time period that meets a preset duration. The preset duration can be set based on actual needs.

[0042] In some embodiments, the first trajectory information may indicate a first set of trajectories and the probabilities corresponding to the first set of trajectories. As some examples, the first trajectory prediction information may include multiple sets of trajectories, each set of trajectories may include multiple trajectory points associated with a prediction period. The probabilities corresponding to each set of trajectories may include the predicted probability that the trailer object 150 will travel according to each set of trajectories within a target time period.

[0043] Therefore, the embodiments of this disclosure can improve the accuracy of trajectory prediction for trailer objects by using multiple sets of trajectories and their corresponding probabilities.

[0044] In frame 230, electronic device 130 determines the towing point of the trailer object and the tractor object.

[0045] As examples, a towing point can be the connection point between a trailer and a tractor, based on which the trailer can provide power support to the tractor. The towing point can be determined through methods such as sensing information.

[0046] The following is in conjunction with the appendix Figure 3A To be continued Figure 3B The present disclosure describes some implementation methods of trajectory prediction in the embodiments. Figures 3A to 3B Schematic diagrams of trajectory prediction models 300A to 300B according to some embodiments of the present disclosure are shown. Models 300A to 300B can be implemented at electronic device 130. References are made below. Figure 1 Describe models 300A to 300B.

[0047] like Figure 3A As shown in / 3B, at label 302, x trailer (t) represents the lateral coordinate of the center point of trailer object 160 at time t; at marker 302, x tractor (t) represents the lateral coordinate of the center point of the trailer object 150 at time t; at marker 304, y tractor (t) represents the longitudinal coordinate of the center point of trailer object 160 at time t; at marker 304, y tractor (t) represents the longitudinal coordinate of the center point of the trailer object 150 at time t; at marker 306, l hitch-tractor This indicates the distance between the center point of the trailer object 150 and the towing point; at marker 308, l hitch-trailer This indicates the distance between the center point of trailer object 160 and the towing point; at marker 310, l trailer This indicates the length of the trailer object 160; at marker 312, θ tractor (t) represents the orientation of the trailer object 150 relative to the autonomous vehicle at time t; at marker 314, θ trailer(t) represents the orientation of trailer 160 relative to the autonomous vehicle at time t; at marker 316, β tractor (t) represents the yaw angle (the angle between speed and orientation) of the trailer at time t; at mark 318, v tractor (t) represents the speed of the trailer 150 at time t; at marker 320, v trailer_rear This indicates the speed of trailer 160; at label 322, the hitch indicates the towing point between trailer 150 and trailer 160.

[0048] The following will be combined with the appendix Figure 3A To be continued Figure 3B The trajectory prediction method of this disclosure is illustrated below.

[0049] To improve the accuracy of towing point determination, in some embodiments, the electronic device 130 can determine the location of the towing point using the first center point of the trailer object, the second center point of the trailer object, the orientation of the trailer object, and the orientation of the trailer object. Specifically, the electronic device 130 can determine the distance between the first center point of the trailer object and the second center point of the trailer object; and determine the towing point based on the distance, the orientation information of the trailer object, and the orientation information of the trailer object.

[0050] As some examples, continue Figure 3A / Figure 3B The electronic device 130 can determine the towing point (identifier 322) between the trailer object 150 and the semi-trailer object 160 using the following formulas 1 to 2:

[0051]

[0052] Back Figure 2 In box 240, electronic device 130 generates second trajectory prediction information for trailer object based on first trajectory prediction information and traction point.

[0053] In some embodiments, the second trajectory prediction information may include a second set of trajectories corresponding to the first set of trajectories, and the second set of trajectories corresponds to probabilities. For specific implementation details, please refer to the embodiments regarding the first trajectory information described above, which will not be repeated here.

[0054] In some embodiments, the electronic device 130 may determine a first distance from the first center point of the trailer object 150 to the traction point and a second distance from the second center point of the trailer object to the traction point based on such a traction point, and generate second trajectory prediction information of the trailer object based on such first trajectory prediction information, the first distance and the second distance.

[0055] As examples, the first center point of trailer object 150 may include the spatial center point of trailer object 150, etc. For example, electronic device 130 may determine the first center point of trailer object 150 based on the circumscribed polygon of trailer object 150, etc. The second center point of trailer object 160 is similar.

[0056] As examples, such first and second distances can be obtained through sensory information or through location information in a high-precision map. This disclosure does not limit the source of the first and second distances.

[0057] In some embodiments, the electronic device 130 may also determine the first orientation angle of the trailer object at a first moment based on the first trajectory prediction information.

[0058] As examples, the first moment includes the next moment after the current moment, etc. The electronic device 130 can perform trajectory prediction according to a preset period, where the time difference between the current moment and the next moment can be the length of the preset period. Therefore, the electronic device 130 can predict the first trajectory prediction information for a target time period after the current moment. In this embodiment, the first moment is taken as moment t+1.

