Patent Description:
With the development of economy, increasing vehicles are traveling on roads, and traffic congestion phenomena are increasingly severe. As a result, driving situations on the roads become increasingly complex. In a driving process, a driver needs to pay much attention to a distance between a vehicle and another vehicle for a long time, to avoid a collision between the vehicle and the another vehicle.

To improve driving safety, the prior art provides an automatic vehicle following system. The automatic vehicle following system tracks a vehicle ahead based on vehicle-mounted radar and a vehicle-mounted camera, to control a velocity of a vehicle, so that the vehicle keeps a specific safe distance from the vehicle ahead.

However, in a road congestion scenario and a night scenario in which a traffic condition is indefinite, tracking results obtained by the vehicle-mounted radar and the vehicle-mounted camera through detection are greatly affected due to frequent flows of other vehicles, non-motor vehicles, or pedestrians, and a dramatic change in a light condition. Consequently, a tracking target is unstable or even lost in this case. As a result, it is impossible to effectively travel following the vehicle, increasing a risk of driving.

<CIT> provides a method, an apparatus, and a program of predicting an obstacle course, capable of appropriately predicting a course of an obstacle even under a complicated traffic environment.

Embodiments of the present invention provide a driving road estimation method and a driving road estimation system that can improve safe driving.

A first aspect of the embodiments of the present invention provides a method for predicting a road occupancy map of a target moving object according to appended claim <NUM>.

According to this aspect, even in a case of a severe traffic condition, such as a road congestion scenario and a night scenario in which a traffic condition is indefinite, the vehicle needs to obtain only the target status information of the target moving object at the current moment; and in a process of obtaining the target status information, the vehicle can estimate, based on the target status information, the road region in which the vehicle travels at the subsequent moment, without relying on a surrounding environment. Because the region in which the vehicle travels at the subsequent moment and the region in which the target moving object travels at the subsequent moment that are determined by the vehicle do not overlap, the vehicle can safely travel in the estimated road region. This effectively improves driving safety and reliability of the vehicle.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, step B specifically includes the following steps.

Step B1: The vehicle sets that the target status information satisfies a target Gaussian distribution <MAT>.

An expectation of the target Gaussian distribution is the lateral acceleration ax1, a variance of the target Gaussian distribution is <MAT>, and the variance <MAT> represents a discrete degree of the target Gaussian distribution <MAT>; and the target status information satisfying the target Gaussian distribution <MAT> is discretized into <NUM>(k +<NUM>) intervals, where k is a preset discretization coefficient.

Specifically, k in this embodiment may be inversely proportional to resolution f of a map stored in the vehicle, that is, k∝<NUM>/f, and the resolution f of the map is measured by using meters.

Step B2: The vehicle performs sampling in a target interval to obtain a target discrete value.

The target interval is any interval included in the <NUM>( k+<NUM>) intervals formed by a bell-shaped region that is obtained after the target status information is discretized.

Step B3: The vehicle obtains a target probability value corresponding to the target discrete value.

The target probability value that is corresponding to the target discrete value and that is obtained by the vehicle is equal to an area of the target interval.

Step B4: The vehicle sets the target probability value in the parameter sequence.

Specifically, the probability value obtained by the vehicle is corresponding to the target discrete value and the target discrete value.

According to this implementation, the vehicle may perform discretization processing on the lateral acceleration of the moving object in a probability discretization processing manner that is based on the Gaussian distribution, to obtain the parameter sequence including a plurality of discrete values, so as to effectively reduce calculation difficulty in estimating the parameter sequence by the vehicle, and increase a speed of calculating the target probability value. This effectively improves efficiency of estimating, by the vehicle, the road region in which the target moving object travels at the subsequent moment.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, a specific execution process of step B3 includes the following step.

Step B31: The vehicle determines, according to a first formula, the target probability value Pi corresponding to the target discrete value.

Specifically, the target status information further includes the longitudinal acceleration ay1 of the target moving object, and the target interval is an ith interval in the <NUM>( k+<NUM>) intervals. In this case, when the vehicle determines that i is any integer greater than <NUM> and less than or equal to k, the vehicle determines, according to the first formula, the target probability value Pi corresponding to the target discrete value, where the first formula is: <MAT>, where an integral interval of the first formula is [ax1 +i×δax1, ax1 +(i-<NUM>)×δax1], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

Alternatively, the specific execution process of step B3 includes step B32: The vehicle determines, according to a second formula, the target probability value Pi corresponding to the target discrete value.

Specifically, if the vehicle determines that i is any integer greater than -k and less than <NUM>, the vehicle may determine, according to the second formula, the target probability value Pi corresponding to the target discrete value, where the second formula is:<MAT>, where an integral interval of the second formula is [ax1 +(i +<NUM>)×δax1, ax1 + i ×δax1], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

Alternatively, the specific execution process of step B3 includes step B33: The vehicle determines, according to a third formula, the target probability value Pi corresponding to the target discrete value.

Specifically, if the vehicle determines that i is equal to -k-<NUM>, the vehicle may determine, according to the third formula, the target probability value Pi corresponding to the target discrete value, where the third formula is:<MAT>, where <MAT>, amax represents a maximum acceleration of the target moving object, an integral interval of the third formula is [ax1 -k×δax1,axmin], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

Alternatively, the specific execution process of step B3 includes step B34: The vehicle determines, according to a fourth formula, the target probability value Pi corresponding to the target discrete value.

If i is equal to k +<NUM>, the vehicle determines, according to the fourth formula, the target probability value Pi corresponding to the target discrete value, where the fourth formula is:
<MAT>, where <MAT>, an integral interval of the fourth formula is [axmax,ax1 + k × δax1], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

According to this implementation, the vehicle can accurately calculate the target probability value corresponding to the target discrete value, so that based on the accurate target probability value, the vehicle can effectively improve accuracy of estimating, by the vehicle, the road region in which the target moving object travels at the subsequent moment.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, step C specifically includes the following steps.

Step C1: The vehicle determines, according to a fifth formula, coordinates (xt<NUM> +nΔt, yt<NUM> +nΔt) of a target track point included in the target movement track.

Specifically, if the target status information further includes a lateral velocity vx<NUM> of the target moving object and a longitudinal velocity vy<NUM> of the target moving object, the vehicle may determine, according to the fifth formula, the coordinates (xt<NUM> +nΔt, yt<NUM> +nΔt) of the target track point included in the target movement track, where the target track point is an nth track point in a plurality of track points included in the target movement track, t<NUM> is the current moment, Δt is a predicted time step, and n is a positive integer greater than or equal to <NUM>;.

According to this implementation, when the vehicle can accurately predict coordinates of any track point included in the target movement track, the vehicle can predict the target movement track, so as to effectively improve accuracy and efficiency of estimating, by the vehicle, the road region in which the target moving object travels at the subsequent moment.

A specific execution process of step D includes the following steps.

Step D1: The vehicle obtains a target probability value corresponding to each target track point included in the target movement track.

The target probability value corresponding to the target track point is equal to the target probability value corresponding to the target discrete value.

Step D2: The vehicle obtains a target track point that satisfies a regression fitting condition.

The target track point that satisfies the regression fitting condition and that is obtained by the vehicle is a track point corresponding to a target probability value greater than or equal to a preset probability value.

Specifically, when the vehicle determines the target probability value corresponding to the target track point included in the target movement track, if the target probability value is larger, it indicates that the target track point is more likely to belong to the road region of the target moving object at the subsequent moment; and if the target probability value is smaller, it indicates that the target track point is less likely to belong to the road region of the target moving object at the subsequent moment. In this case, to enable the vehicle to remove a track point that is less likely to belong to the road region, thereby improving reliability of the road region in which the vehicle travels at the subsequent moment and that is estimated by the vehicle, the vehicle may perform, by using step D2, filtering on each track point included in the target movement track. In this way, a track point that is more likely to belong to the road region is reserved.

