CN116923398A - Vehicle track prediction method and device, vehicle and storage medium - Google Patents

Vehicle track prediction method and device, vehicle and storage medium Download PDF

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Publication number
CN116923398A
CN116923398A CN202310933449.2A CN202310933449A CN116923398A CN 116923398 A CN116923398 A CN 116923398A CN 202310933449 A CN202310933449 A CN 202310933449A CN 116923398 A CN116923398 A CN 116923398A
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Prior art keywords
target vehicle
determining
lane
motion state
information
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冉涪文
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Chongqing Changan Automobile Co Ltd
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Chongqing Changan Automobile Co Ltd
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Priority to CN202310933449.2A priority Critical patent/CN116923398A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention relates to the technical field of track prediction, in particular to a vehicle track prediction method, a vehicle track prediction device, a vehicle and a storage medium, wherein the method comprises the following steps: acquiring instantaneous motion information of a target vehicle, and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information; acquiring cache track information of the target vehicle, and determining an expected motion state of the target vehicle based on the cache track information; based on the instantaneous motion state and the expected motion state, predicted trajectory information of the target vehicle is determined. The method and the device can acquire the instantaneous motion state and the expected motion state of the target vehicle, and analyze the predicted track information of the target vehicle in a combined way, so that the accuracy of track prediction is improved.

Description

Vehicle track prediction method and device, vehicle and storage medium
Technical Field
The present invention relates to the field of track prediction technologies, and in particular, to a vehicle track prediction method, device, vehicle, and storage medium.
Background
With the continuous development of future technologies, automatic driving technologies are gradually developed, and track prediction of vehicles has become a research project of great interest and importance. The track prediction of the vehicle is one of the important realization of the automatic driving technology, and the authenticity and timeliness of the track prediction directly influence the practicability of the automatic driving technology. The vehicle track prediction mainly obtains possible future motion tracks of the vehicle by analyzing the historical tracks, environmental information, driver behaviors and other factors of the vehicle, and further provides guidance for automatic driving decision and control.
However, at present, the motion trail of the vehicle is obtained by analyzing factors such as the historical trail of the vehicle, and the situation that the target has no historical trail buffer just appears or suddenly appears exists, so that the future motion state of the vehicle cannot be accurately predicted. Meanwhile, the running state of the vehicle is complicated, and the running state of the vehicle cannot be accurately defined through the movement track.
Accordingly, there is a need for improvement and advancement in the art.
Disclosure of Invention
The invention aims to solve the technical problems that the future motion state of a vehicle cannot be accurately predicted and the motion state of the vehicle cannot be accurately defined through the motion trail in the prior art.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a vehicle track prediction method, wherein the method includes:
acquiring instantaneous motion information of a target vehicle, and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information;
acquiring cache track information of the target vehicle, and determining an expected motion state of the target vehicle based on the cache track information;
Based on the instantaneous motion state and the expected motion state, predicted trajectory information of the target vehicle is determined.
According to the technical scheme, the method and the device can accurately analyze the instantaneous motion information of the target vehicle when the target vehicle just appears, further analyze the instantaneous motion state, and analyze the expected motion state of the target vehicle based on the cache track information. When the instantaneous motion state and the expected motion state are combined and analyzed, the predicted track information of the target vehicle can be accurately determined, and the track prediction accuracy is improved.
In one embodiment of the present application, the acquiring the instantaneous motion information of the target vehicle includes:
acquiring a preset number of image frames corresponding to the target vehicle after the target vehicle appears;
and determining the course angle, the longitudinal speed and the transverse speed of the target vehicle based on the image frame, and taking the course angle, the longitudinal speed and the transverse speed of the target vehicle as the instantaneous motion information.
According to the technical scheme, the method and the device can extract the preset number of image frames to analyze the steering angle, the longitudinal speed and the transverse speed when the target vehicle suddenly appears, so that the instantaneous motion state can be accurately analyzed in the subsequent steps.
In one embodiment of the present application, the determining the instantaneous motion state of the target vehicle based on the instantaneous motion information includes:
if the course angle is smaller than 45 degrees and the longitudinal speed is larger than a preset first threshold value, determining that the instantaneous motion state of the target vehicle is a forward running state;
if the course angle is smaller than 45 degrees and the longitudinal speed is smaller than the first threshold value, determining that the instantaneous motion state of the target vehicle is a backward running state;
if the course angle is larger than 45 degrees and the transverse speed is larger than a preset second threshold value, determining that the instantaneous motion state of the target vehicle is a left-to-right traversing state;
and if the course angle is larger than 45 degrees and the transverse speed is smaller than the second threshold value, determining that the instantaneous motion state of the target vehicle is a right-to-left traversing state.