[0059] As examples, electronic device 130 can predict the direction of travel of trailer object 150 at the next moment based on the first trajectory prediction information. Thus, electronic device 130 can determine the direction of travel of trailer object 150 at the next moment as a first heading angle. Alternatively, electronic device 130 can determine the first heading angle of trailer object 150 at the first moment through other calculation methods based on the first trajectory prediction information. This disclosure is not intended to limit this process.

[0060] In some embodiments, the electronic device 130 can determine the speed and third heading angle of the trailer object at a second time moment and the fourth heading angle of the trailer object at the second time moment, the second time moment being the time moment preceding the first time moment; determine the angular velocity change of the trailer object based on the speed, the third heading angle, and the fourth heading angle; and determine the second heading angle of the trailer object at the first time moment based on the angular velocity change.

[0061] Following the example above, the second time point can be time t. Based on this, as follows: Figure 3A As shown, the speed of the trailer object at the second moment can be v as indicated by label 318. trailer (t). The speed of trailer object 150 at the second time (time t) can be v as shown in label 318. trailer (t). The third orientation angle of the trailer object 150 can be as follows: Figure 3A The θ shown tractor(t), i.e., the angle shown in label 316. The fourth orientation angle of trailer object 160 at the second time (time t+1) can be the v shown in label 320. trailer_rear .

[0062] Therefore, the electronic device 130 can determine the change in angular velocity of the trailer object 160 through the positional relationship, and the change in angular velocity is...

[0063] As examples, electronic device 130 can determine the angular velocity change of trailer object 160 based on the following formula 3:

[0064]

[0065] Furthermore, based on the angular velocity change of the trailer object 160, the electronic device 130 can determine the change in the orientation angle of the trailer object 150 from the second time (time t) to the first time (time t+1). Thus, the electronic device 130 can determine the fourth orientation angle of the trailer object 160 at the determined first time (time t+1).

[0066] In some embodiments, the electronic device 130 can detect that the trailer object is in a reversing state. When the trailer object is in a reversing state, the electronic device 130 can determine that the directional angle between the speed of the trailer object 150 at a second moment and the orientation of the trailer object 150 itself exceeds 90 degrees. For example, as... Figure 3B The symbol 324 shown indicates the speed v of trailer object 150 at time t. tractor (t).

[0067] In some embodiments, when the next preset cycle arrives, the electronic device 130 can continue to predict the trajectory corresponding to the next moment. In this way, the electronic device 130 can continuously predict the second trajectory prediction information of the trailer object 160 during the driving of the vehicle 110, thereby adjusting the driving trajectory of the vehicle 110 in a timely manner and improving the safety of the vehicle 110.

[0068] In some embodiments, the electronic device 130 may determine whether to generate second trajectory prediction information for the associated trailer object based on a prior assessment of the trailer object. Specifically, the electronic device 130 may determine whether the first attribute information of the trailer object and / or the second attribute information of the trailer object meet preset conditions; and in response to the first attribute information and / or the second attribute information meeting the preset conditions, generate second trajectory prediction information for the trailer object based on the first trajectory prediction information and the towing point.

[0069] As examples, the first attribute information of trailer object 150 includes, but is not limited to, the distance from trailer object 150 to vehicle 110, the direction of trailer object 150 relative to vehicle 110, the orientation of trailer object 150, and the relative position of the towing point relative to trailer object 150, etc. The first attribute information of trailer object 160 includes, but is not limited to, the distance from trailer object 160 to vehicle 110, the direction of trailer object 160 relative to vehicle 110, the orientation of trailer object 160, and the relative position of the towing point relative to trailer object 160, etc.

[0070] Furthermore, the preset conditions may include at least one of the following: the distance between the trailer object or the trailer object and the autonomous vehicle is less than a first threshold; the direction of the trailer object and the trailer object relative to the autonomous vehicle is within a preset range; the orientation difference between the trailer object and the trailer object reaches a second threshold; and the position of the determined towing point relative to the trailer object and the trailer object satisfies the position constraints.

[0071] As examples, if the electronic device 130 determines that the trailer object and / or trolley object does not fall under any of the following conditions 1 to 6, it can generate second trajectory prediction information for the trolley object using the trajectory prediction scheme described above. For example, conditions 1 to 6 include the following.

[0072] Case 1: Vehicle 110 senses a non-vehicle object; Case 2: Vehicle 110 senses a non-trailer type vehicle object; Case 3: The distance between the sensed trailer or tractor object and vehicle 110 exceeds a first threshold; Case 4: The sensed trailer or tractor object is located behind vehicle 110 (e.g., the relative direction is outside a preset range); Case 5: The angular difference between the orientations of the sensed trailer and tractor object is less than a second threshold; Case 6: The determined position of the towing point relative to the trailer and tractor object does not meet the position constraints.

