CN117629197A

CN117629197A - Vehicle data processing method, device, equipment and medium

Info

Publication number: CN117629197A
Application number: CN202311594912.1A
Authority: CN
Inventors: 张磊; 张煜东; 范圣印
Original assignee: Beijing Jidu Technology Co Ltd
Current assignee: Beijing Jidu Technology Co Ltd
Priority date: 2023-10-27
Filing date: 2023-11-27
Publication date: 2024-03-01

Abstract

The invention provides a vehicle data processing method, device, equipment and medium, wherein dynamic points and static points are screened out through inertial navigation data and millimeter wave point cloud data, a dynamic map at the next moment and a static map at the current moment are built, a prediction environment map at the next moment is obtained through fusion, road conditions at the next moment can be known in advance, vehicle control and road condition coping can be performed in advance, therefore, the number of sensors required to be used can be effectively reduced by using the inertial navigation data and the millimeter wave point cloud data, vehicle cost is reduced, the used data size is small, the data size required to be processed and the generated data size are reduced, the dependence on calculation performance and calculation resources is reduced, the calculation effect is improved, the variety of the data required to be processed in the process is small, the map building speed is high, the dependence on complex algorithms and multiple algorithms is reduced, and the vehicle data processing method is high in applicability and robustness.

Description

Vehicle data processing method, device, equipment and medium

Technical Field

The disclosure relates to the technical field of intelligent driving, in particular to a vehicle data processing method, device, equipment and medium.

Background

The vehicle (simultaneous localization and mapping, SLAM) technology is an important technology for assisting intelligent driving, and can enable the vehicle to better understand surrounding environment, detect obstacles and plan safe and efficient paths, and help the intelligent driving vehicle to achieve functions of accurate positioning, environment map construction, autonomous navigation, path planning and the like.

Common vehicle SLAM technology, whether vision SLAM or laser radar SLAM-based, is mostly implemented by combining with other various sensors through a multi-sensor fusion technology, so as to provide comprehensive and accurate sensing capability, and complex algorithms are required to process data fusion between different sensors, and targeted processing algorithms and the like are required to perform auxiliary processing, so as to solve the problems of conflict and inconsistent information of sensor data as much as possible, however, using multiple sensors not only increases a large amount of cost, but also is complex in algorithm, so that a large amount of data needs to be processed in real-time environment and actual use, and great challenges are presented to computing resources, computing performance and computing efficiency.

Disclosure of Invention

The embodiment of the disclosure at least provides a vehicle data processing method, device, equipment and medium.

The embodiment of the disclosure provides a vehicle data processing method, which comprises the following steps:

acquiring inertial navigation data and millimeter wave point cloud data of a vehicle, which are acquired at the current moment;

determining a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud based on the inertial navigation data;

constructing a dynamic map of the next moment by using the plurality of current frame dynamic points, and constructing a static map of the current moment by using the plurality of current frame static points;

and fusing the dynamic map and the static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

Therefore, only inertial navigation data and millimeter wave point cloud data are used, the number of sensors required to be used can be effectively reduced, vehicle cost is reduced, the used data volume is small, the data volume required to be processed and the generated data volume are reduced, dependence on calculation performance and calculation resources is reduced, the calculation effect is improved, dynamic points and static points are screened out through the inertial navigation data and the millimeter wave point cloud data, a dynamic map at the next moment and a static map at the current moment are further constructed, a prediction environment map at the next moment is obtained through fusion, road conditions at the next moment can be known in advance, vehicle control and road condition coping are facilitated to be carried out in advance, the variety of the required processed data in the process is small, the data volume is small, the map construction speed is high, dependence on complex algorithms and multiple algorithms is reduced, the applicability is high, and the robustness is high.

In an optional implementation manner, the determining, based on the inertial navigation data, a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points included in the point cloud includes:

acquiring the calculated previous actual speed of the vehicle at the previous moment;

estimating an estimated speed of the vehicle at the current time based on the inertial navigation data and the prior actual speed;

determining a point cloud indicated by the millimeter wave point cloud data, wherein the point cloud comprises a plurality of acquisition points;

and screening a plurality of current frame dynamic points and a plurality of current frame static points from the plurality of acquisition points based on the estimated speed.

