Patent Description:
At present, traffic accidents caused by vehicle lane changes are very common in urban roads. Therefore, for self-driving, it is also very important to accurately recognize a lane change trend of a vehicle around a current vehicle.

In some self-driving vehicles, when a lane change trend of a surrounding target vehicle is determined, a laser radar technology is usually used to obtain laser point cloud data of a surrounding scene, detect a contour vertex of the target vehicle, and obtain a transverse distance between the contour vertex and the current vehicle. Further, the lane change trend of the target vehicle is determined based on a change of the transverse distance with time.

In addition, in some self-driving vehicles, when a lane change trend of a surrounding target vehicle is determined, a machine vision technology is used to obtain a surrounding scene image, detect a lane line in which the current vehicle is located and the target vehicle in the scene image, and obtain a distance between the target vehicle and the lane line in which the current vehicle is located. Further, the lane change trend of the target vehicle is determined based on a change of the distance with time.

In a process of implementing this application, the inventor finds that the related technology has at least the following problems:
When the lane change trend of the target vehicle is recognized by using the laser radar technology, contour vertex detection of the target vehicle is inaccurate due to exhaust gas of the target vehicle, dust, and the like. As a result, the lane change trend of the target vehicle is inaccurately determined. When the lane change trend of the target vehicle is recognized by using the machine vision technology, there may be no lane line in an intersection area. Consequently, the lane change trend of the target vehicle cannot be recognized. <CIT> discloses a radar-guided vision system for detecting the presence of a target vehicle in front of a host vehicle in the same lane of traffic and for determining whether the target vehicle intends to change lanes. <CIT> discloses a method for identifying a lane-changing condition of an object vehicle.

Embodiments of this application provide a method and an apparatus for recognizing a vehicle lane change trend, to resolve a problem in a related technology that a vehicle lane change trend cannot be accurately recognized by using only a laser radar technology or a machine vision technology. Technical solutions are as follows:.

According to a first aspect, a method for recognizing a vehicle lane change trend is defined in the appended claim <NUM>.

According to a second aspect, an apparatus for recognizing a vehicle lane change trend is defined in the appended claim <NUM>.

According to a third aspect, a computer-readable storage medium is provided. The storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement operations performed by using the method for recognizing the vehicle lane change trend according to the first aspect.

An embodiment of this application provides a method for recognizing a vehicle lane change trend. The method may be applied to a self-driving vehicle. The method may be implemented by a device for recognizing a vehicle lane change trend in the self-driving vehicle. A sensing system, a positioning system, and the like may be deployed in the self-driving vehicle. The sensing system may include a laser radar, a camera, and the like. The positioning system may be a global positioning system (Global Positioning System, GPS), a BeiDou system, or the like.

<FIG> is a schematic diagram of a device <NUM> for recognizing a vehicle lane change trend according to an embodiment of this application. In <FIG>, the device for recognizing a vehicle lane change trend may include a processor <NUM> and a memory <NUM>. The processor <NUM> may be a central processing unit (Central Processing Unit, CPU). The processor <NUM> may be one processor, or may include a plurality of processors. The memory <NUM> may include a volatile memory, for example, a random access memory (random access memory, RAM). Alternatively, the memory may include a nonvolatile memory, for example, a read-only memory (read-only memory, ROM) or a flash memory. The memory may alternatively include a combination of the foregoing types of memories. The memory <NUM> may be one memory or may include a plurality of memories. The memory <NUM> stores a computer-readable instruction, and the computer-readable instruction may be executed by the processor <NUM>, to implement the method for recognizing a vehicle lane change trend provided in the embodiments of this application.

<FIG> is a flowchart of a method for recognizing a vehicle lane change trend according to an embodiment of this application. A procedure of the method includes the following steps.

Step <NUM>: Obtain laser point cloud data of a detected target vehicle.

The target vehicle is a vehicle traveling in a scene around a current vehicle.