According to the technical scheme, the instant motion state of the target vehicle just appears is accurately analyzed based on the course angle, the longitudinal speed and the transverse speed, so that the situation that no historical track cache exists when the vehicle just appears is made up, and the analysis accuracy of the instant motion state is improved.
In one embodiment of the present application, the obtaining the cache track information of the target vehicle includes:
Determining a lane center line, and determining a movement variation of the target vehicle relative to the lane center line based on the lane center line and an initial position of the target vehicle;
determining a lateral movement amount, a longitudinal movement amount, and an angle change amount of the target vehicle with respect to the own lane, the next lane, and the previous lane based on the movement change amount;
and taking the transverse moving amount, the longitudinal moving amount and the angle change amount as the cache track information.
According to the above technical solution, the present embodiment determines the amount of lateral movement, the amount of longitudinal movement, and the amount of angular change based on the amount of movement change of the target vehicle with respect to the lane center line, and uses the amount of lateral movement, the amount of longitudinal movement, and the amount of angular change as the buffer track information, so that the expected movement state of the target vehicle is analyzed based on the buffer track information in the subsequent step.
In one embodiment of the application, the determining the lane centerline includes:
acquiring curvature information of a lane line, and determining corner points of the lane line based on the curvature information;
and acquiring a midpoint between the two corner points, and determining the lane center line based on the midpoint.
According to the technical scheme, the lane center line can be accurately analyzed, so that the cache track information of the target vehicle can be accurately analyzed in the subsequent steps.
In one embodiment of the present application, the determining the expected motion state of the target vehicle based on the cached track information includes:
the lane boundaries on the left side and the right side are obtained, and the lane where the target vehicle is determined by combining the transverse movement amount, the longitudinal movement amount and the angle change amount;
if the lane where the target vehicle is located is the own lane, acquiring the front-back relative movement of the target vehicle relative to the own lane, and determining that the expected movement state is forward running or backward running;
and if the lane where the target vehicle is located is the next lane, acquiring the left-right relative movement of the target vehicle relative to the lane, and determining the expected movement state to be the left-right transverse movement or the right-left transverse movement.
According to the technical scheme, the expected motion state of the target vehicle can be accurately analyzed according to the transverse movement amount, the longitudinal movement amount and the angle change amount.
In one embodiment of the present application, the determining the predicted trajectory information of the target vehicle based on the instantaneous motion state and the expected motion state includes:
In one embodiment of the application, the method further comprises:
acquiring parking space information, and determining the position relation between the target vehicle and the parking space information and the angle change information between the target vehicle and the lane based on the parking space information;
and determining a parking motion state according to the position relation and the angle change information, and determining parking track information corresponding to the parking motion state, wherein the parking motion state comprises a parking state and a parking state.
According to the technical scheme, the parking track information can be analyzed based on the position relation between the target vehicle and the parking space information and the angle change information between the target vehicle and the own lane, so that the method and the device can be applied to more scenes.
In a second aspect, an embodiment of the present application further provides a vehicle track prediction apparatus, where the apparatus includes:
the instantaneous state determining module is used for acquiring instantaneous motion information of the target vehicle and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information;
the expected state determining module is used for acquiring cache track information of the target vehicle and determining an expected motion state of the target vehicle based on the cache track information;
And the track information determining module is used for determining the predicted track information of the target vehicle based on the instantaneous motion state and the expected motion state.
According to the technical scheme, the vehicle track prediction device can accurately analyze the instantaneous motion information of the target vehicle when the target vehicle just appears, further analyze the instantaneous motion state, and analyze the expected motion state of the target vehicle based on the cache track information. When the instantaneous motion state and the expected motion state are combined and analyzed, the predicted track information of the target vehicle can be accurately determined, and the track prediction accuracy is improved.
In one embodiment of the present application, the transient state determination module includes:
the image acquisition unit is used for acquiring a preset number of image frames corresponding to the target vehicle after the target vehicle appears;
and the image analysis unit is used for determining the course angle, the longitudinal speed and the transverse speed of the target vehicle based on the image frame and taking the course angle, the longitudinal speed and the transverse speed of the target vehicle as the instantaneous motion information.