[0073] As examples, the positional constraints of the determined towing point relative to the trailer and the trailer may include, for instance, the distance between the towing point and the center point of the trailer, and the ratio between the towing point and the length of the trailer, satisfying a pre-defined range, etc. Other suitable positional constraints may also apply. In practical applications, such positional constraints can be adjusted according to requirements, and this disclosure does not limit their application.

[0074] Based on this approach, embodiments of this disclosure can incorporate the first trajectory prediction information of the trailer object within the target time period and the towing point into the process of generating the second trajectory prediction information of the trailer object. This allows the trajectory prediction of the trailer object to take into account the influence of the trailer object, improving the accuracy of the trajectory prediction. Furthermore, embodiments of this disclosure can subsequently utilize such first and second trajectory prediction information to reduce the probability of collisions between the autonomous vehicle and the trailer object, thereby improving the safety and reliability of the autonomous vehicle.

[0075] Therefore, embodiments of this disclosure can improve the accuracy of predicting the trajectories of trailers and trolleys. Furthermore, based on such first trajectory prediction information and such second trajectory prediction information, embodiments of this disclosure can subsequently reduce the probability of collisions between the autonomous vehicle and the trailer / trolley, thereby improving the safety and reliability of the autonomous vehicle.

[0076] Example devices and equipment

[0077] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes. Figure 4 A schematic structural block diagram of a device 400 for trajectory prediction according to certain embodiments of the present disclosure is shown. The device 400 may be implemented as or included in the electronic device 130 discussed above. The various modules / components in the device 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0078] like Figure 4 As shown, the device 400 includes an identification module 410 configured to identify a trailer object and a trailer object associated with the trailer object in a traffic scene based on the perception information of the autonomous vehicle; a first determination module 420 configured to determine first trajectory prediction information of the trailer object within a target time period; a second determination module 430 configured to determine the towing point of the trailer object and the trailer object; and a generation module 440 configured to generate second trajectory prediction information of the trailer object based on the first trajectory prediction information and the towing point.

[0079] In some embodiments, the first trajectory information indicates a first set of trajectories and the probabilities corresponding to the first set of trajectories, and the second trajectory prediction information includes a second set of trajectories corresponding to the first set of trajectories, and the second set of trajectories corresponds to the probabilities.

[0080] In some embodiments, the generation module 440 is further configured to: determine a first distance from the first center point of the trailer object to the traction point and a second distance from the second center point of the trailer object to the traction point based on the traction point; and generate second trajectory prediction information of the trailer object based on the first trajectory prediction information, the first distance and the second distance.

[0081] In some embodiments, the generation module 440 is further configured to: determine a first position of the trailer object at a first moment based on the first trajectory prediction information, the first position corresponding to a first center point; and determine a second position of the trailer object at a first moment based on the first position, a first distance, a second distance, a first orientation angle of the trailer object at the first moment, and a second orientation angle of the trailer object at the first moment, the second position corresponding to a second center point.

[0082] In some embodiments, the device 400 further includes a third determining module configured to: determine a first orientation angle of the trailer object at a first moment based on the first trajectory prediction information.

[0083] In some embodiments, the device 400 further includes a fourth determining module configured to: determine the speed and third heading angle of the trailer object at a second time moment and the fourth heading angle of the trailer object at the second time moment, the second time moment being the time moment preceding the first time moment; determine the angular velocity change of the trailer object based on the speed, the third heading angle, and the fourth heading angle; and determine the second heading angle of the trailer object at the first time moment based on the angular velocity change.

[0084] In some embodiments, the second determining module 430 is further configured to: determine the distance between the first center point of the trailer object and the second center point of the trailer object; and determine the towing point based on the distance, the orientation information of the trailer object and the orientation information of the trailer object.

[0085] In some embodiments, the generation module 440 is further configured to: determine whether the first attribute information of the trailer object and / or the second attribute information of the trailer object meet preset conditions; and in response to the first attribute information and / or the second attribute information meeting preset conditions, generate the second trajectory prediction information of the trailer object based on the first trajectory prediction information and the towing point.

[0086] In some embodiments, the preset conditions include at least one of the following: the distance from the trailer object or the trailer to the autonomous vehicle is less than a first threshold; the orientation of the trailer object and the trailer relative to the autonomous vehicle is within a preset range; the orientation difference between the trailer object and the trailer reaches a second threshold; and the position of the determined towing point relative to the trailer object and the trailer satisfies the position constraints.