Step D3: The vehicle performs regression fitting based on the target track point that satisfies the regression fitting condition, to obtain the road region in which the target moving object travels at the subsequent moment.

According to this implementation, the track point that is less likely to belong to the road region is effectively removed, to improve the reliability of the road region in which the vehicle travels at the subsequent moment and that is estimated by the vehicle. In this way, accuracy of the road region predicted by the vehicle is improved.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, step D1 specifically includes the following steps.

Step D11: The vehicle performs, according to a sixth formula, attenuation processing on a target probability value Pi corresponding to the target track point (xt<NUM> +nΔt, yt<NUM>+nΔt).

A target probability value obtained after the vehicle performs the attenuation processing is a target probability value P(xt<NUM>+nΔt,yt<NUM>+nΔt).

The sixth formula is P(xt<NUM>+nΔt, yt<NUM> +nΔt) = λn × Pi, where λ is a preset value.

Step D12: The vehicle determines that the target probability value corresponding to the target track point is the target probability value p(xt<NUM>+nΔt, yt<NUM>+nΔt) obtained after the attenuation processing.

Accuracy of a track point included in the target movement track that is obtained by the vehicle by performing prediction on the target moving object is gradually reduced with time. If the accuracy of the track point estimated by the vehicle is higher, it indicates that a probability that the track point belongs to the road region in which the target moving object travels at the subsequent moment is higher. If the accuracy of the track point estimated by the vehicle is lower, it indicates that a probability that the track point belongs to the road region in which the target moving object travels at the subsequent moment is lower. Therefore, according to this implementation, the vehicle may perform attenuation processing on the target probability value as time goes by, so that accuracy of the track point can be improved by using a target probability value obtained after the attenuation processing.

Based on the first aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, step D may further include the following steps.

Step D13: The vehicle obtains a plurality of probability values corresponding to the target track point.

Specifically, if determining that the target track point that satisfies a first preset condition can be obtained, the vehicle obtains the plurality of probability values corresponding to the target track point, where the first preset condition is that the target track point is located in different movement tracks.

Step D14: The vehicle determines, as the target probability value corresponding to the target track point, a maximum value of probability values that are corresponding to the target track point in the different movement tracks.

Specifically, when the vehicle predicts a movement track of the moving object at the subsequent moment, there is the target track point that satisfies the first preset condition, that is, the target track point is located in the different movement tracks when there is an overlap between spatial positions in the different movement tracks. To improve accuracy of the track point estimated by the vehicle, the vehicle determines, as the target probability value, the maximum value of the probability values that are corresponding to the target track point in the different movement tracks, so that the accuracy of the track point can be improved by using the target probability value obtained after the attenuation processing.

Step D15: The vehicle obtains a historical movement track that has been predicted for the target moving object at a historical moment.

The historical moment is a previous moment of the current moment.

Step D16: The vehicle obtains a first probability value and a second probability value that are corresponding to the target track point.

Specifically, if the target track point that satisfies a second preset condition is obtained, the first probability value and the second probability value that are corresponding to the target track point are obtained, where the second preset condition is that the target track point is located in both the target movement track and the historical movement track, the first probability value is a probability value that is corresponding to the target track point and that is obtained by performing prediction on the target moving object at the current moment, and the second probability value is a probability value that is corresponding to the target track point and that is obtained by performing prediction on the target moving object at the historical moment.

Step D17: The vehicle determines the larger of the first probability value and the second probability value as the target probability value corresponding to the target track point.

If the vehicle has stored the historical movement track that is obtained by performing prediction on the target moving object at the historical moment, the vehicle may obtain the target movement track by performing prediction on the target moving object at the current moment. In addition, the historical movement track and the target movement track intersect at an intersection point, and the intersection point is the target track point that satisfies the second preset condition. In this case, to improve the accuracy of the track point estimated by the vehicle, the vehicle determines the larger of the first probability value and the second probability value as the target probability value corresponding to the target track point.

A second aspect of the embodiments of the present invention provides a driving road estimation system for predicting a road occupancy map of a target moving object according to appended claim <NUM>.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, the processing unit includes:.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, when the target status information further includes the longitudinal acceleration ay1 of the target moving object, and the target interval is an ith interval in the <NUM>( k+<NUM>) intervals, the second obtaining module specifically includes:.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, if the target status information further includes the lateral velocity vx<NUM> of the target moving object and the longitudinal velocity vy<NUM> of the target moving object, the prediction unit is specifically configured to: determine, according to a fifth formula, coordinates (xt<NUM>+nΔt, yt<NUM>+nΔt) of a target track point included in the target movement track, where the target track point is an nth track point in a plurality of track points included in the target movement track, t<NUM> is the current moment, Δt is a predicted time step, and n is a positive integer greater than or equal to <NUM>;.

Based on the second aspect of the embodiments of the present invention, in an optional implementation of the embodiments of the present invention, the first determining module includes:.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium according to appended claim <NUM>.

According to the method and the system that are provided in this application, when obtaining the target status information of the surrounding target moving object, the vehicle may perform discretization processing on the target status information to obtain the parameter sequence, so that the vehicle can perform prediction based on the target status information and the target discrete value that is included in the parameter sequence, to obtain the target movement track. In this way, the vehicle can determine, based on the target movement track, the road region in which the target moving object travels at the subsequent moment, and then the vehicle can determine the region in which the vehicle travels at the subsequent moment, so that the region in which the vehicle travels at the subsequent moment does not overlap the region in which the target moving object travels at the subsequent moment. It can be learned that according to the method in the embodiments, even in a case of a severe traffic condition, such as a road congestion scenario and a night scenario in which a traffic condition is indefinite, the vehicle needs to obtain only the target status information of the target moving object at the current moment, to estimate the road region in which the vehicle travels at the subsequent moment, without relying on a surrounding environment. In this way, the vehicle can safely travel in the estimated road region, thereby effectively improving driving safety and reliability of the vehicle.

This application provides a driving road estimation method and a driving road estimation system. For better understanding of the driving road estimation method provided in this application, a specific hardware structure of a driving road estimation system to which the driving road estimation method is applied is first described with reference to <FIG> by using an example.

First, it should be noted that the driving road estimation system in this application not only can be applied to driving of a vehicle, but also can be applied to driving of an airplane or a ship, so that the airplane or the ship can also travel on a traveling path. In this application, an example in which the driving road estimation system is applied to driving of a vehicle is used for description.

As shown in <FIG>, an example in which the driving road estimation system in this embodiment is applied to a vehicle <NUM> is used for description. In another embodiment, the driving road estimation system may alternatively run on a separate computing device. In this case, when the vehicle needs to perform driving road estimation, the computing device on which the driving road estimation system runs may be installed on the vehicle, so that the vehicle can perform the driving road estimation method in this application.

Specifically, the driving road estimation system in this embodiment may include a sensor layer <NUM>, a data processing layer <NUM>, and a vehicle control layer <NUM>.

Referring to <FIG>, a specific structure of the driving road estimation system is described.

The sensor layer <NUM> includes a camera <NUM>, radar <NUM>, a global positioning system (global positioning system, GPS) receiver <NUM>, and the like. The camera <NUM> is responsible for acquiring a road scenario image. Optionally, a specific device type of the camera <NUM> is not limited in this embodiment, provided that the camera <NUM> can acquire a scenario image of a road on which the vehicle exists. For example, the camera <NUM> may be a monocular camera. For another example, the camera <NUM> may be a binocular camera. The radar <NUM> is responsible for data acquisition of a dynamic obstacle and data acquisition of a static obstacle to generate obstacle status data. Specifically, the obstacle status data may be a position, a movement velocity, a movement direction, or the like of an obstacle. The radar <NUM> in this embodiment may be laser radar, millimeter-wave radar, or the like. The GPS receiver <NUM> is configured to receive a GPS signal and provide an initialized reference position for vehicle positioning. The GPS receiver <NUM> is an instrument for receiving a GPS satellite signal and determining a ground space position, where a navigation and positioning signal sent by a GPS satellite is an information resource that can be shared by a large quantity of users. Vast users on land and sea and in space have receiving devices capable of receiving, tracking, converting, and measuring GPS signals, and the GPS receiver <NUM> may obtain a positioning result with rough precision (precision ranging from several meters to tens of meters) by performing calculation on a received GPS signal.