In implementation, a laser radar may be installed on the current vehicle, and the laser radar scans the surrounding scene at a fixed frequency to obtain laser point cloud data. When the laser point cloud data of the detected target vehicle is obtained, the laser point cloud data of the detected target vehicle may not be obtained in each frame. Instead, the laser point cloud data of the detected target vehicle is obtained based on a detection period. In one detection period, the laser radar may collect one or more frames of laser point cloud data of the detected target vehicle. In each detection period, a last frame of laser point cloud data that is of the detected target vehicle and that is obtained in the current detection period may be obtained.

Step <NUM>: Obtain, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle.

In implementation, for the obtained laser point cloud data, the target vehicle may be recognized, and coordinates of a laser point corresponding to a contour line vertex of the target vehicle in a laser radar coordinate system are obtained. Then, the coordinates of the contour line vertex of the target vehicle in the laser radar coordinate system are converted into coordinates of the contour line vertex of the target vehicle in a self-vehicle coordinate system based on a transformation matrix from the laser radar coordinate system obtained through pre-calibration to the self-vehicle coordinate system, to obtain first coordinates of the target vehicle in the self-vehicle coordinate system. The obtained coordinates of the target vehicle in the laser radar coordinate system may be the coordinates of the contour line vertex of the target vehicle in the laser radar coordinate system. Correspondingly, the converted first coordinates in the self-vehicle coordinate system may include coordinates of the contour line vertex of the target vehicle in the self-vehicle coordinate system. Herein, different shapes of the target vehicle may accordingly correspond to different quantities of contour line vertices.

Then, the first coordinates of the target vehicle in the self-vehicle coordinate system may be converted into coordinates of the target vehicle in a world coordinate system, to obtain second coordinates of the target vehicle in the world coordinate system. In other words, coordinates of each contour line vertex of the target vehicle in the self-vehicle coordinate system are converted into coordinates of the contour line vertex of the target vehicle in the world coordinate system. A method for converting the coordinates of each contour line vertex of the target vehicle from the self-vehicle coordinate system to the world coordinate system may be as follows:.

Timestamp alignment is performed on the laser radar and a positioning system. Coordinates (x<NUM>, y<NUM>) and an orientation angle θ<NUM> of the current vehicle in the world coordinate system are obtained by using the positioning system. Then, the first coordinates of the target vehicle in the self-vehicle coordinate system are converted according to the following formula (<NUM>): <MAT>.

xi, yi is a horizontal coordinate and a vertical coordinate of the ith contour line vertex of the target vehicle in the self-vehicle coordinate system of the current vehicle, and xi', yi' is a horizontal coordinate and a vertical coordinate of the ith contour line vertex of the target vehicle in the world coordinate system. <MAT> is a rotation matrix from the self-vehicle coordinate system to the world coordinate system, and the rotation matrix may be represented as follows: <MAT>.

After the second coordinates of the target vehicle in the world coordinate system are obtained, a center line point set of the lane in which the current vehicle is located may be obtained from a high-definition map. Herein, the obtained center line point set may be a center line point set that meets a preset distance condition from the current vehicle, for example, a center line point set of the lane in which the current vehicle is located within a range of <NUM> meters of the current vehicle.

Then, distances between the second coordinates of the target vehicle in the world coordinate system and coordinates of sampling points in the obtained center line point set of the lane in which the current vehicle is located on the high-definition map may be calculated, and a minimum distance is selected as the distance between the target vehicle and the center line of the lane in which the current vehicle is located. In other words, distances between the coordinates of the contour line vertices of the target vehicle in the world coordinate system and the coordinates of the sampling points in the obtained center line point set of the lane in which the current vehicle is located on the high-definition map are calculated, and a minimum distance is selected as the distance between the target vehicle and the center line of the lane in which the current vehicle is located. Herein, the distance between the target vehicle and the center line of the lane in which the current vehicle is located may be denoted as d<NUM>.