Therefore, the estimated speed is estimated through inertial navigation data, and further, the screening of the dynamic point and the static point is realized, the method is rapid and effective, the accuracy is high, and the dependence is low.

In an alternative embodiment, the selecting a plurality of current frame dynamic points and a plurality of current frame static points from the plurality of acquisition points based on the estimated speed includes

Acquiring Doppler speed and point position of each acquisition point from the millimeter wave point cloud data;

calculating a speed error parameter of each acquisition point by using the estimated speed, the point position of the acquisition point and the Doppler speed;

If the speed error parameter is greater than a preset threshold value, determining the acquisition point as a current frame dynamic point;

and if the speed error parameter is smaller than or equal to the preset threshold value, determining the acquisition point as a current frame static point.

In an optional embodiment, after the determining, based on the inertial navigation data, a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points included in the point cloud, the method further includes:

acquiring the point position and Doppler speed of each current frame static point;

and calculating the current actual speed of the vehicle through the estimated speed, the point position of the static point of each current frame and the Doppler speed by using a least square method.

In an alternative embodiment, the constructing a dynamic map of the next moment using the plurality of current frame dynamic points includes:

calculating a current dynamic speed and a current dynamic angular speed of each current frame dynamic point by using the current actual speed, the point position and the Doppler speed of the current frame dynamic point;

clustering the plurality of current frame dynamic points according to the distance between any two current frame dynamic points and the current dynamic speed to obtain at least one dynamic object, and a current object position and a current object posture of the dynamic object under a vehicle body coordinate system of the vehicle;

Calculating a predicted object position of the dynamic object at the next time using a time interval between the current time and the next time and the current object position and the current actual speed;

calculating the predicted attitude of the dynamic object at the next moment by using the time interval, the current object attitude of the dynamic object and the current dynamic angular velocity corresponding to the dynamic object;

and constructing a dynamic map of the environment in which the vehicle is located at the next moment based on the predicted object position and the predicted gesture of each dynamic object.

In this way, by combining the current actual speed of the vehicle and the information of each point carried in the millimeter wave point cloud data, the current dynamic speed and the current dynamic angular speed of the dynamic point can be obtained, the dynamic objects existing around the vehicle can be obtained through clustering, further, the predicted object position and the predicted gesture of the dynamic objects at the next moment can be predicted, a dynamic map at the next moment can be constructed, and a high-frequency dynamic map can be output, so that the vehicle can be helped to predict possible movement conditions of surrounding objects at the next moment in advance, and the dynamic obstacle avoidance planning is facilitated.

In an alternative embodiment, the constructing the static map of the current moment using the plurality of static points of the current frame includes:

identifying a pure static point and a semi-static point from the plurality of current frame static points;

a purely static map is constructed using the identified purely static points, and a semi-static map is constructed using the identified semi-static points.

The static points are further divided to obtain the pure static points and the semi-static points, which is favorable for fine recognition of surrounding environments, more accurate construction of different kinds of maps and improvement of road environment recognition efficiency and path planning accuracy.

In an optional implementation manner, the fusing the dynamic map and the static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment includes:

and fusing the dynamic map, the pure static map and the semi-static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

In an alternative embodiment, the method comprises:

detecting whether point cloud data are acquired at the current moment under the condition that the inertial navigation data are acquired at the current moment;

And if not, taking the point cloud data acquired last time before the current moment as the millimeter wave point cloud data acquired at the current moment.

Here, in view of the fact that the acquisition frequency of millimeter wave point cloud data is lower than that of inertial navigation data, the acquisition interval of the inertial navigation data is used as a data update interval, so that maps of various modes can be updated along with update of the inertial navigation data, and high-frequency map construction and map output can be achieved.