In one embodiment of the present application, the transient state determination module includes:
The first instantaneous movement state analysis unit is used for determining that the instantaneous movement state of the target vehicle is a forward driving state if the course angle is smaller than 45 degrees and the longitudinal speed is larger than a preset first threshold value;
a second instantaneous motion state analysis unit, configured to determine that the instantaneous motion state of the target vehicle is a backward running state if the heading angle is less than 45 ° and the longitudinal speed is less than the first threshold;
the third instantaneous motion state analysis unit is used for determining that the instantaneous motion state of the target vehicle is a traversing state from left to right if the course angle is larger than 45 degrees and the transverse speed is larger than a preset second threshold value;
and the fourth instantaneous motion state analysis unit is used for determining that the instantaneous motion state of the target vehicle is a right-to-left traversing state if the course angle is larger than 45 degrees and the transverse speed is smaller than the second threshold value.
In one embodiment of the application, the expected state determining module comprises:
a center line determination unit configured to determine a lane center line, and determine a movement variation of the target vehicle with respect to the lane center line based on initial positions of the lane center line and the target vehicle;
A parameter determination unit configured to determine a lateral movement amount, a longitudinal movement amount, and an angle change amount of the target vehicle with respect to the own lane, the next lane, and the previous lane based on the movement change amount;
and the history track determining unit is used for taking the transverse moving amount, the longitudinal moving amount and the angle change amount as the cache track information.
In one embodiment of the present application, the center line determining unit includes:
the angular point determining subunit is used for acquiring curvature information of the lane lines and determining angular points of the lane lines based on the curvature information;
and the corner analysis subunit is used for acquiring a midpoint between the two corner points and determining the lane center line based on the midpoint.
In one embodiment of the application, the expected state determining module comprises:
the parameter analysis unit is used for acquiring lane boundaries at the left side and the right side, and determining a lane where the target vehicle is located by combining the transverse movement amount, the longitudinal movement amount and the angle change amount;
the first expected state determining unit is used for acquiring the front-back relative motion of the target vehicle relative to the own lane if the lane where the target vehicle is located is the own lane, and determining that the expected motion state is forward running or backward running;
And the second expected state determining unit is used for acquiring the left-right relative motion of the target vehicle relative to the lane if the lane where the target vehicle is positioned is the next lane, and determining that the expected motion state is the transverse motion from left to right or the transverse motion from right to left.
In one embodiment of the application, the apparatus further comprises:
the information acquisition module is used for acquiring parking space information and determining the position relation between the target vehicle and the parking space information and the angle change information between the target vehicle and the lane based on the parking space information;
the parking analysis module is used for determining a parking motion state according to the position relation and the angle change information and determining parking track information corresponding to the parking motion state, wherein the parking motion state comprises a parking state and a parking in state.
In a third aspect, an embodiment of the present application further provides a vehicle, where the vehicle includes a memory, a processor, and a vehicle track prediction program stored in the memory and capable of running on the processor, and when the processor executes the vehicle track prediction program, the processor implements the steps of the vehicle track prediction method in any one of the above schemes.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where a vehicle track prediction program is stored on the computer readable storage medium, and when the vehicle track prediction program is executed by a processor, the steps of the vehicle track prediction method in any one of the foregoing solutions are implemented.
The beneficial effects are that: compared with the prior art, the invention provides a vehicle track prediction method, which comprises the steps of firstly obtaining the instantaneous motion information of a target vehicle, and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information. And then, obtaining cache track information of the target vehicle, and determining the expected motion state of the target vehicle based on the cache track information. Finally, based on the instantaneous motion state and the expected motion state, predicted trajectory information of the target vehicle is determined. The invention can accurately analyze the instantaneous motion information of the target vehicle when the target vehicle just appears, further analyze the instantaneous motion state, and analyze the expected motion state of the target vehicle based on the buffer track information. When the instantaneous motion state and the expected motion state are combined and analyzed, the predicted track information of the target vehicle can be accurately determined, and the track prediction accuracy is improved.
Drawings
FIG. 1 is a flowchart of a specific embodiment of a vehicle trajectory prediction method provided by the present application;
FIG. 2 is a schematic view of corner points in the vehicle trajectory prediction method of the present application;
FIG. 3 is a schematic diagram illustrating a midpoint in a vehicle trajectory prediction method of the present application;
FIG. 4 is a schematic diagram of a method for predicting a vehicle track according to the present application;
FIG. 5 is a functional schematic of a vehicle trajectory prediction apparatus according to the present application;
fig. 6 is a schematic block diagram of a vehicle provided by the present application.