Then, a ratio of the distance d<NUM> between the target vehicle and the center line of the lane in which the current vehicle is located to a width D<NUM> of the lane in which the current vehicle is located may be calculated, and the ratio is used as the first distance relationship value γl between the target vehicle and the center line of the lane in which the current vehicle is located. That is, <MAT>. Herein, the width D<NUM> of the lane in which the current vehicle is located may be obtained by using the high-definition map.

In addition, it should be further noted that a distance relationship value is used to indicate a distance relationship between another vehicle and the center line of the lane in which the current vehicle is located. In addition to a ratio of a distance between the another vehicle and the center line of the lane in which the current vehicle is located to the width of the lane in which the current vehicle is located, the distance relationship value may also be represented by a reciprocal of the ratio.

Step <NUM>: Obtain a scene image including the target vehicle.

In implementation, a machine vision device, such as a camera, may be installed on the current vehicle. The machine vision device photographs a surrounding scene at a fixed frequency to obtain the scene image. When the scene image including the target vehicle is obtained, each frame of scene image including the target vehicle may not be obtained, but the laser point cloud data including the target vehicle is obtained based on the detection period. In one detection period, the machine vision device may obtain one or more frames of scene images including the target vehicle. In a detection period, a last frame of scene image that includes the target vehicle and that is obtained in the current detection period may be obtained.

Step <NUM>: Obtain, based on the scene image, a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle.

In implementation, lane line recognition and target vehicle recognition may be performed on the obtained scene image including the target vehicle. When target vehicle recognition is performed, a bounding box (bounding box) corresponding to the target vehicle may be generated, and coordinates of two vertices that are in the bounding box of the target vehicle and that are close to the current vehicle and in contact with the ground in an image coordinate system are obtained. As shown in <FIG>, the image coordinate system may use an upper left corner of the image as an origin. Coordinates of a vertex <NUM> that is in the bounding box of the target vehicle and that is close to the current vehicle and in contact with the ground may be denoted as (xv<NUM>, yv<NUM>). Coordinates of a vertex <NUM> that is in the bounding box of the target vehicle and that is close to the current vehicle and in contact with the ground may be denoted as (xv<NUM>, yv<NUM>).

In the image coordinate system shown in <FIG>, a point A<NUM> that is on a left lane line of the lane in which the current vehicle is located and that has a same vertical coordinate as the vertex <NUM> and a point B<NUM> that is on a right lane line and that has a same vertical coordinate as the vertex <NUM> are separately obtained. Coordinates of A<NUM> are (xl<NUM>, yv<NUM>), and coordinates of B<NUM> are (xr<NUM>, yv<NUM>). Similarly, a point A<NUM> that is on the left lane line of the lane in which the current vehicle is located and that has a same vertical coordinate as the vertex <NUM> and a point B<NUM> that is on the lane line and that has a same vertical coordinate as the vertex <NUM> are obtained. Coordinates of A<NUM> are (xl<NUM>, yv<NUM>) , and coordinates of B<NUM> are (xr<NUM>, yv<NUM>). A location relationship among the vertex <NUM>, A<NUM>, B<NUM>, the vertex <NUM>, A<NUM>, and B<NUM> may be shown in <FIG>. An image coordinate system shown in <FIG> is the same as the image coordinate system shown in <FIG>. For the vertex <NUM> and the vertex <NUM>, distance relationship values between the corresponding vertices and the center line of the lane in which the current vehicle is located may be separately calculated, and a smaller value obtained through calculation is used as the second distance relationship value between the target vehicle and the center line of the lane in which the current vehicle is located.

A distance relationship value between the vertex <NUM> and the center line of the lane in which the current vehicle is located may be calculated as follows:.

First, a point C<NUM> that is on the center line of the lane and that corresponds to the vertex <NUM> is obtained, coordinates of C<NUM> are (xc<NUM>, yv<NUM>) , and xc<NUM> may be calculated according to the following formula (<NUM>): <MAT>.

Then, the distance relationship value between the vertex <NUM> and the center line of the lane in which the current vehicle is located may be calculated. A calculation method may be the following formula (<NUM>): <MAT>.