In an alternative embodiment, after the acquiring the inertial navigation data acquired by the inertial measurement unit at the current moment and the current frame point cloud acquired by the millimeter wave radar, the method includes:

detecting whether the inertial navigation data and the millimeter wave point cloud data are first frame data or not;

and initializing the vehicle state of the vehicle if the inertial navigation data and the millimeter wave point cloud data are both the first frame data.

In an alternative embodiment, the method further comprises:

and planning the running path of the vehicle according to the predicted environment map.

The disclosed embodiments also provide a vehicle data processing apparatus applied to a vehicle mounted with an inertial measurement unit and a millimeter wave radar, the apparatus including:

The data acquisition module is used for acquiring inertial navigation data and millimeter wave point cloud data of the vehicle, which are acquired at the current moment;

the data processing module is used for determining a point cloud indicated by the millimeter wave point cloud data, a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud based on the inertial navigation data;

the map construction module is used for constructing a dynamic map of the next moment by using the plurality of current frame dynamic points and constructing a static map of the current moment by using the plurality of current frame static points;

and the map fusion module is used for fusing the dynamic map and the static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

In an alternative embodiment, the data processing module is specifically configured to:

In an optional implementation manner, the data processing module is specifically configured to, when used for screening a plurality of current frame dynamic points and a plurality of current frame static points from the plurality of acquisition points based on the estimated speed:

In an alternative embodiment, the apparatus further comprises a vehicle speed calculation module for:

In an alternative embodiment, the map construction module is specifically configured to, when configured to construct a dynamic map of a next moment using the plurality of current frame dynamic points:

constructing a dynamic map of the environment in which the vehicle is located at the next time based on the predicted object position and predicted pose of each of the dynamic objects

In an alternative embodiment, the map construction module is specifically configured to, when configured to construct the static map of the current moment using the plurality of static points of the current frame:

In an alternative embodiment, the map fusion module is specifically configured to:

In an alternative embodiment, the apparatus further comprises a data validation module for:

In an alternative embodiment, the apparatus further comprises an initialization module, the initialization module being configured to:

In an alternative embodiment, the apparatus further comprises a path planning module for:

The embodiment of the disclosure also provides an electronic device, including: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the machine readable instructions when executed by the processor performing steps in any of the alternative embodiments of the vehicle data processing method described above.

The disclosed embodiments also provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor performs steps in any of the optional implementations of the vehicle data processing method described above.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the aspects of the disclosure.

The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for the embodiments are briefly described below, which are incorporated in and constitute a part of the specification, these drawings showing embodiments consistent with the present disclosure and together with the description serve to illustrate the technical solutions of the present disclosure. It is to be understood that the following drawings illustrate only certain embodiments of the present disclosure and are therefore not to be considered limiting of its scope, for the person of ordinary skill in the art may admit to other equally relevant drawings without inventive effort.

FIG. 1 is a flow chart of a vehicle data processing method provided by an embodiment of the present disclosure;

FIG. 2 is a flow chart of another vehicle data processing method provided by an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of one of the reference frames during movement of the vehicle;

FIG. 4 is a second schematic view of the reference system during the movement of the vehicle;

FIG. 5 is a schematic illustration of a vehicle data processing device provided in an embodiment of the present disclosure;

FIG. 6 is a second schematic diagram of a vehicle data processing device according to an embodiment of the disclosure;

Fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments. The components of the embodiments of the present disclosure, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present disclosure provided in the accompanying drawings is not intended to limit the scope of the disclosure, as claimed, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be made by those skilled in the art based on the embodiments of this disclosure without making any inventive effort, are intended to be within the scope of this disclosure.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.