Detailed Description
In order to make the objects, technical solutions and effects of the present application clearer and more specific, the present application will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Based on the fact that a target just appears or suddenly appears and no history track buffer exists in the prior art, the future motion state of the vehicle cannot be accurately predicted, meanwhile, due to the fact that the running state of the vehicle is complicated, the motion state of the vehicle cannot be accurately defined through the motion track, and the like, the embodiment provides a vehicle track prediction method, and the prediction track information of the target vehicle can be accurately determined based on the method of the embodiment. In specific application, the embodiment firstly acquires the instantaneous motion information of the target vehicle, and determines the instantaneous motion state of the target vehicle based on the instantaneous motion information. And then, obtaining cache track information of the target vehicle, and determining the expected motion state of the target vehicle based on the cache track information. Finally, based on the instantaneous motion state and the expected motion state, predicted trajectory information of the target vehicle is determined. The embodiment can accurately analyze the instantaneous motion information of the target vehicle when the target vehicle just appears, further analyze the instantaneous motion state, and analyze the expected motion state of the target vehicle based on the cache track information. When the instantaneous motion state and the expected motion state are combined and analyzed, the predicted track information of the target vehicle can be accurately determined, and the track prediction accuracy is improved.
The vehicle track prediction method of the present embodiment may be applied to a control terminal, which may be a vehicle-mounted controller, such as a vehicle-mounted central control computer. Or, the control terminal may also be a mobile terminal of the user, such as a mobile phone, where the mobile terminal may be connected to the vehicle-mounted terminal, so as to receive data transmitted by the vehicle-mounted terminal and perform corresponding analysis processing. Specifically, as shown in fig. 1, the vehicle track prediction method of the present embodiment includes the steps of:
step S100, acquiring instantaneous motion information of a target vehicle, and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information.
In practice, there are vehicles that have just appeared, and these have no cached trajectory data, so that they cannot be accurately analyzed when predicting the trajectories of these vehicles. For this reason, the present embodiment can acquire the instantaneous motion information of the target vehicle, which is the motion information acquired when the target vehicle has just appeared, and the terminal device can determine the instantaneous motion state of the target vehicle based on the instantaneous motion information after acquiring the instantaneous motion information.
In particular application, the embodiment may collect an image of the target vehicle based on a vehicle-mounted front view camera or a radar sensor preset on the host vehicle when the target vehicle just appears, then determine a heading angle, a longitudinal speed and a lateral speed of the target vehicle based on a preset number of image frames (for example, 20 images just appearing) corresponding to the target vehicle just after the target vehicle appears, and take the heading angle, the longitudinal speed and the lateral speed of the target vehicle as the instantaneous motion information, so as to accurately analyze an instantaneous motion state in a subsequent step.
In one implementation, the present embodiment includes the following steps in determining the instantaneous motion state:
step S101, if the course angle is smaller than 45 degrees and the longitudinal speed is larger than a preset first threshold value, determining that the instantaneous motion state of the target vehicle is a forward running state;
step S102, if the course angle is smaller than 45 degrees and the longitudinal speed is smaller than the first threshold value, determining that the instantaneous motion state of the target vehicle is a backward running state;
step S103, if the course angle is larger than 45 degrees and the transverse speed is larger than a preset second threshold, determining that the instantaneous motion state of the target vehicle is a traversing state from left to right;
and step S104, if the course angle is larger than 45 degrees and the transverse speed is smaller than the second threshold value, determining that the instantaneous motion state of the target vehicle is a right-to-left traversing state.
Specifically, the embodiment compares the course angle in the instantaneous motion information with a preset angle after the instantaneous motion information is acquired. For example, the preset angle is 45 °, and if the heading angle is smaller than 45 °, the longitudinal speed of the target vehicle is compared with the preset first threshold value by considering that the target vehicle moves forward and backward. And if the longitudinal speed is greater than a preset first threshold value, determining that the instantaneous motion state of the target vehicle is a forward running state. If the longitudinal speed is less than the first threshold, determining that the instantaneous motion state of the target vehicle is a backward running state. And when the heading angle is greater than 45 ° and less than 90 °, the lateral speed of the target vehicle is compared with a preset second threshold value in consideration of the lateral movement of the target vehicle, and if the lateral speed is greater than the preset second threshold value, the instantaneous movement state of the target vehicle is determined to be a left-to-right traversing state. If the lateral speed is less than the second threshold, determining that the instantaneous motion state of the target vehicle is a right-to-left traversing state. According to the method and the device for analyzing the instantaneous motion state of the target vehicle, the instantaneous motion state of the target vehicle when the target vehicle just appears is accurately analyzed based on the course angle, the longitudinal speed and the transverse speed, so that the situation that no history track is cached when the target vehicle just appears is made up, and the analysis accuracy of the instantaneous motion state is improved.