Finally, a smaller value between γv<NUM> and γv<NUM> is used as the second distance relationship value between the target vehicle and the center line of the lane in which the current vehicle is located.

Step <NUM>: Calculate first confidence of a plurality of obtained first distance relationship values and second confidence of a plurality of obtained second distance relationship values.

In implementation, when the plurality of first distance relationship values and the plurality of second distance relationship values are obtained, the first confidence of the first distance relationship values and the second confidence of the second distance relationship values may be calculated. When the first distance relationship values and the second distance relationship values are obtained based on same detection periods, the confidence of the first distance relationship values and the confidence of the second distance relationship values may be calculated after M detection periods, and the confidence of the first distance relationship values and the confidence of the second distance relationship values may be recalculated in each subsequent detection period. M is a preset positive integer, and may be set according to an actual requirement, for example, may be set to <NUM>, <NUM>, or the like.

When the first confidence of the plurality of obtained first distance relationship values and the second confidence of the plurality of obtained second distance relationship values are calculated, an ideal lane change model and an ideal lane keep model may be used for calculation. The ideal lane change model is used to represent a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane. The ideal lane keep model is used to represent a time-varying relationship of a distance relationship value between the another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane. The following describes a method for calculating, based on the ideal lane change model and the ideal lane keep model, the first confidence of the plurality of obtained first distance relationship values and the second confidence of the plurality of obtained second distance relationship values.

First, a first available lane change model and a first available lane keep model are obtained based on the plurality of obtained first distance relationship values, the ideal lane change model, and the ideal lane keep model. A second available lane change model and a second available lane keep model are obtained based on the plurality of obtained second distance relationship values, the ideal lane change model, and the ideal lane keep model.

It should be noted that the plurality of obtained first distance relationship values and the plurality of obtained second distance relationship values may be first distance relationship values and second distance relationship values obtained in a first preset quantity of consecutive detection periods including a current detection period. The first preset quantity may be set according to an actual requirement. For example, the first preset quantity may be the same as a value of M, and is set to <NUM>, <NUM>, or the like.

A method for obtaining the second available lane change model is the same as a method for obtaining the first available lane change model, and a method for obtaining the second available lane keep model is the same as a method for obtaining the first available lane keep model. The following uses the method for obtaining the first available lane change model and the method for obtaining the first available lane keep model as examples for description.

The ideal lane change model may be represented by the following relational expression (<NUM>): <MAT>.

qLC(xi) is a predicted distance relationship value. α<NUM> , α<NUM> and α<NUM> are to-be-determined unknown parameters.

The ideal lane keep model may be represented by the following relational expression (<NUM>): <MAT>.

α<NUM> is a to-be-determined unknown parameter, and qLK(xi) is a predicted distance relationship value.

For a method for calculating the to-be-determined unknown parameters α<NUM>, α<NUM> and α<NUM> in the ideal lane change model based on the plurality of obtained first distance relationship values, the calculation method may be the following formula (<NUM>): <MAT>.

N is a quantity of obtained first distance relationship values, and xi may be a sorting value corresponding to the ith obtained first distance relationship value in the plurality of obtained first distance relationship values. For example, x<NUM> is a sorting value corresponding to the first obtained first distance relationship value in the plurality of obtained first distance relationship values, x<NUM> = <NUM>, and so on. p(xi) is the ith obtained first distance relationship value in the plurality of obtained first distance relationship values. The plurality of obtained first distance relationship values used to determine the to-be-determined unknown parameters in the ideal lane change model and sorting values respectively corresponding to the plurality of obtained first distance relationship values are substituted into the foregoing formula (<NUM>), and values of the unknown parameters α<NUM>, α<NUM>, and α<NUM> are adjusted. When an accumulation result of the foregoing formula (<NUM>) is the smallest, values of α<NUM>, α<NUM>, and α<NUM> are obtained, for example, α<NUM> = α<NUM>, α<NUM> = α<NUM> , and α<NUM> = α<NUM>, and α<NUM>, α<NUM>, and α<NUM> are substituted into the foregoing formula (<NUM>), to obtain the first available lane change model, as shown in the following formula (<NUM>): <MAT>.