The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

It has been found that in vehicle SLAM technology, multi-sensor fusion is mostly implemented, which, while having great advantages in positioning, sensing and path planning, faces challenges and limitations. The use of multiple sensors adds to the cost of the overall system, each sensor itself has a price, and additional engineering and labor costs may be required during integration and installation. In addition, multi-sensor fusion techniques require complex algorithms to be designed and implemented to handle data fusion between different sensors, such as important steps of data alignment, data calibration, and timing synchronization. In severe weather or environments where multiple dynamic obstacles exist, the collision of sensor data and information inconsistency may be more significant, and in order to obtain a high quality data fusion result, problems of inconsistency, calibration errors, noise, and the like in the data need to be dealt with. The multi-sensor fusion technique requires processing a large amount of data in a real-time environment and making high-quality real-time decisions, which presents a great challenge to the computational resources of the system and the efficiency of the algorithms used, and also requires employing techniques such as optimization algorithms, hardware acceleration, parallel computing, etc. to meet the real-time and performance requirements. It follows that there are many problems and challenges associated with implementing a vehicle SLAM via multi-sensor fusion.

Based on the above study, the disclosure provides a vehicle data processing method, which uses only inertial navigation data and millimeter wave point cloud data, can effectively reduce the number of sensors required to be used, reduce the vehicle cost, reduce the data volume required to be processed and the generated data volume, reduce the dependence on calculation performance and calculation resources, and has the advantages of few types of required processing data, small data volume, high map construction speed, greatly reduced dependence on complex algorithms and multiple algorithms, strong applicability and high robustness due to the fact that only inertial navigation data and millimeter wave point cloud data are used.

It can be appreciated that, if it is required to obtain personal information of a user, before using the technical solutions disclosed in the embodiments of the present disclosure, the user should be informed and authorized of the type, the usage range, the usage scenario, etc. of the personal information related to the present disclosure by an appropriate manner according to relevant legal regulations.

For example, in response to receiving an active request from a user, a prompt is sent to the user to explicitly prompt the user that the operation it is requesting to perform will require personal information to be obtained and used with the user. Thus, the user can autonomously select whether to provide personal information to software or hardware such as an electronic device, an application program, a server or a storage medium for executing the operation of the technical scheme of the present disclosure according to the prompt information.

As an alternative but non-limiting implementation, in response to receiving an active request from a user, the manner in which the prompt information is sent to the user may be, for example, a popup, in which the prompt information may be presented in a text manner. In addition, the popup window can also bear a selection control for the user to select to provide personal information for the electronic equipment and the like in a 'consent' or 'disagreement' mode.

It will be appreciated that the above-described process of notifying and obtaining user authorization is merely illustrative, and not limiting of the implementations of the present disclosure, and that other ways of satisfying relevant legal regulations may be applied to the implementations of the present disclosure.

For the sake of understanding the present embodiment, first, a detailed description will be given of a vehicle data processing method disclosed in the present embodiment, and an execution main body of the vehicle data processing method provided in the present embodiment is generally an electronic device having a certain computing capability, where in this embodiment, the electronic device includes, for example: the terminal device or server or other processing device, the terminal device may be a computing device, an in-vehicle device, etc. In some possible implementations, the vehicle data processing method may be implemented by way of a processor invoking computer readable instructions stored in a memory.

A vehicle data processing method provided in an embodiment of the present disclosure is described below.

It should be noted that, the vehicle data processing method provided by the embodiment of the present disclosure is applied to a vehicle, and can help the vehicle to implement a vehicle SLAM technology, and an inertial measurement unit and a millimeter wave radar are installed on the vehicle.

The inertial measurement unit Inertial Measurement Unit, abbreviated as IMU, is a device which is realized by using the law of inertia and is mainly used for detecting and measuring the acceleration and the angular velocity (or three-axis attitude angle) of the rotating motion of the object to be measured. Generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, where the accelerometers detect acceleration signals of the object in the carrier coordinate system on three independent axes, and the gyroscopes detect angular velocity signals of the carrier relative to the navigation coordinate system, measure angular velocity and acceleration of the object in three-dimensional space, and calculate the attitude of the object based on the angular velocity and acceleration.

Millimeter wave radar is a radar that operates in millimeter wave (millimeter wave) detection. Millimeter waves generally refer to the frequency domain (wavelength 1-10 mm) of 30-300 GHz.