Step 200, obtaining cache track information of the target vehicle, and determining an expected motion state of the target vehicle based on the cache track information.
For a target vehicle which exists for a long time or after the target vehicle runs for a long time, the embodiment can buffer the history track in real time, the terminal device can acquire buffer track information of the target vehicle when track prediction is carried out, and then the expected motion state of the target vehicle is determined based on the buffer track information.
In this embodiment, when the buffer track information is acquired, the terminal device may first determine the lane center line. The present embodiment first obtains curvature information of a lane line, divides the lane into a plurality of lanes, determines curvature change based on the curvature information, and uses a point of curvature change as a corner point of the lane line, as shown in fig. 2. Next, a midpoint between two corner points is calculated by indexing the corner points, and the midpoint is a midpoint of a middle line of each lane, as shown in fig. 3. Because most of all lanes in the garage are mutually perpendicular, the longest lane line is firstly indexed by the length of the connecting line of the two corner points, then the next lane line is obtained by taking the vertical line through the middle point, and the lane center line can be determined by analogy. After obtaining the lane center line, the present embodiment obtains a certain number of image frames, and then determines the movement variation of the target vehicle with respect to the lane center line based on the initial positions of the lane center line and the target vehicle. Then, based on the movement variation, the present embodiment can determine the lateral movement amount, the longitudinal movement amount, and the angle variation of the target vehicle with respect to the own lane, the next lane, and the previous lane. The previous lane is a lane before the vehicle changes to the own lane, and the next lane is a target lane for changing from the own lane to other lanes. In particular, the present embodiment may provide a variable that is move_counter. The variable is defined as the movement variation of the target vehicle relative to the lane center line during driving, and the calculation method only considers the transverse distance is that a distance difference is calculated according to each acquired frame image, if the distance difference is larger than a preset threshold value, the target vehicle moves towards the opposite direction of the lane, the move_up_counter is added with 1, and if the distance difference is smaller than 0 and smaller than the preset threshold value, the target vehicle moves towards the direction of the lane, and the move_down_counter is added with 1. If the moving distance of the target vehicle is within a certain threshold, the target vehicle is considered to be stationary in the transverse direction, the move_step_counter is added with 1, and if the target vehicle continuously moves to the lane or continuously moves to the opposite direction of the lane or continuously keeps stationary, the other two counters are set to 0. And respectively calculating the transverse movement amount, the longitudinal movement amount and the angle change amount of the target vehicle to the lane where the vehicle is located, namely the lane where the vehicle is located, the next lane and the previous lane by the counter, and taking the transverse movement amount, the longitudinal movement amount and the angle change amount as the cache track information so as to analyze the expected movement state of the target vehicle based on the cache track information in the subsequent step.
In one implementation, the present embodiment, when determining the expected motion state of the target vehicle, includes the steps of:
step S201, lane boundaries on the left side and the right side are obtained, and the lane where the target vehicle is determined by combining the transverse movement amount, the longitudinal movement amount and the angle change amount;
step S202, if the lane where the target vehicle is located is the own lane, acquiring the front-back relative movement of the target vehicle relative to the own lane, and determining that the expected movement state is forward running or backward running;
in step S203, if the lane in which the target vehicle is located is the next lane, the left-right relative movement of the target vehicle with respect to the own lane is acquired, and the expected movement state is determined to be the left-right traversing movement or the right-left traversing movement.
Specifically, the present embodiment first determines whether the target vehicle is in the own lane or in the next lane. Therefore, the present embodiment acquires lane boundaries on both the left and right sides, and determines the lane in which the target vehicle is located in combination with the lateral movement amount, the longitudinal movement amount, and the angle change amount. If the lane where the target vehicle is located is the own lane, the target vehicle of the own lane is considered to have forward and backward traveling. Therefore, the present embodiment acquires the forward-backward relative movement of the target vehicle with respect to the own lane, and determines that the expected movement state is forward running or backward running. Of course, it is also possible to consider that there is a lateral movement of the target vehicle with respect to the own lane, and therefore, the present embodiment may also acquire a lateral relative movement of the target vehicle with respect to the own lane and determine the expected movement state as a lateral movement from left to right or a lateral movement from right to left.