The data processing layer <NUM> includes an image processor <NUM>, a central processing unit (central processing unit, CPU) <NUM>, and a memory <NUM>. The memory <NUM> stores a computer-readable program, and the CPU <NUM> may run the computer-readable program. In this case, the CPU <NUM> obtains, by running the computer-readable program, the road scenario image acquired by the camera <NUM> and the obstacle status data acquired by the radar <NUM>, and sends the obtained road scenario image and obstacle status data to the image processor <NUM>. The image processor <NUM> is configured to perform operations such as lane line recognition, road edge recognition, vehicle recognition, non-motor vehicle recognition, and pedestrian recognition on the road scenario image and the obstacle status data, to generate recognition result data, where the recognition result data is used to indicate a lane line, a road edge, a vehicle, a non-motor vehicle, a pedestrian, and the like that have been recognized by the image processor <NUM>.

The image processor <NUM> sends the recognition result data to the CPU <NUM>, so that the CPU <NUM> can perform road prediction, road merging, road extraction, and the like based on the recognition result data and the obstacle status data that is acquired by the radar <NUM>, to generate a control signal. The CPU <NUM> is configured to control the vehicle based on the control information, so that the vehicle travels according to a planned track.

The vehicle control layer <NUM> includes a controller <NUM>. The controller <NUM> is configured to receive the control signal sent by the CPU <NUM>, and implement lateral and/or longitudinal control on the vehicle based on the control signal.

A specific position of each of the foregoing components on the vehicle is not limited in this embodiment, provided that a corresponding function of the component can be implemented.

Based on the driving road estimation system shown in <FIG>, with reference to <FIG>, the following describes in detail a specific execution process of the driving road estimation method provided in this embodiment.

Step <NUM>: The vehicle obtains target status information of a target moving object at a current moment.

In this embodiment, the vehicle may directly perform step <NUM> in a traveling process to perform the step of obtaining, by the vehicle, target status information of a target moving object at a current moment; or when determining that a target condition is satisfied, the vehicle may perform the step of obtaining, by the vehicle, target status information of a target moving object at a current moment. The following describes the target condition.

The target condition in this embodiment may be that the vehicle fails to obtain road information of a current road, where the current road is a road on which the vehicle is currently located. The road information is used to indicate a driving status of the current road, for example, a geometric structure of the current road, a type and a position of a lane line, and a type and a position of a traffic light.

A reason why the vehicle fails to obtain the road information may be that the vehicle fails to query a high-precision map and fails to obtain the road information. The high-precision electronic map provides map data with higher precision (centimeter-level precision) that is used for autonomous driving. A reason why the vehicle fails to obtain the road information may be that the vehicle cannot detect the road information based on the sensor layer due to a poor driving environment of the vehicle. The poor driving environment in this embodiment may be a case in which the vehicle travels in a night scenario, a case in which there is no lane line on the current road, or the like. A reason why the vehicle fails to obtain the road information may alternatively be that the vehicle fails in positioning, for example, the vehicle fails to obtain GPS positioning data.

For a scenario in which the vehicle fails to obtain the road information of the current road, refer to <FIG>. As shown in <FIG>, a vehicle <NUM>, a vehicle <NUM>, a vehicle <NUM>, and a vehicle <NUM> that are ahead of a vehicle <NUM> block lane lines of the current road, and therefore the vehicle <NUM> fails to obtain the road information of the current road. In this case, the vehicle <NUM> can determine a target moving object on which driving road estimation needs to be performed.

The following describes a specific process in which the vehicle determines the target moving object.

The target moving object is described first. The target moving object in this embodiment is any one of at least one moving object around the vehicle <NUM>. For example, the moving object may be an object that can affect a traveling track of the vehicle, such as a vehicle, a non-motor vehicle, or a pedestrian.

To reduce a computation amount of the driving road estimation in this embodiment, the vehicle <NUM> may determine that the target moving object is a moving object ahead of the vehicle <NUM>. Using <FIG> as an example, target moving objects may be the vehicle <NUM>, the vehicle <NUM>, the vehicle <NUM>, and the vehicle <NUM>.

After determining the target moving object, the vehicle may determine target status information of the target moving object at a current moment.

The following describes specific content included in the target status information.

The target status information determined by the vehicle may be attribute information of the target moving object. The attribute information of the target moving object is used to indicate a type of the target moving object. The type of the target moving object in this embodiment may be a vehicle, a non-motor vehicle, a pedestrian, or the like.

Alternatively, the target status information may be position information of the target moving object. Specifically, the position information of the target moving object is position information of the target moving object in a GPS coordinate system.

Alternatively, the target status information may be movement velocities of the target moving object. Specifically, the movement velocities of the target moving object may include a lateral movement velocity Vx along an X-axis and a longitudinal movement velocity Vy along a Y-axis that are of the target moving object in the GPS coordinate system.

Alternatively, the target status information may be accelerations of the target moving object. Specifically, the accelerations of the target moving object may include a lateral acceleration ax along the X-axis and a longitudinal acceleration ay along the Y-axis that are of the target moving object in the GPS coordinate system.

Alternatively, the target status information may be a movement direction θ of the target moving object in the GPS coordinate system.

Using <FIG> as an example, if the vehicle <NUM> determines that the target moving object is the vehicle <NUM>, status information that is corresponding to the vehicle <NUM> and that is obtained by the vehicle <NUM> may be {attribute information (type): vehicle, position information (position): (x<NUM>, y<NUM>), movement velocity (velocity): (Vx1, Vy1), acceleration (acceleration): (ax1, ay1), movement direction (heading): θ1}.

By analogy, status information that is corresponding to the vehicle <NUM> and that may be obtained by the vehicle <NUM> is {type: vehicle, position: (x<NUM>, y<NUM>), velocity: (Vx2, Vy2), acceleration: (ax2, ay2), heading: θ2}; status information that is corresponding to the vehicle <NUM> and that may be obtained by the vehicle <NUM> is {type: vehicle, position: (x<NUM>, y<NUM>), velocity: (Vx3, Vy3), acceleration: (ax3, ay3), heading: θ3}; and status information that is corresponding to the vehicle <NUM> and that may be obtained by the vehicle <NUM> is {type: vehicle, position: (x<NUM>, y<NUM>), velocity: (Vx4, Vy4), acceleration: (ax4, ay4), heading: θ4}.

Step <NUM>: The vehicle performs discretization processing on the target status information to obtain a parameter sequence.

To estimate a driving road without relying on detection of an environment around the vehicle, the vehicle needs to estimate the movement direction of the target moving object, and can predict a movement track of the vehicle at a subsequent moment based on the estimated movement direction of the target moving object.

The following describes a manner in which the vehicle estimates the movement direction of the target moving object.

An example in which the moving object is a vehicle is used for description. For the vehicle, a change in a movement direction of the vehicle is limited by an acceleration of the vehicle. To be specific, different lateral accelerations ax and different longitudinal accelerations ay of the vehicle cause a change in the movement direction of the vehicle. Using <FIG> as an example, <FIG> is a grid graph drawn for a vehicle for a current road on which the vehicle is located. In this embodiment, an example in which radar included in the vehicle is laser radar is used for description. Because precision of the laser radar is <NUM> centimeters, a size of each grid of the grid graph drawn for the vehicle is <NUM>×<NUM> centimeters. A lateral acceleration ax and a longitudinal acceleration ay of a vehicle shown on a left side of <FIG> are respectively different from those of a vehicle shown on a right side of <FIG>. Consequently, a movement direction θi of the vehicle shown on the left side of <FIG> is different from a movement direction θj of the vehicle shown on the right side of <FIG>.