As the millimeter wave radar used in the present application, a 4D millimeter wave radar may be preferably used, but is not limited thereto, and a 3D millimeter wave radar may also be used.

Referring to fig. 1, fig. 1 is a flowchart of a vehicle data processing method according to an embodiment of the disclosure. As shown in fig. 1, a vehicle data processing method provided by an embodiment of the present disclosure includes the steps of:

s101: and acquiring inertial navigation data and millimeter wave point cloud data of the vehicle, which are acquired at the current moment.

The inertial navigation data may be data collected by the inertial measurement unit, the millimeter wave point cloud data may be data collected by the millimeter wave radar, but not limited to, and in other embodiments, the inertial navigation data and the millimeter wave point cloud data may be obtained through other sensors or other data acquisition modes. For visual and convenient understanding, in the following embodiments, the inertial measurement unit is used for acquiring the inertial navigation data, and the millimeter wave radar is used for acquiring millimeter wave point cloud data.

After the vehicle is electrified, the inertia measurement unit and the millimeter wave radar can start to work to acquire data. As is well known, the data acquisition frequency of the inertial measurement unit is greater than that of the millimeter wave radar, and correspondingly, the data acquisition interval of the inertial measurement unit for acquiring two adjacent frames of data is smaller than that of the millimeter wave radar. In general, the data acquisition frequency of the millimeter wave radar is relatively fixed, the data acquisition frequency of the inertial measurement unit may be adjusted, and by way of example, the data acquisition frequency of the millimeter wave radar is generally 10hz, that is, one frame of point cloud data is acquired every 0.1 second, and the data acquisition frequency of the inertial measurement unit may be set to 100hz, that is, one frame of inertial navigation data is acquired every 0.01 second.

The current time, the next time and the last time are all data acquisition time of the inertial measurement unit, the last time, the current time and the next time are three continuous data acquisition time, and the current time is between the last time and the next time.

It is known that the inertial measurement unit can output acceleration and angular velocity of a carrier (such as a vehicle in the present embodiment), that is, inertial navigation data collected by the inertial measurement unit includes acceleration and angular velocity, and velocity, pose locus, and the like of motion of the carrier can be calculated by the inertial navigation data, and usually, the inertial measurement unit works for a long time to cause offset in the output data, but accuracy of the output data is relatively high in a short time, so that it is generally necessary to use the data output by the inertial measurement unit to perform compensation, correction, and the like on the data, and use the data of the inertial measurement unit to derive motion conditions of the carrier, that is, generally, from a start time, that is, after the carrier starts, derive from the data collected from a first frame.

Accordingly, in an alternative embodiment, after acquiring the inertial navigation data and the millimeter wave point cloud data, i.e. after step S101, the method further includes:

It can be understood that when the inertial measurement unit and the millimeter wave radar are used normally, the system on the vehicle needs to be initialized after the vehicle is started, at this time, the vehicle should be in a static state, after the system is powered on and runs a program, the first frame data respectively collected by the inertial measurement unit and the millimeter wave radar can be received, at this time, all states of the vehicle such as speed, acceleration, position and the like can be considered as zero, namely, the speed and angular velocity of the vehicle are all zero, and the vehicle is in an initial position and can also be considered as zero. It will be appreciated that since each subsequent frame of derivative calculation is based on the correlation of the previous frame of data, and is a Markov process, initialization is necessary, whereas normal vehicle use is typically initiated from vehicle standstill, so the conditions of the initial process are reasonable.

Therefore, in this step, in the case where the inertial navigation data and the millimeter wave point cloud data are acquired, it is possible to detect whether both data are first frame data, and if both data are first frame data, that is, initialize the vehicle state of the vehicle, and return each state value of the vehicle to zero.

Accordingly, after initializing the vehicle state of the vehicle, the step of acquiring the data acquired by the inertial measurement unit and the millimeter wave radar may be re-performed.

