Patent Publication Number: US-2023159023-A1

Title: Method and electronic apparatus for predicting path based on object interaction relationship

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of Taiwan application serial no. 110143485, filed on Nov. 23, 2021. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
     BACKGROUND 
     Technical Field 
     The disclosure relates to an autonomous driving decision-making technology, and in particular to a method and an electronic apparatus for predicting a path based on an object interaction relationship. 
     Description of Related Art 
     With the vigorous development of science and technology, research on autonomous driving is thriving. Currently, an autonomous vehicle analyzes a large number of information in real time to realize effective self-driving. For example, an autonomous vehicle needs to accurately analyze data such as map information or surrounding objects during operation. The analysis results of these data are used as the basis for controlling the driving of the autonomous vehicle, so that the decision of the autonomous vehicle in the event of an emergency is similar to the behavior of a human driver. 
     However, the decision-making ability of autonomous driving has an effect on the safety of the autonomous vehicle. Once a decision of autonomous driving is wrong, serious problems such as traffic accidents may occur. Therefore, improving the accuracy of decision making in autonomous driving is an important issue to those skilled in the art. 
     SUMMARY 
     The disclosure provides a method and an electronic apparatus for predicting a path based on an object interaction relationship, which improve the accuracy of predicting a trajectory of an object around a main vehicle. 
     A method for predicting a path based on an object interaction relationship of the disclosure is adapted for an electronic apparatus including a processor. The processor is configured to control a first vehicle. The method includes the following. A video including multiple image frames is received. Object recognition is performed on a certain image frame among the plurality of image frames to recognize at least one object in the certain image frame. 
     Preset interactive relationship information associated with the at least one object is obtained from an interactive relationship database based on the at least one object. A first trajectory for navigating the first vehicle is determined based on the preset interactive relationship information. 
     An electronic apparatus of the disclosure is adapted for controlling a first vehicle. The electronic apparatus includes a storage device and a processor. The storage device stores an interactive relationship database. The processor is coupled to the storage device, and the processor is configured to: receive a video including multiple image frames; perform object recognition on a certain image frame among the plurality of image frames to recognize at least one object in the certain image frame; obtain preset interactive relationship information associated with the at least one object from the interactive relationship database based on the at least one object; and determine a first trajectory for navigating the first vehicle based on the preset interactive relationship information. 
     Based on the above, in the method and the electronic apparatus for predicting a path based on an object interaction relationship provided by the embodiment of the disclosure, the predicted trajectory of the predicted object is generated based on the preset interactive relationship information between the objects. The predicted trajectory is used to determine the trajectory for navigating the main vehicle. In this way, the predicted trajectory of the predicted object is generated by considering the preset interactive relationship between the objects. The disclosure reduces the trajectory prediction error of the object around the main vehicle. Based on the above, the accuracy of predicting the trajectory of the object around the main vehicle is improved, and the trajectory for navigating the main vehicle is accurately planned. 
     To provide a further understanding of the above features and advantages of the disclosure, embodiments accompanied with drawings are described below in details. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates a block diagram of a path prediction system based on an embodiment of the disclosure. 
         FIG.  2    illustrates a flow chart of a method for predicting a path based on an object interaction relationship based on an embodiment of the disclosure. 
         FIG.  3    illustrates a schematic view of object recognition based on an embodiment of the disclosure. 
         FIG.  4    illustrates a schematic view of an object interactive relationship based on an embodiment of the disclosure. 
         FIG.  5    illustrates a flow chart of a method for predicting a path based on an object interaction relationship based on an embodiment of the disclosure. 
         FIG.  6    illustrates a schematic view of an object interactive relationship based on an embodiment of the disclosure. 
         FIG.  7    illustrates a schematic view of an object interactive relationship based on an embodiment of the disclosure. 
         FIG.  8    illustrates a flow chart of a method for predicting a path based on an object interaction relationship based on an embodiment of the disclosure. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
       FIG.  1    illustrates a block diagram of a path prediction system based on an embodiment of the disclosure. Referring to  FIG.  1   , a path prediction system  10  includes an electronic apparatus  11  and an image capturing apparatus  12 . The electronic apparatus  11  includes but is not limited to include a processor  110 , a storage device  120 , and an input/output (I/O) device  130 . The electronic apparatus  11  of this embodiment is, for example, a device that is disposed on a vehicle and has arithmetic functions. However, the electronic apparatus  11  may also be a remote server to remotely control the vehicle, and the disclosure is not limited thereto. 