It can be learned that discretization processing on the acceleration of the vehicle approximates to discretization processing on the movement direction of the vehicle. However, because the vehicle in this embodiment needs to perform driving road estimation, the vehicle is more concerned about whether a feasible region of a road is wide enough in a driving process of the vehicle. Therefore, the vehicle needs to estimate impact of a lateral acceleration of a vehicle ahead on a movement direction of the vehicle ahead. Therefore, the vehicle only needs to perform discretization processing on a lateral acceleration of a moving object.

A manner in which the vehicle performs discretization processing on the lateral acceleration of the moving object is not limited in this embodiment, provided that the lateral acceleration of the moving object that is presented as continuous data can be discretized into discrete data. In this embodiment, an example in which the vehicle performs discretization processing on the lateral acceleration of the moving object in a probability discretization processing manner that is based on Gaussian distribution (gaussian distribution) is used for description.

Specifically, using an example in which the target moving object is the vehicle <NUM>, the vehicle may set that a lateral acceleration of the vehicle <NUM> satisfies a target Gaussian distribution <MAT> shown in <FIG>.

As shown in <FIG>, the lateral acceleration ax1 in the target Gaussian distribution is discretized into a bell-shaped region, an expectation of the target Gaussian distribution is the lateral acceleration ax1 of the vehicle <NUM>, a variance of the target Gaussian distribution is <MAT>, and the variance <MAT> represents a discrete degree of the target Gaussian distribution <MAT>; and the bell-shaped region presented by the lateral accelerationax1 satisfying the target Gaussian distribution <MAT> is discretized into <NUM>( k+<NUM>) intervals shown in <FIG>, where k is a preset discretization coefficient. A specific value range of k is not limited in this embodiment.

Optionally, k in this embodiment may be inversely proportional to resolution f of a map stored in the vehicle, that is, k∝<NUM>/f, and the resolution f of the map is measured by using meters.

Specifically, a coordinate point at a central position of the bell-shaped region is C1, and a value of C1 is the lateral acceleration ax1 of the vehicle <NUM>. In other words, the bell-shaped region is symmetrically distributed by using the horizontal coordinate C1 as a center, a left side of the horizontal coordinate C1 is a negative direction of the bell-shaped region, and a right side of the horizontal coordinate C <NUM> is a positive direction of the bell-shaped region. In this case, the <NUM>( k+<NUM>) intervals may be represented as P={P-k-<NUM>, P-k,. , Pk+<NUM>}, where i is a non-zero integer in an interval [-k -<NUM>, k + <NUM>].

Specifically, an interval P-k obtained after the lateral acceleration ax1 is discretized indicates k intervals that are on the left side of the horizontal coordinate C1 and that are in the <NUM>( k+<NUM>) intervals obtained after the lateral acceleration ax1 is discretized; and an interval Pi obtained after the lateral acceleration ax1 is discretized indicates i intervals that are on the right side of the horizontal coordinate C1 and that are in the <NUM>( k+<NUM>) intervals obtained after the lateral acceleration ax1 is discretized.

After discretization processing is performed on the lateral acceleration ax1 of the vehicle <NUM>, the vehicle can perform sampling in a target interval to obtain a target discrete value, where the target interval is any interval included in the <NUM>( k+<NUM>) intervals formed by the bell-shaped region that is obtained after the lateral acceleration of the vehicle <NUM> is discretized.

The vehicle creates a parameter sequence based on a sampled value obtained by performing sampling in any one of the <NUM>( k+<NUM>) intervals, where the parameter sequence includes a discrete value obtained by performing sampling in any one of the <NUM>( k+<NUM>) intervals that are obtained after the lateral acceleration of the vehicle <NUM> is discretized. Specifically, the parameter sequence is S={(ax_sample[-k-<NUM>]), (ax_sample[-k]),. , (ax_sample[i]),. , (ax_sample[k]), (ax_sample[k+<NUM>])}, where ax_sample[i] represents a discrete value obtained by performing sampling by the vehicle in an ith interval in the <NUM>( k +<NUM>) intervals.

Step <NUM>: The vehicle predicts, based on a target discrete value and the target status information, a target movement track corresponding to the target discrete value.

The target discrete value is any one of a plurality of discrete values included in the parameter sequence.

It can be learned from the foregoing description that the vehicle can obtain the target discrete value by performing sampling based on any target interval included in the <NUM>( k+<NUM>) intervals obtained after the discretization processing, and can predict the target movement track based on the target discrete value. It can be learned that the vehicle can predict <NUM>( k+<NUM>) movement tracks by predicting the <NUM>( k+<NUM>) intervals, where the <NUM>( k+<NUM>) movement tracks may be represented as T={T-k-<NUM>,. , Tk+<NUM>}, and a target movement track Ti is an ith track in the <NUM>( k+<NUM>) movement tracks.

The following describes a specific process in which the vehicle predicts the target movement track Ti based on the target discrete value.

The target movement track Ti in this embodiment includes a series of track points. Specifically, the target movement track is Ti = {(xt<NUM> , yt<NUM>), (xt<NUM>+Δt, yt<NUM>+Δt),. ,(xt<NUM>+nΔt,yt<NUM>+nΔt)}, where t0 represents a current moment, Δt is a predicted time step, nΔt represents a total time for the vehicle to perform forward prediction. A specific value of Δt is not limited in this embodiment. In this embodiment, an example in which Δt is any value from <NUM> seconds to <NUM> seconds is used for description. n is a positive integer greater than or equal to <NUM>. Coordinates of a target track point included in the target motion track Ti are (xt<NUM>+nΔt,yt<NUM>+nΔt). The target track point is an nth track point in a plurality of track points included in the target movement track Ti. It can be learned that when the vehicle has determined coordinates of all the track points included in the target track point Ti, the vehicle can determine the target movement track Ti.

More specifically, the vehicle may determine the target movement track according to a fifth formula.

The fifth formula in this embodiment is: <MAT> and <MAT> where
in the fifth formula, ay1 is the longitudinal acceleration included in the target status information, and ax_sample[i] is the target discrete value.

Referring to the foregoing process in which the vehicle predicts the target movement track Ti according to the fifth formula, the vehicle can predict any one of the <NUM>( k+<NUM>) movement tracks, as shown in <FIG>.

Step <NUM>: The vehicle determines, based on the target movement track, a road region in which the target moving object travels at a subsequent moment.

With reference to <FIG>, the following describes, by using an example, a manner in which the road region based on the target movement track is determined. <FIG> shows a target movement track predicted by the vehicle by performing estimation on a target moving object <NUM>.

Specifically, if the vehicle determines the target movement track, the vehicle can determine a road occupancy map of the target moving object <NUM> at the subsequent moment based on the target movement track, where the subsequent moment is a next moment of the current moment.

Optionally, an optional manner in which the vehicle determines the road occupancy map of the target moving object <NUM> at the subsequent moment is as follows.

More specifically, the vehicle may obtain target status information of the target moving object <NUM>. The target status information in this embodiment may further include a vehicle width L of the target moving object <NUM>. In this case, the vehicle may perform first preset processing on a horizontal coordinate of any track point included in the target movement track to obtain a first track point. All first track points determined by the vehicle may form a left lane boundary line of the target moving object <NUM> at the subsequent moment. The vehicle may further perform second preset processing on a vertical coordinate of any track point included in the target movement track to obtain a second track point. All second track points determined by the vehicle may form a right lane boundary line of the target moving object <NUM> at the subsequent moment.

Using <FIG> as an example, after first preset processing is performed on a horizontal coordinate of any track point included in the target movement track to obtain all first track points, all the first track points may form a left lane boundary line <NUM> of the target moving object <NUM> at the subsequent moment.

The left lane boundary line <NUM> of the target moving object <NUM> at the subsequent moment is <MAT>.

After second preset processing is performed on a vertical coordinate of any track point included in the target movement track to obtain all second track points, all the second track points may form a right lane boundary line <NUM> of the target moving object <NUM> at the subsequent moment.