It will be appreciated that at the moment of the first frame of data, only one frame of data is present, and that there may be no dynamic objects, i.e. objects that are not moving, in the surrounding objects.

In contrast, if it is detected that at least one of the inertial navigation data and the millimeter wave point cloud data is non-first frame data, it can be considered that the initialization of the vehicle has been completed, the vehicle has been in motion, and further, the subsequent steps of processing and using the inertial navigation data and the millimeter wave point cloud data can be performed.

Wherein at least one of the inertial navigation data and the millimeter wave point cloud data is non-first frame data, and one possible case is that both the inertial navigation data and the millimeter wave point cloud data are non-first frame data, and one possible case is that the inertial navigation data are non-first frame data, but the millimeter wave point cloud data may still be first frame data, and as described in the foregoing, the data acquisition interval of the inertial measurement unit is smaller than the data acquisition interval of the millimeter wave radar, so after both the inertial measurement unit and the millimeter wave radar acquire the first frame data, the second data acquisition time point, the third data acquisition time point of the inertial measurement unit may be preferentially reached, but at this time, the millimeter wave radar has not yet reached the second data acquisition time point, and therefore, when the inertial measurement unit has acquired the second frame data, the third frame data, there may be a second data acquisition time point of the millimeter wave radar, and therefore, for the inertial measurement unit and the millimeter wave point data that can be acquired, the inertial measurement unit has not yet reached the second data acquisition time point.

S102: and determining a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud based on the inertial navigation data.

In the step, after the inertial navigation data and the millimeter wave point cloud data are acquired, the motion state of the vehicle can be deduced through the inertial navigation data, the acquired point cloud can be resolved through the millimeter wave point cloud data, further, the acquired points in the point cloud can be screened through the deduced motion state of the vehicle, each acquired point is traversed, and further, a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud are screened out.

The current frame dynamic point may refer to a collection point of a moving object detected by the millimeter wave radar for a moving object existing in an environment where the vehicle is located, and it is understood that one dynamic object may detect a plurality of current frame dynamic points. Similarly, the current frame stationary point may refer to a collection point of a non-moving object detected by the millimeter wave radar for a non-moving object existing in an environment where the vehicle is located, and it is understood that one stationary object may detect a plurality of the current frame stationary points. Further, the non-moving object herein may refer to an object that is completely stationary, or an object that has a tendency to move, or a very small moving speed, and, for example, in an actual environment, the moving speed of the vehicle or other moving objects such as moving vehicles is relatively fast, while the moving speed of pedestrians is relatively slow with respect to the vehicle or other moving vehicles, and then the moving speed of pedestrians is relatively stationary in the case where the data acquisition time interval of the inertial measurement unit and the millimeter wave radar is small, and then, for example, a vehicle that is parked or started, or a vehicle that is waiting for a person to get on or get off is slow or does not move at all in the current period, and therefore, in the case where the data acquisition time interval is small, the moving object may be considered to be relatively stationary, while in the actual environment, the object such as a tree, a building, a traffic light is completely stationary.

Accordingly, in an alternative embodiment, for each acquisition point, the acquisition point may be divided according to the speed of the acquisition point, where the speed is greater than a certain threshold, and the acquisition point is used as a dynamic point of the current frame, and the speed is less than a certain threshold, and the acquisition point is used as a static point of the current frame. Correspondingly, the speed of the acquisition point can be Doppler speed acquired by the millimeter wave radar or actual speed calculated by actual.

S103: and constructing a dynamic map of the next moment by using the plurality of current frame dynamic points, and constructing a static map of the current moment by using the plurality of current frame static points.

In this step, after the plurality of current frame dynamic points and the plurality of current frame static points are screened out, for the current frame dynamic points, the state information of the current frame dynamic points at the next moment can be deduced through the state information, such as the position and the gesture, of the current frame dynamic points, and further the state information of the next moment can be further used for constructing a dynamic map of the plurality of current frame dynamic points at the next moment, and similarly, for the current frame static points, the static map at the current moment can be constructed through the state information of the current frame static points.