If the lane in which the target vehicle is located is the next lane, that is, only the state in which the target vehicle traverses left and right is considered, the present embodiment acquires the left and right relative movement of the target vehicle with respect to the own lane, and determines that the expected movement state is the traversing left to right movement or traversing right to left. According to the embodiment, the expected motion state of the target vehicle can be accurately analyzed according to the transverse movement amount, the longitudinal movement amount and the angle change amount.
Step S300, determining predicted track information of the target vehicle based on the instantaneous motion state and the expected motion state.
After the instantaneous motion state and the expected motion state are obtained, the embodiment can combine and analyze the instantaneous motion state and the expected motion state, because the instantaneous motion state reflects the motion state of the target vehicle when the target vehicle just appears and the motion state of the target vehicle when the target vehicle moves for a long time in the expected motion state, and the predicted track information of the target vehicle can be accurately determined by combining and analyzing the instantaneous motion state and the expected motion state. In particular, as shown in fig. 4, the terminal device may optimize information by using vehicle history information (i.e. buffer track information corresponding to the embodiment, and further analyze an expected motion state) and vehicle transient information (corresponding to the transient motion information in the embodiment), and then fuse the vehicle transverse intention to generate a future track, where the generated future track of the vehicle is the predicted track information of the embodiment.
In one implementation manner, since the instantaneous motion state of the present embodiment reflects the motion state of the target vehicle when the target vehicle just appears, the motion state of the target vehicle when the target vehicle moves for a long time when the motion state is expected, when the instantaneous motion state and the expected motion state are combined and analyzed, the motion state is predicted based on the instantaneous motion state and the expected motion state, that is, the motion state of the target vehicle is determined by combining the motion state of the target vehicle just appearing and the motion state after long time motion, and then the predicted track information of the motion state of the target is determined. For example, if the instantaneous motion state is the same as the expected motion state, it is indicated that the motion state of the target vehicle is not changed, so that the expected motion state may be the target motion state, and then the predicted track information may be determined based on the cache track information corresponding to the expected motion state. Therefore, the present embodiment combines the analysis of the instantaneous motion state and the expected motion state, and the instantaneous motion state is the motion state of the target vehicle just appearing and the expected motion state is the motion state of the target vehicle for a long time, so that the future motion track can be accurately predicted by combining the analysis of the instantaneous motion state and the expected motion state, thereby providing a favorable guidance for the decision and control of the automatic driving.
In another implementation manner, the terminal device of the present embodiment may further determine a parking state, where the parking motion state includes a parking state and a parking state. For the judgment of the parking state, in the embodiment, the situation that a parking space exists in a lane, the target is judged to move, and the angle of the target relative to the lane is continuously changed is considered, if the target comes out of the parking space, the parking state is the parking state, and if the target runs into the parking space, the parking state is the parking state. In specific application, the embodiment can acquire parking space information, and determine the position relationship between the target vehicle and the parking space information and the angle change information between the target vehicle and the lane based on the parking space information. And then, according to the position relation and the angle change information, determining the parking motion state and determining parking track information corresponding to the parking motion state, so that the embodiment is applied to more scenes.
Based on the above-described embodiments, the present invention also provides a vehicle trajectory prediction apparatus, as shown in fig. 5, the vehicle trajectory prediction apparatus 100 of the present embodiment includes: the transient state determination module 10, the expected state determination module 20, and the trajectory information determination module 30. Specifically, the transient state determining module 10 is configured to obtain transient motion information of a target vehicle, and determine a transient motion state of the target vehicle based on the transient motion information. The expected state determining module 20 is configured to obtain cache track information of the target vehicle, and determine an expected motion state of the target vehicle based on the cache track information. The track information determining module 30 is configured to determine predicted track information of the target vehicle based on the instantaneous motion state and the expected motion state.
In one embodiment of the present application, the transient state determination module 10 includes:
the image acquisition unit is used for acquiring a preset number of image frames corresponding to the target vehicle after the target vehicle appears;
and the image analysis unit is used for determining the course angle, the longitudinal speed and the transverse speed of the target vehicle based on the image frame and taking the course angle, the longitudinal speed and the transverse speed of the target vehicle as the instantaneous motion information.