The right lane boundary line <NUM> of the target moving object <NUM> at the subsequent moment is <MAT> where <MAT> and θn = θn - θ<NUM>.

The vehicle may form the road occupancy map of the target moving object <NUM> at the subsequent moment based on the determined left lane boundary line and right lane boundary line.

Optionally, another optional manner in which the vehicle determines the road occupancy map of the target moving object <NUM> at the subsequent moment is as follows.

The vehicle determines that the target movement track is a lane central axis of the road occupancy map of the target moving object <NUM> at the subsequent moment. In this case, when the vehicle has determined the lane central axis of the road occupancy map of the target moving object <NUM> at the subsequent moment, the vehicle can determine the road occupancy map of the target moving object <NUM> at the subsequent moment.

It can be learned that based on the foregoing process, the vehicle may determine road occupancy maps of all moving objects ahead of the vehicle at the subsequent moment, and in this case, the vehicle can estimate the road region in which the target moving object travels at the subsequent moment. A specific extension direction of the road region is not limited in this embodiment.

Still referring to <FIG>, the vehicle may obtain road regions in which the target moving object <NUM>, the target moving object <NUM>, the target moving object <NUM>, and the target moving object <NUM> travel at the subsequent moment. In this case, the vehicle can estimate a road region in which the vehicle travels at the subsequent moment. The road region in which the vehicle travels at the subsequent moment is not limited in this embodiment, provided that the road region in which the vehicle travels at the subsequent moment does not overlap the estimated road region in which the target moving object travels at the subsequent moment. Using <FIG> as an example, the road region in which the vehicle travels at the subsequent moment does not overlap the road region in which the target moving object <NUM> travels at the subsequent moment, the road region in which the target moving object <NUM> travels at the subsequent moment, the road region in which the target moving object <NUM> travels at the subsequent moment, and the road region in which the target moving object <NUM> travels at the subsequent moment. This ensures that the vehicle does not collide with the target moving object <NUM>, the target moving object <NUM>, the target moving object <NUM>, and the target moving object <NUM> in a traveling scenario at the subsequent moment, thereby implementing safe driving.

Beneficial effects of using the driving road estimation method in this embodiment are as follows.

According to the method in this embodiment, the vehicle obtains the target status information of the target moving object at the current moment; the vehicle directly performs, without relying on a current driving environment, discretization processing on the target status information to obtain the parameter sequence, and then can obtain the target movement track based on the parameter sequence; and the vehicle can estimate, by performing calculation on the target movement track, the road region in which the vehicle travels at the subsequent moment. In a process of obtaining the road region, the vehicle does not need to rely on a driving environment. Therefore, even in a severe environment, for example, in a night traveling scenario and a complex traffic scenario, the vehicle can accurately estimate the road region in which the vehicle travels, without tracking the target moving object, thereby improving accuracy and robustness of estimating the road region in which the vehicle travels at the subsequent moment. This provides a safety assurance for autonomous driving.

The vehicle may predict, based on the embodiment in <FIG>, a road occupancy map of each target moving object at a subsequent moment. With reference to an embodiment shown in <FIG>, the following describes a specific process of improving accuracy of a road region, predicted by a vehicle, at a subsequent moment.

For details about a specific execution process of step <NUM> in this embodiment, refer to step <NUM> shown in <FIG>. The details are not described in this embodiment again.

In the embodiment shown in <FIG>, the parameter sequence includes a target discrete value obtained by performing sampling in any one of <NUM>( k+<NUM>) intervals obtained after a lateral acceleration of the target moving object is discretized. Specifically, the parameter sequence is s={(ax_sample[-k-<NUM>]), (ax_sample[-k]),. , (ax_sample[i]),. , (ax_sample[k]), (ax_sample[k+<NUM>])}, where ax_sample[i] represents a discrete value obtained by performing sampling by the vehicle in an ith interval in the <NUM>( k+<NUM>) intervals.

In contrast, in this embodiment, compared with the parameter sequence shown in <FIG>, the parameter sequence in this embodiment further includes a target probability value corresponding to the target discrete value. A specific process is as follows.

A target probability value corresponding to the target discrete value obtained by the vehicle through sampling in the target interval is equal to a target probability value corresponding to the target interval. It can be learned that when determining the target probability value corresponding to the target interval, the vehicle can determine the target probability value that is corresponding to the target discrete value and that is equal to the target probability value corresponding to the target interval.

The following describes, by using an example, a specific process in which the vehicle obtains the target probability value corresponding to the target interval.

Specifically, if the target probability value corresponding to the target interval is equal to an area of the target interval, the vehicle may calculate, by calculating the area of the target interval, the target probability value corresponding to the target interval.

A specific process in which the vehicle calculates the area of the target interval is as follows.

If the target interval is an ith interval in the <NUM>( k+<NUM>) intervals, and when the vehicle determines that i is any integer greater than <NUM> and less than or equal to k, the vehicle may determine, according to a first formula, the target probability value Pi corresponding to the target interval, where the first formula is:<MAT>, where an integral variable is ax, ax is any value in an interval from ax1 + i × δax1 to ax1 + (i - <NUM>) × δax1, an integral interval of the first formula is [ax1 + i × δax1, ax1 + (i -<NUM>) × δax1], an integrand is <MAT>, and <MAT> is a <MAT> power of e.

If determining that i is any integer greater than -k and less than <NUM>, the vehicle may determine, according to a second formula, the target probability value Pi corresponding to the target interval, where the second formula is:<MAT>, where ax is any value in an interval from ax1 + (i +<NUM>) ×δax1 to ax1 + i × δax1, an integral interval of the second formula is [ax1 +(i+<NUM>)×δax1,ax1 +i×δax1], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

If determining that i is equal to -k-<NUM>, the vehicle may determine, according to a third formula, the target probability value Pi corresponding to the target interval, where the third formula is:
<MAT>, where ax is any value in an interval from ax1 -k ×δax1 to ax min, <MAT>, amax represents a maximum acceleration of the vehicle <NUM>, amax in this embodiment is less than a gravitational acceleration g, g=<NUM>/s<NUM>, and ay<NUM> is a longitudinal acceleration of the vehicle <NUM> at the current moment; an integral interval of the third formula is [ax1 -k-×δax1,axmin], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

If determining that i is equal to k + <NUM>, the vehicle may determine, according to a fourth formula, the target probability value Pi corresponding to the target interval, where the fourth formula is:
<MAT>, where ax is any value in an interval from axmax to ax1 + k × δax1 , <MAT>, an integral interval of the fourth formula is [axmax, ax1 + k × δax1], an integral variable is ax, an integrand is <MAT>, and <MAT> is a <MAT> power of e.

When obtaining the target probability value Pi corresponding to the target interval, the vehicle can determine that the target probability value corresponding to the target discrete value obtained by sampling in the target interval is also Pi. In this case, the vehicle can create a parameter sequence, where the parameter sequence includes a discrete value obtained by performing sampling in any one of <NUM>( k+<NUM>) intervals obtained after a lateral acceleration of the vehicle <NUM> is discretized and a corresponding target probability value. Specifically, the parameter sequence in this embodiment is S={(ax_sample[-k-<NUM>], P-k-<NUM>), (ax_sample[-k], P-k),. , (ax_sample[i], Pi),. , (ax_sample[k], Pk), (ax_sample[k+<NUM>], Pk+<NUM>)}, where ax_sample[i] represents a discrete value obtained by performing sampling by the vehicle in an ith interval in the <NUM>( k+<NUM>) intervals, and Pi represents a target probability value corresponding to the ith interval.

Step <NUM>: The vehicle predicts, based on the target discrete value and the target status information, a target movement track corresponding to the target discrete value.

For details about a specific execution process of step <NUM> in this embodiment, refer to step <NUM> shown in <FIG>. The specific execution process is not described again in this embodiment.