For a method for determining the to-be-determined unknown parameter α<NUM> in the ideal lane keep model based on the plurality of first distance relationship values, the method may be the following formula (<NUM>): <MAT>.

The plurality of obtained first distance relationship values and the sorting values respectively corresponding to the plurality of first distance relationship values are substituted into the foregoing formula (<NUM>), and a value of the unknown parameter α<NUM> is adjusted. When an accumulation result of the foregoing formula (<NUM>) is the smallest, a value of α<NUM> is obtained, for example, α<NUM> = α<NUM>. The value of α<NUM> is substituted into the foregoing formula (<NUM>), to obtain the first available lane keep model, as shown in the following formula (<NUM>): <MAT>.

Similarly, the second available lane change model and the second available lane keep model are obtained based on the plurality of obtained second distance relationship values, the ideal lane change model, and the ideal lane keep model. The second available lane change model may be obtained according to the following formula (<NUM>): <MAT>.

The second available lane keep model may be obtained according to the following formula (<NUM>): <MAT>.

Then, after the first available lane change model, the first lane keep model, the second available lane change model, and the second lane keep model are obtained, a first fitting degree of the plurality of obtained first distance relationship values to the first available lane change model and a second fitting degree of the plurality of obtained first distance relationship values to the first lane keep model may be separately calculated, and a third fitting degree of the plurality of obtained second distance relationship values to the second available lane change model and a fourth fitting degree of the plurality of obtained second distance relationship values to the second lane keep model may be calculated.

The first fitting degree of the plurality of obtained first distance relationship values to the first available lane change model may be calculated by using the following formula (<NUM>): <MAT>.

The second fitting degree of the plurality of obtained first distance relationship values to the first available lane keep model may be calculated by using the following formula (<NUM>): <MAT>.

The third fitting degree of the plurality of obtained second distance relationship values to the second available lane change model may be calculated by using the following formula (<NUM>): <MAT>.

The fourth fitting degree of the plurality of obtained second distance relationship values to the second available lane keep model may be calculated by using the following formula (<NUM>): <MAT>.

Finally, a reciprocal of a smaller value between the first fitting degree and the second fitting degree is calculated, and is used as the first confidence T<NUM> of the obtained first distance relationship values. A reciprocal of a smaller value between the third fitting degree and the fourth fitting degree is calculated, and is used as the first confidence T<NUM> of the obtained second distance relationship values.

Step <NUM>: Calculate, based on the first confidence and the second confidence, a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of second distance relationship values.

In implementation, for the plurality of obtained first distance relationship values and the plurality of obtained second distance relationship values, a target first distance relationship value and a target second distance relationship value that are obtained in a same detection period may be obtained. A product of the target first distance relationship value and a first weight is added to a product of the target second distance relationship value and a second weight, to obtain a fusion distance relationship value corresponding to the detection period to which the target first distance relationship value and the target second relationship value belong. In this way, corresponding fusion distance values may be obtained for detection periods to which the plurality of obtained first distance relationship values and the plurality of obtained second distance relationship values belong.

The first weights W<NUM> corresponding to the plurality of obtained first distance relationship values may be calculated according to the following formula (<NUM>): <MAT>.

The second weights W<NUM> corresponding to the plurality of obtained second distance relationship values may be calculated according to the following formula (<NUM>): <MAT>.

Then, a target first distance relationship value and a target second distance relationship value that are obtained in a same detection period are sequentially obtained from the plurality of obtained first distance relationship values and the plurality of obtained second distance relationship values. A product of a target first distance relationship value γli obtained in the ith detection period and a first weight W<NUM> is added to a product of a target second distance relationship value γvi obtained in the ith detection period and a second weight W<NUM>, to obtain a fusion distance relationship value γfi corresponding to the ith detection period, where i is a detection period to which the currently obtained target first distance relationship value and the target second distance relationship value belong; and γfi is a sorting value in each of the plurality of detection periods to which the plurality of obtained first distance relationship values and the plurality of obtained second distance relationship values belong.