     The processor  110  is coupled to the storage device  120  and the input/output device  130 . The processor  110  is, for example, a central processing unit (CPU), or other programmable general-purpose or special-purpose devices such as a microprocessor, a digital signal processor (DSP), a programmable controller, application specific integrated circuits (ASIC), a programmable logic controller (PLC), or other similar devices or a combination of these devices. The processor  110  loads and performs the program stored in the perform storage device  120  to perform the method for predicting a path based on an object interaction relationship based on the embodiment of the disclosure. 
     The storage device  120  is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk, or a similar element or a combination of the above elements. The storage device  120  is used to store the program and data that may be performed by the processor  110 . In an embodiment, the storage device  120  stores an interactive relationship database  121  and an environment information database  122 . In addition, the storage device  120  also stores, for example, a video received by the input/output device  130  from the image capturing apparatus  12 . 
     The input/output device  130  is a wired or wireless transmission interface such as a Universal Serial Bus (USB), RS232, Bluetooth (BT), and Wireless fidelity (Wi-Fi). The input/output device  130  is used to receive a video provided by an image capturing apparatus such as a camera. 
     The image capturing apparatus  12  is used to extract an image in front of it. The image capturing apparatus  12  may be a camera that adopts a charge coupled device (CCD), a complementary metal-oxide semiconductor (CMOS) element, or other element lenses. In this embodiment, the image capturing apparatus  12  may be disposed in a main vehicle (also known as a first vehicle), and disposed to extract a road image in front of the main vehicle. It is worth noting that this main vehicle is a vehicle controlled by the processor  110 . 
     In an embodiment, the electronic apparatus  11  may include the above-mentioned image capturing apparatus, and the input/output device  130  is a bus used to transmit data within the device, and the video captured by the image capturing apparatus may be transmitted to the processor  110  for processing. The embodiment is not limited to the above architecture. 
       FIG.  2    illustrates a flow chart of a method for predicting a path based on an object interaction relationship based on an embodiment of the disclosure. Referring to  FIG.  1    and  FIG.  2   , the method of this embodiment is adapted to the above-mentioned electronic apparatus  11 . The following is detailed steps of the method for predicting a path based on an object interaction relationship of this embodiment in connection with the elements of the electronic apparatus  11 . 
     First, in step S 202 , the processor  110  may receive a video including a plurality of image frames. Specifically, the processor  110  receives the video including the plurality of image frames from the image capturing apparatus  12  by using the input/output device  130 . 
     In step S 204 , the processor  110  may perform object recognition on a certain image frame among the plurality of image frames, so as to recognize at least one object in the certain image frame. In an embodiment, the processor  110 , for example, performs object detection and a recognition algorithm on the certain image frame to recognize the object in the certain image frame. For example, the processor  110  extracts features in the certain image frame and recognizes the object by using a pre-established and trained object recognition model. The object recognition model is a machine learning model established through, for example, a convolutional neural network (CNN), deep neural networks (DNN), or other types of neural networks combined with a classifier. The object recognition model learns from a large number of input images, and may extract the features in the image and classify these features to recognize the object corresponding to a specific object type. Those skilled in the art should know how to train the object recognition model that may recognize the object in the certain image frame. 
     For example,  FIG.  3    illustrates a schematic view of object recognition based on an embodiment of the disclosure. Referring to  FIG.  3   , the processor  110  may obtain an image frame img through the image capturing apparatus  12 , and the image frame img is the road image in front of the main vehicle. After the processor  110  performs object recognition on the image frame img, the processor  110  may recognize an object obj 1  and an object obj 2 . In this embodiment, the processor  110  may classify the object obj 1  as a traffic cone and classify the object obj 2  as a vehicle by using the object recognition model. It is worth mentioning that the processor  110  may also analyze the image content of the plurality of image frames to obtain the distance between the main vehicle and the object in the image frame, the distance between the plurality of objects in the image frame, and the movement velocity of the object. For example, the processor  110  may analyze the image content of the plurality of image frames to obtain the distance between the main vehicle and the object obj 1  or the object obj 2  in  FIG.  3   , the distance between the object obj 1  and the object obj 2 , or the movement velocity of the object obj 2 . However, the above-mentioned technical concept related to analyzing distance and velocity by using the image content of image frames is a common technical method to those skilled in the art and will not be repeated herein. 