In one embodiment of the present application, the transient state determination module 10 includes:
the first instantaneous movement state analysis unit is used for determining that the instantaneous movement state of the target vehicle is a forward driving state if the course angle is smaller than 45 degrees and the longitudinal speed is larger than a preset first threshold value;
a second instantaneous motion state analysis unit, configured to determine that the instantaneous motion state of the target vehicle is a backward running state if the heading angle is less than 45 ° and the longitudinal speed is less than the first threshold;
the third instantaneous motion state analysis unit is used for determining that the instantaneous motion state of the target vehicle is a traversing state from left to right if the course angle is larger than 45 degrees and the transverse speed is larger than a preset second threshold value;
And the fourth instantaneous motion state analysis unit is used for determining that the instantaneous motion state of the target vehicle is a right-to-left traversing state if the course angle is larger than 45 degrees and the transverse speed is larger than the second threshold value.
In one embodiment of the present application, the expected state determining module 20 includes:
a center line determination unit configured to determine a lane center line, and determine a movement variation of the target vehicle with respect to the lane center line based on initial positions of the lane center line and the target vehicle;
a parameter determination unit configured to determine a lateral movement amount, a longitudinal movement amount, and an angle change amount of the target vehicle with respect to the own lane, the next lane, and the previous lane based on the movement change amount;
and the history track determining unit is used for taking the transverse moving amount, the longitudinal moving amount and the angle change amount as the cache track information.
In one embodiment of the present application, the center line determining unit includes:
the angular point determining subunit is used for acquiring curvature information of the lane lines and determining angular points of the lane lines based on the curvature information;
and the corner analysis subunit is used for acquiring a midpoint between the two corner points and determining the lane center line based on the midpoint.
In one embodiment of the present application, the expected state determining module 20 includes:
the parameter analysis unit is used for acquiring lane boundaries at the left side and the right side, and determining a lane where the target vehicle is located by combining the transverse movement amount, the longitudinal movement amount and the angle change amount;
the first expected state determining unit is used for acquiring the front-back relative motion of the target vehicle relative to the own lane if the lane where the target vehicle is located is the own lane, and determining that the expected motion state is forward running or backward running;
and the second expected state determining unit is used for acquiring the left-right relative motion of the target vehicle relative to the lane if the lane where the target vehicle is positioned is the next lane, and determining that the expected motion state is the transverse motion from left to right or the transverse motion from right to left.
In one embodiment of the application, the apparatus further comprises:
the information acquisition module is used for acquiring parking space information and determining the position relation between the target vehicle and the parking space information and the angle change information between the target vehicle and the lane based on the parking space information;
The parking analysis module is used for determining a parking motion state according to the position relation and the angle change information and determining parking track information corresponding to the parking motion state, wherein the parking motion state comprises a parking state and a parking in state.
The working principle of each module in the vehicle track prediction apparatus 100 of the present embodiment is the same as that of each step in the above-described method embodiment, and will not be described here again.
The vehicle trajectory prediction apparatus 100 according to the present embodiment can accurately analyze the instantaneous motion information of the target vehicle at the time of the occurrence, and thus analyze the instantaneous motion state, and can also analyze the expected motion state of the target vehicle based on the cached trajectory information. When the instantaneous motion state and the expected motion state are combined and analyzed, the predicted track information of the target vehicle can be accurately determined, and the track prediction accuracy is improved.
Fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include: memory 501, processor 502, and a computer program stored on memory 501 and executable on processor 502. The processor 502 implements the vehicle track prediction method provided in the above embodiment when executing a program.
Further, the vehicle further includes:
a communication interface 503 for communication between the memory 501 and the processor 502.
Memory 501 for storing a computer program executable on processor 502.
The memory 501 may include high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 may be connected to each other via a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Periphera l Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
In a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 may perform communication with each other through internal interfaces. The processor 502 may be a central processing unit (Central Processing Unit, abbreviated as CPU) or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC) or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle trajectory prediction method as above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "particular embodiments," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (10)

1. A vehicle trajectory prediction method, characterized in that the method comprises:
acquiring instantaneous motion information of a target vehicle, and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information;
acquiring cache track information of the target vehicle, and determining an expected motion state of the target vehicle based on the cache track information;
based on the instantaneous motion state and the expected motion state, predicted trajectory information of the target vehicle is determined.