A calculation formula may be the following formula (<NUM>): <MAT>.

Step <NUM>: Determine, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend.

In implementation, the fusion distance relationship value corresponding to each of the plurality of detection periods and the corresponding sorting value are substituted into the foregoing formula (<NUM>). A sorting value corresponding to the fusion distance value may be represented by a sorting value of a detection period in which the fusion distance value is obtained through calculation in the plurality of detection periods. The sorting value corresponding to the fusion distance value is substituted into xi in the foregoing formula (<NUM>), and the fusion distance relationship value is substituted into p(xi) in the foregoing formula (<NUM>). Values of the unknown parameters α<NUM>, α<NUM>, and α<NUM> are adjusted. When an accumulation result of the foregoing formula (<NUM>) is the smallest, values of α<NUM>, α<NUM>, and α<NUM> are obtained, for example, α<NUM> = b<NUM>, α<NUM> = b<NUM>, and α<NUM> = b<NUM>. The values of α<NUM>, α<NUM>, and α<NUM> are substituted into the foregoing formula (<NUM>), to obtain a third available lane change model, as shown in the following formula (<NUM>): <MAT>.

Then, a fitting degree of the fusion distance relationship values corresponding to the plurality of detection periods to the third available lane change model may be calculated. A calculation method may be the following formula (<NUM>). The sorting value corresponding to the fusion distance value is substituted into xi in the formula (<NUM>), and the fusion distance relationship value is substituted into p(xi) in the formula (<NUM>).

N is a quantity of fusion distance values used to determine whether the target vehicle has a lane change trend.

Finally, it is determined whether the calculated fitting degree DLC<NUM> is greater than the preset fitting degree. If the calculated fitting degree DLC<NUM> is greater than the preset fitting degree, it may be determined that the target vehicle has the lane change trend.

In this embodiment of this application, the lane change trend of the target vehicle is comprehensively determined with reference to the laser point cloud data obtained by using the laser radar technology and the scene image obtained by using the machine vision technology. This can effectively avoid a problem of inaccurate recognition of the vehicle lane change trend that is caused by using only the laser radar technology or the machine vision technology.

Based on a same technical concept, an embodiment of this application further provides an apparatus for recognizing a vehicle lane change trend, and the apparatus may be applied to a device for recognizing a vehicle lane change trend. As shown in <FIG>, the apparatus for recognizing the vehicle lane change trend includes the following modules.

An obtaining module <NUM> is configured to: obtain laser point cloud data of a detected target vehicle, where the target vehicle is a vehicle traveling in a scene around the current vehicle; obtain, based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle; obtain a scene image including the target vehicle; and obtain, based on the scene image, a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle. Specifically, the obtaining function in the foregoing steps <NUM> to <NUM> and other implicit steps may be implemented.

A calculation module <NUM> is configured to calculate first confidence of a plurality of obtained first distance relationship values and second confidence of a plurality of obtained second distance relationship values. Specifically, a calculation function in step <NUM> and other implicit steps may be implemented.

A fusion module <NUM> is configured to calculate a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of second distance relationship values based on the first confidence and the second confidence. Specifically, a fusion function in step <NUM> and other implicit steps may be implemented.

A determining module <NUM> is configured to determine, based on the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend. Specifically, a determining function in step <NUM> and other implicit steps may be implemented.

In a possible implementation, the obtaining module <NUM> is configured to:.