     In step S 206 , the processor  110  may obtain preset interactive relationship information associated with at least one object from the interactive relationship database  121  based on the at least one object. In this embodiment, the preset interactive relationship information between the plurality of preset objects may be included in the interactive relationship database. 
     In an embodiment, the preset object may refer to a certain traffic object in the road image, and the preset interactive relationship information may refer to the object interactive relationship among a plurality of certain traffic objects. Taking the situation of an autonomous vehicle driving on the road as an example, the certain traffic object may be a traffic cone, a ball, a street tree, a vehicle, a construction sign, a person, a vehicle, etc. The disclosure is not limited thereto. In other words, the certain traffic object refers to an object that may appear on the road and may induce a driving behavior by a human driver. 
     In this embodiment, the object interactive relationship between certain traffic objects may be divided into two types of object interactive relationships. The first type of object interactive relationship records the object interactive relationship between an actual object and a virtual object. Based on the first type of object interactive relationship, the virtual object and the trajectory for the virtual object may be predicted and generated based on the detected actual object. On the other hand, the second type of object interactive relationship records the object interactive relationship between two actual objects. Based on the second type of object interactive relationship, the trajectory of one actual object may be predicted based on another one of the detected two actual objects. In other words, the first type of object interactive relationship may include the object interactive relationship between the virtual object that does not appear in the lane but is predicted to appear because of the actual object appearing on the lane and the actual object. On the other hand, the second type of object interactive relationship may include the object interactive relationship between two actual objects appearing in the lane. 
     The following will explain the situation that may occur on an actual lane. For example, the object interactive relationship may include, for example, the object interactive relationship between a ball and a person, and the object interactive relationship between a traffic cone/street tree/construction sign and a vehicle, etc. The disclosure is not limited thereto. In this embodiment, the object interactive relationship between the ball (the actual object) and the person (the virtual object) belongs to the first type of object interactive relationship. Generally, when the ball rolls into the lane, there is a possibility that a child (person) chasing the ball and rushing into the lane may appear. Therefore, the interactive relationship database may store the object interactive relationship between the ball and the person as “after the ball is detected, a person with the same path as the ball and moving in m seconds appears after n seconds”, where n and m are preset values. On the other hand, the object interactive relationship between the traffic cone/street tree/construction sign (that is, the actual object) and the vehicle (that is, the actual object) belongs to the second type of object interactive relationship. Generally, when a human driver is driving a vehicle, if the driver sees an obstacle such as a traffic cone/street tree/construction sign in the lane in front of the vehicle, the driver turns to avoid these obstacles. Therefore, the interactive relationship database may store the object interactive relationship between the traffic cone/street tree/construction sign and the vehicle as “when the traffic cone/street tree/construction sign and the vehicle are detected, the driving speed of the vehicle slows down to k kilometers per hour in order for the vehicle to switch lanes when the vehicle is j meters away from the traffic cone/street tree/construction sign”, where j and k are preset values. It is worth noting that driver may encounter other different situations while driving the vehicle, so the disclosure is not limited to the above object interactive relationships. Those skilled in the art should design an object interactive relationship between other certain traffic objects based on the enlightenment of the above-mentioned exemplary embodiment. 
     In step S 208 , the processor  110  may determine a trajectory (also known as a first trajectory) for navigating the main vehicle based on the preset interactive relationship information. In this embodiment, the trajectory may include a path and the velocity at each trajectory point in the path. Specifically, the processor  110  may generate a predicted trajectory of a predicted object based on the preset interactive relationship information. Next, the processor  110  may determine the first trajectory of the main vehicle based on the predicted trajectory. 
     In an embodiment, the processor  110  first determines whether the preset interactive relationship information includes the first type or the second type of object interactive relationship to generate a determination result. Next, the processor  110  may generate the predicted trajectory of the predicted object based on the determination result. 
     In this embodiment, in response to determining that the preset interactive relationship information includes the first type of object interactive relationship, the processor  110  may obtain the preset object corresponding to the preset interactive relationship information associated with the recognized object from the interactive relationship database  121  based on the object recognized in step S 204  as the predicted object. As in the foregoing example, assuming that there is the first type of object interactive relationship between the ball and the person, the processor  110  may obtain the “person” from the interactive relationship database  121  as the predicted object based on the recognized ball. Next, the processor  110  may calculate the predicted trajectory of the object based on the preset interactive relationship information and the trajectory of the recognized object. 