Step <NUM>: The vehicle performs, according to a sixth formula, attenuation processing on a target probability value corresponding to the target track point, to obtain a target probability value after the attenuation processing.

Specifically, <FIG> is used as an example. Accuracy of a track point included in the target movement track that is obtained by the vehicle by performing prediction on the target moving object is gradually reduced with time. If the accuracy of the track point estimated by the vehicle is higher, it indicates that a probability that the track point belongs to a road region in which the target moving object travels at the subsequent moment is higher. If the accuracy of the track point estimated by the vehicle is lower, it indicates that a probability that the track point belongs to the road region in which the target moving object travels at the subsequent moment is lower.

Still referring to <FIG>, accuracy of estimating a track point (xt0+nΔt, yt<NUM>+nΔt) by the vehicle is lower than accuracy of estimating a track point (xt<NUM>+nΔt, yt<NUM>+nΔt) by the vehicle.

Based on a characteristic that accuracy of a track point is reduced with time, in this embodiment, the vehicle may perform attenuation processing on the target track point included in the predicted target movement track. The following describes in detail a specific process of performing attenuation processing on the target track point by the vehicle.

Using the target track point (xt0+nΔt, yt<NUM>+nΔt) as an example, the vehicle may first query the parameter sequence for a target probability value corresponding to the target track point. Specifically, the vehicle may obtain a target movement track to which the target track point belongs, so as to determine a target probability value P, that is corresponding to the target movement track and that is in the parameter sequence.

The vehicle can perform, according to the sixth formula, attenuation processing on the target probability value P, corresponding to the target track point (xt<NUM>+nΔt, yt<NUM>+nΔt) to obtain a target probability value P(xt<NUM>+nΔt, yt<NUM>+nΔt) after the attenuation processing.

The sixth formula is P(xt<NUM>+nΔt, yt<NUM>+nΔt) = λn × Pi, where A is an attenuation factor prestored by the vehicle, that is, A is a preset value stored by the vehicle. A specific value of A is not limited in this embodiment. Specifically, to perform attenuation for a track point included in a target track line, A in this embodiment may be any value from <NUM> to <NUM>.

When the vehicle determines, based on the sixth formula, the target probability value P(xt<NUM>+nΔt , yt<NUM>+nΔt) obtained after the attenuation processing, the vehicle may determine that the target probability value corresponding to the target track point is the target probability value P(xt<NUM>+nΔt, yt<NUM>+nΔt) obtained after the attenuation processing.

By using the sixth formula in this embodiment, the vehicle can perform attenuation processing on a target probability value corresponding to coordinates of each track point included in the target movement track of the target moving object.

It can be learned that the vehicle performs attenuation processing on a probability of any track point included in the target movement track, to effectively improve accuracy of the any track point included in the target movement track. In this way, the track point predicted by the vehicle can more accurately reflect a movement track of the target moving object at the subsequent moment.

Optionally, when the vehicle predicts the movement track of the moving object at the subsequent moment, there is a target track point that satisfies a first preset condition. The following describes a case of the target track point that satisfies the first preset condition.

The first preset condition in this embodiment is that the target track point is located in different movement tracks. Using <FIG> as an example, the vehicle performs prediction on a first moving object <NUM> to obtain a first target movement track <NUM>, and performs prediction on a second moving object <NUM> to obtain a second target movement track <NUM>. As shown in <FIG>, there is an overlap between spatial positions in the first target movement track <NUM> and the second target movement track <NUM>, an intersection point in the overlap region is an intersection point <NUM> shown in <FIG>, the intersection point <NUM> is an (n1)th track point in the first target movement track <NUM>, and the intersection point <NUM> is an (n2)th track point in the second target movement track <NUM>. It can be seen that, the intersection point <NUM> in this embodiment is located in both the first target movement track <NUM> and the second target movement track <NUM>.

It can be learned that, if the intersection point <NUM> shown in <FIG> is the target track point that satisfies the first preset condition, the vehicle may obtain a first probability value and a second probability value that are corresponding to the intersection point <NUM>. If the vehicle has performed attenuation processing on the first target movement track <NUM> based on the foregoing description, the first probability value is a target probability value that is corresponding to the intersection point <NUM> in the first target movement track <NUM> and that is obtained after the vehicle performs attenuation processing on the first target movement track <NUM>. If the vehicle has performed attenuation processing on the second target movement track <NUM> based on the foregoing description, the second probability value is a target probability value that is corresponding to the intersection point <NUM> in the second target movement track <NUM> and that is obtained after the vehicle performs attenuation processing on the second target movement track <NUM>.

Optionally, if the vehicle does not perform attenuation processing on the target tracks, the first probability value is a target probability value corresponding to the first target movement track <NUM> of the vehicle, and the second probability value is a target probability value corresponding to the second target movement track <NUM> of the vehicle.

Using <FIG> as an example, the vehicle may determine the first probability value Pi(xt<NUM>+n1Δt, yt<NUM>+n1Δt) and the second probability value Pj(xt<NUM>+n2Δt, yt<NUM>+n2Δt) that are corresponding to the intersection point <NUM>. In this case, the vehicle may determine, in the following manner, a target probability value P(x, y) corresponding to the intersection point <NUM>, where P(x, y) = max((Pi(xt<NUM>+n1Δt,yt<NUM>+n1Δt),Pj(xt<NUM>+n2Δt,yt0+n2Δt)), that is, the target probability value P(x, y) corresponding to the intersection point <NUM> is the larger of the first probability value Pi(xt<NUM>+n1Δt,yt<NUM>+n1Δt) and the second probability value Pj(xt<NUM>+n2Δt,yt<NUM>+n2Δt) that are corresponding to the intersection point <NUM>.

In the foregoing description, an example in which the target track point is an intersection point between two movement tracks is used for description. In another embodiment, the target track point may alternatively be an intersection point among more than two movement tracks, provided that the vehicle can determine a probability value corresponding to the target track point in each of the plurality of movement tracks to determine a maximum value among a plurality of probability values as the target probability value.

Optionally, in this embodiment, the vehicle may correct, based on a historical movement track, a target probability value of a track point included in the target movement track, to improve accuracy of an obtained target probability value. Specifically, before performing step <NUM>, in a process of performing step <NUM>, or after performing step <NUM>, the vehicle may perform a process of correcting, based on the historical movement track, the target probability value of the track point included in the target movement track.

Referring to <FIG>, it can be learned from the foregoing description that <FIG> is a schematic diagram of distribution of <NUM>( k+<NUM>) movement tracks obtained by the vehicle by performing prediction on the target moving object at the current moment, and the vehicle further has stored <NUM>( k+<NUM>) historical movement tracks obtained by performing prediction on the target moving object at a historical moment, where the historical moment is a previous moment of the current moment. Using <FIG> as an example, the vehicle may obtain a historical movement track <NUM> by performing prediction on the target moving object <NUM> at the historical moment, and may obtain the target movement track <NUM> by performing prediction on the target moving object <NUM> at the current moment, the historical movement track <NUM> and the target movement track <NUM> intersect at an intersection point <NUM>, and the intersection point <NUM> is a target track point that satisfies a second preset condition. Specifically, as shown in <FIG>, the second preset condition is that the target track point <NUM> is located in both the target moving object <NUM> and the historical movement track <NUM>.

The vehicle can obtain a first probability value PC1(x, y) and a second probability value PH<NUM>(x, y) that are corresponding to the target track point <NUM>, where the first probability value PC1(x, y) is a probability value that is corresponding to the target track point and that is obtained by performing prediction on the target moving object <NUM> at the current moment, and the second probability value PH<NUM>(x, y) is a probability value that is corresponding to the target track point and that is obtained by performing prediction on the target moving object <NUM> at the historical moment.

The vehicle determines, based on the first probability value PC1(x, y) and the second probability value PH1(x, y) , a target probability value P(x, y) = max(PC<NUM>(x, y), PH<NUM>(x, y)) corresponding to the target track point, that is, the target probability value is the larger of the first probability value and the second probability value.