2. The vehicle trajectory prediction method according to claim 1, characterized in that the acquiring the instantaneous motion information of the target vehicle includes:
acquiring a preset number of image frames corresponding to the target vehicle after the target vehicle appears;
and determining the course angle, the longitudinal speed and the transverse speed of the target vehicle based on the image frame, and taking the course angle, the longitudinal speed and the transverse speed of the target vehicle as the instantaneous motion information.
3. The vehicle trajectory prediction method according to claim 2, characterized in that the determining the instantaneous motion state of the target vehicle based on the instantaneous motion information includes:
if the course angle is smaller than 45 degrees and the longitudinal speed is larger than a preset first threshold value, determining that the instantaneous motion state of the target vehicle is a forward running state;
if the course angle is smaller than 45 degrees and the longitudinal speed is smaller than the first threshold value, determining that the instantaneous motion state of the target vehicle is a backward running state;
if the course angle is larger than 45 degrees and the transverse speed is larger than a preset second threshold value, determining that the instantaneous motion state of the target vehicle is a left-to-right traversing state;
and if the course angle is larger than 45 degrees and the transverse speed is smaller than the second threshold value, determining that the instantaneous motion state of the target vehicle is a right-to-left traversing state.
4. The vehicle track prediction method according to claim 1, wherein the obtaining the cache track information of the target vehicle includes:
determining a lane center line, and determining a movement variation of the target vehicle relative to the lane center line based on the lane center line and an initial position of the target vehicle;
Determining a lateral movement amount, a longitudinal movement amount, and an angle change amount of the target vehicle with respect to the own lane, the next lane, and the previous lane based on the movement change amount;
and taking the transverse moving amount, the longitudinal moving amount and the angle change amount as the cache track information.
5. The vehicle trajectory prediction method according to claim 4, characterized in that the determining of the lane center line includes:
acquiring curvature information of a lane line, and determining corner points of the lane line based on the curvature information;
and acquiring a midpoint between the two corner points, and determining the lane center line based on the midpoint.
6. The vehicle trajectory prediction method according to claim 4, characterized in that the determining the expected motion state of the target vehicle based on the cached trajectory information includes:
the lane boundaries on the left side and the right side are obtained, and the lane where the target vehicle is determined by combining the transverse movement amount, the longitudinal movement amount and the angle change amount;
if the lane where the target vehicle is located is the own lane, acquiring the front-back relative movement of the target vehicle relative to the own lane, and determining that the expected movement state is forward running or backward running;
And if the lane where the target vehicle is located is the next lane, acquiring the left-right relative movement of the target vehicle relative to the lane, and determining the expected movement state to be the left-right transverse movement or the right-left transverse movement.
7. The vehicle trajectory prediction method according to claim 1, characterized in that the method further comprises:
acquiring parking space information, and determining the position relation between the target vehicle and the parking space information and the angle change information between the target vehicle and the lane based on the parking space information;
and determining a parking motion state according to the position relation and the angle change information, and determining parking track information corresponding to the parking motion state, wherein the parking motion state comprises a parking state and a parking state.
8. A vehicle trajectory prediction device, characterized in that the device comprises:
the instantaneous state determining module is used for acquiring instantaneous motion information of the target vehicle and determining the instantaneous motion state of the target vehicle based on the instantaneous motion information;
the expected state determining module is used for acquiring cache track information of the target vehicle and determining an expected motion state of the target vehicle based on the cache track information;
And the track information determining module is used for determining the predicted track information of the target vehicle based on the instantaneous motion state and the expected motion state.
9. A vehicle comprising a memory, a processor and a vehicle trajectory prediction program stored in the memory and operable on the processor, wherein the processor, when executing the vehicle trajectory prediction program, performs the steps of the vehicle trajectory prediction method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a vehicle trajectory prediction program, which, when executed by a processor, implements the steps of the vehicle trajectory prediction method according to any one of claims 1-7.
CN202310933449.2A 2023-07-27 2023-07-27 Vehicle track prediction method and device, vehicle and storage medium Pending CN116923398A (en)

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CN202310933449.2A CN116923398A (en) 2023-07-27 2023-07-27 Vehicle track prediction method and device, vehicle and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310933449.2A CN116923398A (en) 2023-07-27 2023-07-27 Vehicle track prediction method and device, vehicle and storage medium

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