       FIG.  4    illustrates a schematic view of an object interactive relationship based on an embodiment of the disclosure. For the convenience of description,  FIG.  4    illustrates a schematic view of a main vehicle  1  and other objects mapped onto the lane. In this embodiment, it is assumed that the interactive relationship database  121  stores the preset interactive relationship information “after the ball is detected, a person with the same path as the ball and moving in m seconds appears after n seconds” between the actual object “ball” and the virtual object “person”. 
     Referring to  FIG.  4   , the main vehicle  1  of this embodiment is controlled by the processor  110  to drive along a trajectory d 1 . This trajectory d 1  is an original target trajectory of the main vehicle  1 . It is assumed that the processor  110  recognizes an object  2  from the certain image frame, and this object  2  is classified as a ball. In this embodiment, the processor  110  obtains the preset interactive relationship information associated with the object  2 , “after the ball is detected, a person with the same path as the ball and moving in m seconds appears after n seconds” from the interactive relationship database  121  based on the object  2 . Based on the preset interactive relationship information, the interactive relationship between the object  2  and the virtual object “person” is stored in the interactive relationship database  121 , so the processor  110  may determine that the preset interactive relationship information associated with the object  2  includes the first type of object interactive relationship. Next, the processor  110  may obtain a preset object  4  corresponding to the preset interactive relationship information associated with the object  2  from the interactive relationship database  121  based on the object  2  as the predicted object. In this embodiment, the preset object  4  is a “person”. Therefore, the processor  110  may calculate a trajectory d 4  of the preset object  4  based on the preset interactive relationship information “after the ball is detected, a person with the same path as the ball and moving in m seconds appears after n seconds” and the trajectory d 2  of the object  2 . 
     In this embodiment, if the processor  110  determines that the preset interactive relationship information includes the second type of object interactive relationship, the processor  110  may adopt a predicted trajectory generation process different from that for the first type of object interactive relationship. Specifically, referring to  FIG.  5   ,  FIG.  5    illustrates a flow chart of a method for predicting a path based on an object interaction relationship based on an embodiment of the disclosure. In step S 2081 , in response to determining that the preset interactive relationship information includes the second type of object interactive relationship, the processor  110  may determine whether the object recognized in step S 204  includes a second vehicle. In step S 2082 , in response to determining that the recognized object includes the second vehicle, the processor  110  may determine whether the recognized object includes a first object with the preset interactive relationship information with the second vehicle. In step S 2083 , in response to determining that the recognized object includes the first object, the processor  110  sets the second vehicle as the predicted object. Next, in step S 2084 , the processor  110  calculates the predicted trajectory of the predicted object based on the preset interactive relationship information, the position of the first object relative to the predicted object, and the movement velocity of the predicted object. 
       FIG.  6    illustrates a schematic view of an object interactive relationship based on an embodiment of the disclosure. For the convenience of description,  FIG.  6    illustrates a schematic view of a main vehicle  3  and other objects mapped onto the lane. In this embodiment, it is assumed that the interactive relationship database  121  stores the preset interactive relationship information “when the traffic cone and the vehicle are detected, the driving speed of the vehicle slows down to k kilometers per hour in order for the vehicle to switch lanes when the vehicle is j meters away from the traffic cone” between the actual object “vehicle” and the actual object “traffic cone”. 
     Referring to  FIG.  6   , the main vehicle  3  of this embodiment is controlled by the processor  110  to drive along a trajectory d 3 , and this trajectory d 3  is an original target trajectory of the main vehicle  3 . The processor  110  recognizes an object  6  and an object  8  from the certain image frame, and the object  6  is classified as a traffic cone, and the object  8  is classified as a vehicle. In this embodiment, the processor  110  obtains the preset interactive relationship information respectively associated with the object  6  and the object  8  from the interactive relationship database  121  based on the object  6  and the object  8 . In this embodiment, the preset interactive relationship obtained by the processor  110  from the interactive relationship database  121  based on the object  6  or the object  8  may include the interactive relationship information between the actual object “vehicle” and the actual object “traffic cone”. Therefore, the processor  110  determines that the preset interactive relationship information associated with the object  6  or the object  8  includes the second type of object interactive relationship. Next, in response to determining that the preset interactive relationship information includes the second type of object interactive relationship, the processor  110  determines whether the recognized object  6  and object  8  include a vehicle. In this embodiment, in response to determining that the recognized object  8  is a vehicle, the processor  110  further determines whether the other recognized objects are objects with the second type of object interactive relationship with the object  8 . In this embodiment, the processor  110  may determine that there is the second type of object interactive relationship between the object  6  and the object  8  among the other recognized objects, so the processor  110  sets the object  8  (the vehicle) as the predicted object. In addition, the processor  110  calculates a predicted trajectory d 8  of the object  8  based on the preset interactive relationship information “when the traffic cone and the vehicle are detected, the driving speed of the vehicle slows down to k kilometers per hour in order for the vehicle to switch lanes when the vehicle is j meters away from the traffic cone”, the position of the object  6  relative to the object  8 , and the movement velocity of the object  8 . 