Based on the foregoing description, the vehicle can perform overlay processing on a probability value of any track point included in the target movement track predicted at the current moment and the historical movement track predicted at the historical moment, to obtain the target probability value corresponding to the target track point, so as to improve accuracy of the target probability value corresponding to the target track point predicted by the vehicle.

Step <NUM>: The vehicle obtains a target track point that satisfies a regression fitting condition.

It can be learned that the vehicle may determine, by using step <NUM>, a target probability value corresponding to any track point (target track point) included in the target movement track. If the target probability value is larger, it indicates that the target track point is more likely to belong to the road region of the target moving object at the subsequent moment; and if the target probability value is smaller, it indicates that the target track point is less likely to belong to the road region of the target moving object at the subsequent moment.

According to the method in this embodiment, to remove a track point that is less likely to belong to the road region, thereby improving reliability of a road region in which the vehicle travels at the subsequent moment and that is estimated by the vehicle, the vehicle may perform filtering on each track point included in the target movement track. In this way, a track point that is more likely to belong to the road region is reserved.

The following describes a manner in which the vehicle performs filtering on each track point included in the target movement track.

When the vehicle obtains a target probability value corresponding to a target track point, and if the target probability value is greater than or equal to a preset probability value, the vehicle determines that the target track point satisfies the regression fitting condition, where the target track point that satisfies the regression fitting condition is used to estimate the road region in which the target moving object travels at the subsequent moment; or if the target probability value is less than a preset probability value, the vehicle may determine that the target track point does not satisfy the regression fitting condition, so that the target track point that does not satisfy the regression fitting condition is not used to estimate the road region in which the target moving object travels at the subsequent moment. A specific value of the preset probability value Pthreshold is not limited in this embodiment, provided that the target track point corresponding to the target probability value belongs to the road region when the target probability value is greater than or equal to the preset probability value Pthreshold.

Specifically, description is provided with reference to the following formula. For example, coordinates of the target track point are (x, y). When the target probability value corresponding to the target track point satisfies P(x, y) ≥ Pthreshold , the target track point (x, y) satisfies the regression fitting condition; or when the target probability value corresponding to the target track point satisfies P(x, y) < Pthreshold, the target track point (x, y) does not satisfy the regression fitting condition.

Step <NUM>: The vehicle performs regression fitting based on the target track point that satisfies the regression fitting condition, to obtain the road region in which the target moving object travels at the subsequent moment.

By using step <NUM>, the vehicle performs filtering on all the track points included in the target movement track. For example, a target movement track obtained after the filtering may be as follows.

Ti = {(xsample<NUM>,ysample<NUM>),(xsample2,ysample<NUM>),. ,(xsamplek,ysamplek),. ,(xsamplen,ysamplen)} where <NUM> ≤ k ≤ n , a track point (xsample<NUM>,ysample1), a track point (xsample2,ysample2) ,. , and a track point (xsamplen,ysamplen) included in the target movement track all satisfy the regression fitting condition. It can be learned that the track points included in the target movement track obtained after the vehicle performs filtering on the target movement track are relatively dispersedly distributed. In this case, the vehicle cannot estimate, based on the scattered target track points included in the target movement track, a road region in which the vehicle can travel. Therefore, to improve accuracy of the road region in which the vehicle can travel and that is estimated by the vehicle, the vehicle may process, by using a regression fitting method, the track points that are included in the target movement track and that satisfy the regression fitting condition.

The following describes, by using an example, a manner in which the vehicle performs regression fitting on the track points that are included in the target movement track and that satisfy the regression fitting condition.

A specific regression fitting manner is not limited in this embodiment, provided that the vehicle can predict a target reference line based on the target movement track, making the vehicle predict, based on the target reference line, the road region in which the target moving object travels at the subsequent moment.

In this embodiment, an example in which the vehicle determines the target reference line by using a generalized regression neural network (general regression neural network, GRNN) is used for description. To be specific, in this embodiment, regression fitting is performed, by using the GRNN, on the scattered track points included in the target movement track to form the target reference line.

Specifically, the vehicle may train, based on a GRNN network structure shown in <FIG>, the track points that are included in the target movement track and that satisfy the regression fitting condition, to obtain the target reference line.

In this embodiment, an example in which regression fitting is performed by using the GRNN network structure is used for description. In this case, the GRNN in this embodiment uses, as learning samples, the track points that are included in the target movement track and that satisfy the regression fitting condition. Because the GRNN has a quite strong nonlinear mapping capability, a regression effect is still quite good when there is a small amount of sample data. Therefore, accuracy of a predicted movement track of the target moving object at the subsequent moment can be improved by using the GRNN.

The following first describes a specific structure of the GRNN network structure. The GRNN network structure in this embodiment includes four layers of networks. A first layer of network is an input layer <NUM>, a second layer of network is a pattern layer <NUM>, a third layer of network is a summation layer <NUM>, and a fourth layer of network is an output layer <NUM>.

Based on the target movement track Ti={(xsample<NUM>,ysample<NUM>),(xsample<NUM>,ysample<NUM>),. ,(xsamplek,ysamplek),. ,(xsamplen,ysamplen)}, the vehicle may generate an input vector. An element included in the input vector in this embodiment is a horizontal coordinate xsamplek of a target track point included in the target movement track, and a dimension of the input vector is <NUM>.

The pattern layer <NUM> includes a plurality of pattern layer neurons, and a quantity of pattern layer neurons included at the pattern layer <NUM> is equal to a quantity of track points included in the target movement track. For example, the target movement track in this embodiment includes n track points, and in this case, the pattern layer <NUM> also includes n pattern layer neurons. To be specific, the vehicle creates a correspondence between different pattern layer neurons and different track points included in the target movement track, so that the input layer <NUM> can input any element included in the input vector to a corresponding pattern layer neuron.

Specifically, the vehicle may input any element xsamplek included in the input vector to a kth pattern layer neuron included at the pattern layer <NUM>, and the kth pattern layer neuron performs calculation on xsamplek by using a pattern neuron transfer function, to obtain a pattern neuron output parameter pk.

Specifically, the pattern neuron transfer function is:
<MAT>, where xi is any value within a target range, a minimum value in the target range is a minimum value among horizontal coordinates of a track point (xsample<NUM>,ysample<NUM>), a track point (xsample<NUM>,ysample<NUM>),. , and a track point (xsamplen,ysamplen) included in the target movement track, and a maximum value in the target range is a maximum value among the horizontal coordinates of the track point (xsample<NUM>,ysample<NUM>), the track point (xsample<NUM>,ysample<NUM>),. , and the track point (xsamplen, ysamplen) included in the target movement track. xi with different values may be selected for the pattern neuron transfer function for different pattern layer neurons in this embodiment.

The vehicle may input, to both a first summation neuron and a second summation neuron, a pattern neuron output parameter that is output by any pattern layer neuron included at the pattern layer <NUM>.

Specifically, the first summation neuron performs calculation based on a first summation function to generate a first target parameter SD, where the first summation function is:
<MAT>. It can be learned that the first summation neuron in this embodiment may perform arithmetic summation on output parameters pk output by all the pattern layer neurons, where M1 is a connection weight between the first summation neuron and each pattern layer neuron. In this embodiment, an example in which the connection weight M1 of the first summation neuron is <NUM> is used for description.

Specifically, the second summation neuron performs calculation based on a second summation function to generate a second target parameter SN, where the second summation function is:
<MAT>. It can be learned that the second summation neuron in this embodiment may perform weighted summation on the output parameters pk output by all the pattern layer neurons, where M2 is a connection weight between the second summation neuron and each pattern layer neuron. In this embodiment, an example in which the connection weighted value M2 of the second summation neuron is a vertical coordinate of each track point included in the target movement track is used for description.

The vehicle inputs the first target parameter SD and the second target parameter SN to the output layer, and the output layer calculates an output parameter based on an output function, where the output function is <MAT>.