As described above, the current frame static point includes a completely stationary acquisition point and has a certain motion condition, but is relatively a stationary acquisition point, so that when the acquisition point is divided, the current frame static point can be further distinguished, so as to obtain a finer and accurate map, and facilitate subsequent use of the vehicle for path planning and obstacle avoidance, thereby achieving a better effect.

Accordingly, in an alternative embodiment, for constructing the static map at the current time using the plurality of current frame static points, it is also possible to identify a pure static point and a semi-static point from the plurality of current frame static points, then construct the pure static map using the identified pure static points, and construct the semi-static map using the identified semi-static points.

S104: and fusing the dynamic map and the static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

In this step, after the dynamic map and the static map are obtained, the dynamic map and the static map may be preprocessed, aligned, or the like, and then fused to be used as a predicted environment map of the environment where the vehicle is located at the next moment.

Accordingly, in a possible real-time manner, in the case of identifying the purely static point and the semi-static point, and constructing the purely static map and the semi-static map, the dynamic map, the purely static map, and the semi-static map may be fused in step S104, so as to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

Thus, the predicted environment map of the next moment is obtained, the road condition of the next moment can be known in advance, and the vehicle can be helped to control the vehicle and deal with the road condition in advance.

Accordingly, in an alternative embodiment, the method further comprises:

Here, the predicted environment map at the next time is used for planning the running path for the vehicle, so that the obstacle avoidance can be performed in advance for the running of the vehicle, and the running safety can be improved.

According to the vehicle data processing method provided by the embodiment of the disclosure, only inertial navigation data and millimeter wave point cloud data are used, so that the number of sensors required to be used can be effectively reduced, the vehicle cost is reduced, the used data volume is small, the data volume required to be processed and the generated data volume are reduced, the dependence on calculation performance and calculation resources is reduced, the calculation effect is improved, dynamic points and static points are screened out through the inertial navigation data and the millimeter wave point cloud data, a dynamic map at the next moment and a static map at the current moment are further constructed, a prediction environment map at the next moment is obtained through fusion, the road condition at the next moment can be known in advance, the vehicle control and road condition coping are facilitated to be carried out in advance, the variety of the data required to be processed in the process is small, the map construction speed is high, the dependence on complex algorithms and multiple algorithms is reduced, the applicability is high, and the robustness is high.

Referring to fig. 2, fig. 2 is a flowchart of another vehicle data processing method according to an embodiment of the disclosure. As shown in fig. 2, the vehicle data processing method provided by the embodiment of the disclosure includes the following steps:

s201: and detecting whether point cloud data are acquired at the current moment under the condition that the inertial navigation data are acquired at the current moment.

It will be appreciated that, as described above, the data acquisition interval of the inertial measurement unit for acquiring the two adjacent frames of data is smaller than the data acquisition interval of the millimeter wave radar for acquiring the two adjacent frames of data, so that the current moment when the inertial measurement unit acquires the inertial navigation data may not be the moment when the inertial measurement unit acquires the millimeter wave radar, that is, at the current moment, if the millimeter wave radar does not acquire data, the millimeter wave point cloud data cannot be acquired.

Therefore, in this step, after the inertial measurement unit and the millimeter wave radar are powered on, that is, start to work, the inertial measurement unit and the millimeter wave radar may be monitored in real time, and correspondingly, when the inertial measurement unit is detected to acquire the inertial navigation data at the current time, the data acquisition condition of the millimeter wave radar may be detected to detect whether the millimeter wave radar acquires point cloud data at the current time, that is, whether the millimeter wave radar has work for acquiring data at the current time.

S202: and if not, taking the point cloud data acquired last time before the current moment as the millimeter wave point cloud data acquired at the current moment.

In this step, if the millimeter wave radar does not acquire point cloud data at the current time, that is, does not acquire data at the current time, it may be considered that a time node at which the millimeter wave radar acquires data has not yet arrived, in which case, the point cloud data acquired last time by the millimeter wave radar before the current time, that is, the last frame of point cloud data acquired last time, may be used as the millimeter wave point cloud data acquired at the current time, so that the latest data may be obtained at high frequency, and after corresponding processing, a corresponding result may be output at high frequency.