       FIG.  7    illustrates a schematic view of an object interactive relationship based on an embodiment of the disclosure. For the convenience of description,  FIG.  7    illustrates a schematic view of a main vehicle  5  and other objects mapped onto the lane. In this embodiment, it is assumed that the interactive relationship database  121  stores the preset interactive relationship information “when two vehicles are detected, the following vehicle accelerates to y kilometers per hour when the following vehicle is x meters away from the preceding vehicle to switch lanes” between the actual object “vehicle” and the actual object “vehicle”, where x and y are preset values. 
     Referring to  FIG.  7   , the main vehicle  5  of the embodiment is controlled by the processor  110  to drive along a trajectory d 5 , and this trajectory d 5  is an original target trajectory of the main vehicle  5 . The processor  110  recognizes an object  10  and an object  12  from the certain image frame, and both the object  10  and the object  12  are classified as vehicles. The object  10  is the preceding vehicle, the object  12  is the following vehicle, and the object  10  is driving along a trajectory d 10 . In this embodiment, the processor  110  obtains the preset interactive relationship information respectively associated with the object  10  and the object  12  from the interactive relationship database  121  based on the object  10  and the object  12 . In this embodiment, the preset interactive relationship information obtained by the processor  110  from the interactive relationship database  121  based on the object  10  or the object  12  may include the interactive relationship between the actual object “vehicle” and the actual object “vehicle”. Therefore, the processor  110  determines that the preset interactive relationship information associated with the object  10  or the object  12  includes the second type of object interactive relationship. Next, in response to determining that the preset interactive relationship information includes the second type of object interactive relationship, the processor  110  determines whether the recognized object  10  and the object  12  include a vehicle. In this embodiment, in response to determining that the recognized object  12  is a vehicle, the processor  110  determines whether the other recognized objects are objects with a second type of object interactive relationship with the object  12 . In this embodiment, the processor  110  may determine that there is the second type of object interactive relationship between the object  10  and the object  12  among the other recognized objects, so the processor  110  sets the object  12  (the following vehicle) as the predicted object. In addition, the processor  110  calculates a predicted trajectory d 12  of the object  12  based on the preset interactive relationship information “when two vehicles are detected, the following vehicle accelerates to y kilometers per hour when the following vehicle is x meters away from the preceding vehicle to switch lanes”, the position of the object  10  relative to the object  12 , and the movement velocity of the object  12 . 
     After the predicted trajectory of the predicted object other than the main vehicle is calculated, the processor  110  determines the first trajectory for navigating the main vehicle based on the predicted trajectory. In an embodiment, the processor  110  may calculate a predicted collision time between a generated predicted trajectory and the original target trajectory of the main vehicle, and adjust the original target trajectory of the main vehicle based on the predicted collision time to generate the first trajectory. For example, the processor  110  adjusts the driving velocity (for example, acceleration and deceleration) or the driving direction (for example, turning) of the main vehicle in the original target trajectory to generate the first trajectory. It is worth noting that the processor  110  may update the path included in the original target trajectory and the velocity at each trajectory point in the path based on the adjusted driving velocity or direction of the main vehicle to generate the first trajectory. In this way, by considering the preset interactive relationship between objects, the embodiment of the disclosure may accurately predict the trajectory of the object around the main vehicle, thereby accurately planning the trajectory for navigating the main vehicle. 
     Referring to  FIG.  4    again, for example, after calculating the trajectory d 4  of the object  4 , the processor  110  may calculate a predicted collision time t between the trajectory d 4  and the trajectory d 1  of the main vehicle  1 , and reduce the driving velocity of the main vehicle  1  in the trajectory d 1  of the main vehicle  1  based on the predicted collision time t. In other words, the processor  110  may reduce the velocity at a specific trajectory point in the trajectory d 1  to update the original target trajectory to generate the first trajectory for navigating the main vehicle  1 . In this way, the main vehicle  1  may be prevented from colliding with the preset object  4  that may rush out. 