[0087] The units included in device 400 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units may be implemented using software and / or firmware, such as machine-executable instructions stored on a storage medium. In addition to or as an alternative to machine-executable instructions, some or all of the units in device 1000 may be implemented at least partially by one or more hardware logic components. By way of example, and not limitation, exemplary types of hardware logic components that may be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0088] Figure 5 A block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented is shown. It should be understood that... Figure 5 The electronic device 500 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 5 The electronic device 500 shown can be used to achieve Figure 1 Electronic devices 130 or Figure 4 Device 400.

[0089] like Figure 5 As shown, electronic device 500 is in the form of a general-purpose electronic device. Components of electronic device 500 may include, but are not limited to, one or more processors or processing units 510, memory 520, storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. Processing unit 510 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 520. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 500.

[0090] Electronic device 500 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 520 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 530 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media capable of storing information and / or data and accessible within electronic device 500.

[0091] Electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 5 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 520 may include computer program product 525 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0092] Communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 500 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 500 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0093] Input device 550 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 560 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 500 can also communicate with one or more external devices (not shown) via communication unit 540 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 500, or with any device that enables electronic device 500 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0094] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0095] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0096] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0097] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0099] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A trajectory prediction method, comprising: Based on the perception information of autonomous vehicles, identify the trailer objects and the associated trailer objects in the traffic scene. Determine the first trajectory prediction information of the trailer object within the target time period; Determine the towing points of the trailer and the tractor-trailer; as well as Based on the first trajectory prediction information and the traction point, the second trajectory prediction information of the trailer object is generated.

2. The method according to claim 1, wherein the first trajectory information indicates the probability corresponding to the first set of trajectories and the first set of trajectories, and the second trajectory prediction information includes a second set of trajectories corresponding to the first set of trajectories, and the second set of trajectories corresponds to the probability.

3. The method according to claim 1, wherein generating second trajectory prediction information for the trailer object based on the first trajectory prediction information and the traction point includes: Based on the towing point, determine the first distance from the first center point of the trailer object to the towing point and the second distance from the second center point of the trailer object to the towing point; as well as Based on the first trajectory prediction information, the first distance, and the second distance, second trajectory prediction information of the trailer object is generated.

4. The method according to claim 3, wherein generating the second trajectory prediction information of the trailer object based on the first trajectory prediction information, the first distance, and the second distance includes: Based on the first trajectory prediction information, the first position of the trailer object at the first moment is determined, and the first position corresponds to the first center point; Based on the first position, the first distance, the second distance, the first orientation angle of the trailer object at the first time and the second orientation angle of the trailer object at the first time, the second position of the trailer object at the first time is determined, and the second position corresponds to the second center point.

5. The method according to claim 4, further comprising: Based on the first trajectory prediction information, the first orientation angle of the trailer object at the first moment is determined.

6. The method according to claim 4, further comprising: Determine the speed and third orientation angle of the trailer object at a second moment, and the fourth orientation angle of the trailer object at the second moment, wherein the second moment is the moment preceding the first moment; The angular velocity change of the trailer is determined based on the speed, the third orientation angle, and the fourth orientation angle; as well as Based on the change in angular velocity, the second orientation angle of the trailer at the first moment is determined.

7. The method of claim 1, wherein determining the towing point of the trailer and the tethered object comprises: Determine the distance between the first center point of the trailer object and the second center point of the semi-trailer object; as well as The towing point is determined based on the distance, the orientation information of the trailer object, and the orientation information of the trolley object.

8. The method according to claim 1, wherein generating second trajectory prediction information for the trailer object based on the first trajectory prediction information and the traction point comprises: Determine whether the first attribute information of the trailer object and / or the second attribute information of the trailer object meet preset conditions; as well as In response to the first attribute information and / or the second attribute information satisfying the preset condition, second trajectory prediction information of the trailer object is generated based on the first trajectory prediction information and the traction point.

9. The method according to claim 8, wherein the preset condition includes at least one of the following: The distance between the trailer or the semi-trailer and the autonomous vehicle is less than a first threshold. The orientation of the trailer and the trolley relative to the autonomous vehicle is within a preset range; The orientation difference between the trailer and the trolley reaches a second threshold. The determined position of the traction point relative to the trailer object and the semi-trailer object satisfies the position constraints.

10. An apparatus for trajectory prediction, comprising: The identification module is configured to identify trailer objects and associated trailer objects in a traffic scene based on the perception information of the autonomous vehicle. The first determining module is configured to determine the first trajectory prediction information of the trailer object within the target time period; The second determining module is configured to determine the towing points of the trailer object and the semi-trailer object; as well as The generation module is configured to generate second trajectory prediction information for the trailer object based on the first trajectory prediction information and the traction point.

11. An electronic device, comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, which, when executed by the at least one processing unit, cause the electronic device to perform the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 9.

13. A computer program product comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 9.

Images