In a possible implementation, the calculation module <NUM> is configured to:
calculate, based on an ideal lane change model and an ideal lane keep model, the first confidence of the plurality of obtained first distance relationship values and the second confidence of the plurality of obtained second distance relationship values, where the ideal lane change model is used to represent a time-varying relationship of a distance relationship value between another vehicle in the scene around the current vehicle and the center line of the lane in which the current vehicle is located when the another vehicle changes a lane; and the ideal lane keep model is used to represent a time-varying relationship of a distance relationship value between the another vehicle and the center line of the lane in which the current vehicle is located when the another vehicle moves along the lane.

In a possible implementation, the calculation module <NUM> is configured to:.

In a possible implementation, the calculation module <NUM> is configured to:
determine a reciprocal of a smaller value between the first fitting degree and the second fitting degree as the first confidence of the plurality of obtained first distance relationship values.

Obtaining the second confidence of the plurality of obtained second distance relationship values based on the third fitting degree and the fourth fitting degree includes:
determining a reciprocal of a smaller value between the third fitting degree and the fourth fitting degree as the second confidence of the plurality of obtained second distance relationship values.

The fusion module <NUM> is configured to:.

In a possible implementation, the determining module <NUM> is configured to:.

It should be noted that, when the apparatus for recognizing the vehicle lane change trend provided in the foregoing embodiment recognizes the vehicle lane change trend, division of the foregoing functional modules is merely used as an example for description. In actual application, the foregoing functions may be allocated to different functional modules and implemented according to a requirement. In other words, an internal structure of the device for recognizing the vehicle lane change trend may be divided into different functional modules to implement all or some of the functions described above. In addition, the apparatus for recognizing a vehicle lane change trend provided in the foregoing embodiment and the method embodiment for recognizing a vehicle lane change trend belong to a same concept. For a specific implementation process, refer to the method embodiment.

In the foregoing embodiment, all or part of the software, hardware, firmware, or any combination thereof may be implemented. When the software is used for implementation, all or part of the implementation may be implemented in a form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a device, the procedures or functions are all or partially generated according to embodiments of this application. The computer instructions may be stored in a computer-readable storage medium or may be transmitted from a computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired (for example, a coaxial optical cable, an optical fiber, or a digital subscriber line) or wireless (for example, infrared, radio, or microwave) manner. The computer-readable storage medium may be any usable medium accessible by a device, or a data storage device, such as a server or a data center, integrating one or more usable media. The usable media may be a magnetic medium (for example, a floppy disk, a hard disk drive, a magnetic tape, or the like), an optical medium (for example, a digital video disk (Digital Video Disk, DVD), or the like), a semiconductor medium (for example, a solid-state drive, or the like).

A person of ordinary skill in the art may understand that all or some of the steps of the embodiments may be implemented by hardware or a program instructing related hardware. The program may be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, an optical disc, or the like.

Claim 1:
A method for recognizing a vehicle lane change trend, the method comprising:
obtaining, in a plurality of consecutive detection periods, a plurality of obtained first distance relationship values and a plurality of obtained second distance relationship values,
wherein each first distance relationship value is obtained by, based on a respective detection period of said plurality of consecutive detection periods:
obtaining (<NUM>) laser point cloud data of a detected target vehicle, wherein the target vehicle is a vehicle traveling in a scene around the current vehicle;
obtaining (<NUM>), based on the laser point cloud data, a first distance relationship value between a center line of a lane in which the current vehicle is located and the target vehicle;
wherein each second distance relationship value is obtained by, based on a respective detection period of said plurality of consecutive detection periods:
obtaining a scene image (<NUM>) comprising the target vehicle;
obtaining (<NUM>), based on the scene image, a second distance relationship value between the center line of the lane in which the current vehicle is located and the target vehicle;
calculating (<NUM>) a first confidence of said plurality of obtained first distance relationship values and a second confidence of said plurality of obtained second distance relationship values;
calculating (<NUM>) a plurality of fusion distance relationship values of the plurality of first distance relationship values and the plurality of second distance relationship values based on the first confidence and the second confidence; and
determining (<NUM>), based on a time-varying relationship of the plurality of fusion distance relationship values, whether the target vehicle has a lane change trend.