       FIG.  8    illustrates a flow chart of a method for predicting a path based on an object interaction relationship based on an embodiment of the disclosure. In an embodiment, the processor  110  may further determine the predicted trajectory of the predicted object based on an object feature value of a surrounding object or surrounding environment information. 
     Referring to  FIG.  8   , in step S 801 , the processor  110  may sense an object in the certain image frame as the predicted object. In step S 8021 , the processor  110  may perform an image recognition operation on the certain image frame to obtain the object feature value of the predicted object. The object feature value is, for example, the signal of the vehicle&#39;s turn signal or the speed of the vehicle. For example, the image recognition operation may be implemented as obtaining the object feature value of the predicted object in the certain image frame by using a pre-established and trained object recognition model, and the disclosure is not limited thereto. In step S 8022 , the processor  110  may obtain the preset interactive relationship information associated with the object from the interactive relationship database  121  based on the object recognized from the certain image frame. The description of step S 206  may be referred to for the detailed implementation of obtaining the preset interactive relationship information, which will not be repeated herein. 
     In step S 8023 , the processor  110  may obtain lane geometry information from the environment information database  122  based on positioning data of the main vehicle. The environment information database  122  may store map information, and the map information may include road information and intersection information. The processor  110  may obtain the lane geometry information such as lane reduction and curves from the environment information database  122 . Specifically, the electronic apparatus  11  of the embodiment may be further coupled to a positioning device (not shown). The positioning device is, for example, a Global Positioning System (GPS) device, which may receive the positioning data of the current position of the main vehicle, including longitude and latitude data. 
     In step S 803 , the processor  110  may calculate the predicted trajectory of the predicted object based on at least one of the object feature value, the preset interactive relationship information, and the lane geometry information. Referring to  FIG.  7   , assuming that the obtained object feature value of the object  12  is the right signal of the turn signal lighting up, the processor  110  may determine that the object  12  is about to turn right. Here, the processor  110  may calculate the trajectory d 12  of the object  12  based on the object feature value. In an example of lane geometry information, assuming that the obtained lane geometry information is reduction of the road ahead, the processor  110  may determine that the predicted object drives towards an unreduced lane when the predicted object is a vehicle. Here, the processor  110  may calculate the trajectory of the predicted object based on the lane geometry information “reduction of the road ahead”. 
     In step S 804 , the processor  110  may determine the first trajectory for navigating the main vehicle based on the predicted trajectory of the predicted object. The aforementioned embodiment may be referred to for the specific description of determining the first trajectory, which will not be repeated herein. After the first trajectory is determined, the processor  110  may control the movement of the main vehicle based on the first trajectory. 
     It is worth noting that each step in  FIGS.  2 ,  5  and  8    and the aforementioned embodiment may be implemented as a plurality of codes or circuits, and the disclosure is not limited thereto. In addition, the methods shown in  FIGS.  2 ,  5 , and  8    may be used in connection with the above exemplary embodiment or used alone, and the disclosure is not limited thereto. 
     In summary, in the method and the electronic apparatus for predicting a path based on an object interaction relationship provided by the embodiment of the disclosure, the predicted trajectory of the predicted object may be generated based on the preset interactive relationship information between the objects. The predicted trajectory is used to determine the trajectory for navigating the main vehicle. In this way, the predicted trajectory of the predicted object is generated by considering the preset interactive relationship between the objects. The disclosure may reduce the trajectory prediction error of the objects around the main vehicle, thereby improving the accuracy of predicting the trajectory of these surrounding objects. In addition, the disclosure may accurately calculate and the predicted trajectory of the predicted object through the object feature values of the surrounding objects and the lane geometry information. Based on the above, the disclosure may accurately plan the trajectory for navigating the main vehicle by effectively predicting the impact of the surrounding objects on the main vehicle. 
     Although the disclosure has been disclosed in the above by way of embodiments, the embodiments are not intended to limit the disclosure. Those with ordinary knowledge in the technical field can make various changes and modifications without departing from the spirit and scope of the disclosure. Therefore, the protection scope of the disclosure is subject to the scope of the appended claims.