It can be learned that in the GRNN network structure in this embodiment, a coordinate point sequence may be output, where the coordinate point sequence includes a plurality of coordinate points (x<NUM>, y<NUM>), (x<NUM>, y<NUM>),. , and (xn , yn). In this case, the vehicle can determine that a track line formed by the coordinate point sequence is the target reference line, so that the vehicle can determine, based on the target reference line, the road region in which the target moving object travels at the subsequent moment.

For details about a specific process in which the vehicle determines, based on the target reference line formed by the coordinate point sequence, the road region in which the vehicle travels at the subsequent moment, refer to the specific process in which the vehicle determines the road region based on the target movement track in the embodiment shown in <FIG>. Details are not described again.

According to the method in this embodiment, the vehicle obtains the target status information of the target moving object at the current moment; the vehicle directly performs, without relying on a current driving environment, discretization processing on the target status information to obtain the parameter sequence, and then can obtain the target movement track based on the parameter sequence; and the vehicle can estimate, by performing calculation on the target movement track, the road region in which the vehicle travels at the subsequent moment. In a process of obtaining the road region, based on a characteristic that the target probability value gradually deviates from a true value with time, each target probability value is multiplied by a corresponding time attenuation factor λ, so as to obtain a relatively accurate road region of the target moving object at the subsequent moment. The vehicle performs estimation based on the road region of the target moving object at the subsequent moment, to accurately obtain the road region of the vehicle at the subsequent moment, thereby improving accuracy and robustness of estimating the road region in which the vehicle travels at the subsequent moment. This provides a safety assurance for autonomous driving.

With reference to <FIG>, the following describes, by using an example, a specific structure of a driving road estimation system provided in an embodiment.

As shown in <FIG>, the driving road estimation system includes an obtaining unit <NUM>, a processing unit <NUM>, a prediction unit <NUM>, and a determining unit <NUM>.

The obtaining unit <NUM> is configured to obtain target status information of a target moving object at a current moment, where the target moving object is any one of at least one moving object around the vehicle, and the target status information includes a lateral acceleration of the target moving object.

The processing unit <NUM> is configured to perform discretization processing on the target status information to obtain a parameter sequence, where the parameter sequence includes a plurality of discrete values.

Specifically, the processing unit <NUM> includes:.

The prediction unit <NUM> is configured to predict, based on the target discrete value and the target status information, a target movement track corresponding to the target discrete value, where the target discrete value is any one of the plurality of discrete values included in the parameter sequence.

If the target status information further includes a lateral velocity vx<NUM> of the target moving object and a longitudinal velocity vy<NUM> of the target moving object, the prediction unit <NUM> is specifically configured to: determine, according to a fifth formula, coordinates (xt<NUM>+nΔt,yt<NUM>+nΔt) of a target track point included in the target movement track, where the target track point is an nth track point in a plurality of track points included in the target movement track, t<NUM> is the current moment, Δt is a predicted time step, and n is a positive integer greater than or equal to <NUM>;.

The determining unit <NUM> is configured to determine, based on the target movement track, a road region in which the target moving object travels at a subsequent moment.

For details about a specific process and beneficial effects of performing a driving road estimation method by the driving road estimation system in this embodiment, refer to the embodiment shown in <FIG>. Details are not described in this embodiment again.

Based on the embodiment shown in <FIG>, the driving road estimation system can predict a road occupancy map of each target moving object at the subsequent moment. With reference to an embodiment shown in <FIG>, the following describes a specific process in which the driving road estimation system improves accuracy of a road region, predicted by the driving road estimation system, at a subsequent moment.

The obtaining unit <NUM> is configured to obtain target status information of a target moving object at a current moment, where the target moving object is any one of at least one moving object around a vehicle, and the target status information includes a lateral acceleration of the target moving object.

Specifically, the processing unit <NUM> includes a first obtaining module <NUM>, a sampling module <NUM>, a second obtaining module1523, and a setting module <NUM>.

The first obtaining module <NUM> is configured to set that the target status information satisfies a target Gaussian distribution <MAT>, where an expectation of the target Gaussian distribution is the lateral acceleration ax1, a variance of the target Gaussian distribution is <MAT>, and the variance <MAT> represents a discrete degree of the target Gaussian distribution <MAT>; and the target status information satisfying the target Gaussian distribution <MAT> is discretized into <NUM>(k+<NUM>) intervals, where k is a preset discretization coefficient.

The sampling module <NUM> is configured to perform sampling in a target interval to obtain a target discrete value, where the target interval is any one of the <NUM>(k+<NUM>) intervals obtained after discretization.

The second obtaining module <NUM> is configured to obtain a target probability value corresponding to the target discrete value, where the target probability value corresponding to the target discrete value is equal to an area of the target interval.

Specifically, the second obtaining module <NUM> specifically includes:.

The setting module <NUM> is configured to set the target discrete value and the target probability value corresponding to the target discrete value in the parameter sequence.

If the target status information further includes a lateral velocity vx<NUM>, of the target moving object and a longitudinal velocity vy<NUM>, of the target moving object, the prediction unit <NUM> is specifically configured to: determine, according to a fifth formula, coordinates (xt<NUM> +nΔt,yt<NUM>+nΔt) of a target track point included in the target movement track, where the target track point is an nth track point in a plurality of track points included in the target movement track, t<NUM> is the current moment, Δt is a predicted time step, and n is a positive integer greater than or equal to <NUM>;.

Specifically, the determining unit <NUM> includes a first determining module <NUM>, a second determining module <NUM>, and a third determining module <NUM>.

The first determining module <NUM> is configured to obtain a target probability value corresponding to the target track point included in the target movement track, where the target probability value corresponding to the target track point is equal to the target probability value corresponding to the target discrete value.

Optionally, the first determining module <NUM> specifically includes:.

Optionally, the first determining module <NUM> further includes:.

The second determining module <NUM> is configured to obtain a target track point that satisfies a regression fitting condition, where the target track point that satisfies the regression fitting condition is a track point corresponding to a target probability value greater than or equal to a preset probability value.

The third determining module <NUM> is configured to perform regression fitting based on the target track point that satisfies the regression fitting condition, to obtain the road region in which the target moving object travels at the subsequent moment.

This application further provides a computer-readable storage medium. The computer-readable storage medium includes an instruction. When the instruction is run on the central processing unit shown in <FIG>, the central processing unit is enabled to perform the methods shown in <FIG> and <FIG>. Specific execution processes are not described again.

This application further provides a computer program product including an instruction. When the computer program product is run on the central processing unit shown in <FIG>, the central processing unit is enabled to perform the methods shown in <FIG> and <FIG>. Specific execution processes are not described again.

Claim 1:
A method for predicting a road occupancy map of a target moving object, comprising:
obtaining (<NUM>) target status information of the target moving object at a current moment, wherein the target moving object is any one of at least one moving object around a vehicle, and the target status information comprises a lateral acceleration of the target moving object;
performing (<NUM>) discretization processing on the target status information to obtain a parameter sequence, wherein the parameter sequence comprises a plurality of discrete values;
predicting (<NUM>), based on a target discrete value and the target status information, a target movement track corresponding to the target discrete value, wherein the target discrete value is any one of the plurality of discrete values comprised in the parameter sequence, and the target movement track comprises a plurality of target track points;
determining (<NUM>), based on the target movement track, a road region in which the target moving object travels at a subsequent moment, wherein the subsequent moment is a next moment of the current moment;
characterized in that the determining comprises:
obtaining a target probability value corresponding to each target track point comprised in the target movement track, wherein the target probability value corresponding to the target track point is equal to the target probability value corresponding to the target discrete value;
obtaining a target track point that satisfies a regression fitting condition, wherein the target track point that satisfies the regression fitting condition is a track point corresponding to a target probability value greater than or equal to a preset probability value; and
performing regression fitting based on the target track point that satisfies the regression fitting condition, to obtain the road region in which the target moving object travels at the subsequent moment.