S203: and acquiring inertial navigation data and millimeter wave point cloud data of the vehicle, which are acquired at the current moment.

S204: and determining a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud based on the inertial navigation data.

S205: and constructing a dynamic map of the next moment by using the plurality of current frame dynamic points, and constructing a static map of the current moment by using the plurality of current frame static points.

S206: and fusing the dynamic map and the static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

The descriptions of step S203 to step S206 may refer to the descriptions of step S101 to step S104, and may solve the same technical problems and achieve the same technical effects, which are not described herein.

The steps described above are further described below in connection with some embodiments.

In an alternative embodiment, step S204 includes:

Here, the stored previous actual speed of the vehicle obtained by calculation at the previous moment may be obtained first, and then the inertial navigation data, that is, the acceleration and the angular speed of the vehicle included in the inertial navigation data, are combined to derive the estimated speed of the vehicle at the current moment, that is, the estimated speed of the vehicle at the current moment may be derived by combining the inertial navigation data and a corresponding inertial navigation calculation method.

Then, for the millimeter wave point cloud data, a plurality of acquisition points acquired by the millimeter wave radar can be obtained from the millimeter wave point cloud data through analysis and other processing, then the estimated speed of the vehicle can be used for traversing the plurality of acquisition points, and each acquisition point is compared one by one, so that the current frame dynamic point and the current frame static point are screened out.

It can be understood that, from the millimeter wave point cloud data, besides the acquisition point can be analyzed, the doppler velocity and the point position of the acquisition point can also be obtained.

For the screening of the plurality of current frame dynamic points and the plurality of current frame static points from the plurality of sampling points, as described above, the sampling points with the same doppler velocity as the estimated velocity can be classified into the current frame dynamic points by comparing the doppler velocity of the sampling points with the estimated velocity, for example, the matching may be the same or the difference between the two sampling points is within a certain range, and the rest of the sampling points may be considered as the current frame static points. Accordingly, in different embodiments, the two speeds may be calculated and compared by different specific algorithms based on the requirements of different accuracies.

In a specific application scenario, for the filtering of the plurality of current frame dynamic points and the plurality of current frame static points from the plurality of collection points based on the estimated speed, the doppler speed and the point position of each collection point may be obtained from the millimeter wave point cloud data first, that is, the doppler speed and the point position of each collection point obtained in the parsing process of the millimeter wave point cloud data may be obtained, for each collection point, the estimated speed and the point position and the doppler speed of the collection point may be used to calculate a speed error parameter of the collection point, and if the speed error parameter is greater than a preset threshold, the collection point is determined to be the current frame dynamic point, and if the speed error parameter is less than or equal to the preset threshold, the collection point is determined to be the current frame static point.

For example, referring to fig. 3 and 4, fig. 3 is a first reference frame diagram during the movement of the vehicle, and fig. 4 is a second reference frame diagram during the movement of the vehicle. As shown in fig. 3 and 4, in the embodiment of the present disclosure, the vehicle 300 has a body coordinate system b-xyz (where the z-axis is not shown), which may also be considered as the coordinate system of the inertial unit, i.e., the IMU coordinate system, and correspondingly, has a global coordinate system W-xyz (where the z-axis is not shown) for the environment in which the vehicle 300 is located, and at the initial time, i.e., the time when the vehicle 300 has not yet moved, the body coordinate system of the vehicle 300 may be considered to coincide with the global coordinate system, i.e., the initial time, the body coordinate system of the vehicle 300 may be considered as the global coordinate system, but the global coordinate system is stationary, and the body coordinate system moves with the vehicle 300.

In the present embodiment, when estimating the speed state of the vehicle 300, the speed state of the vehicle 300 is based on a vehicle body coordinate system (which may also be referred to as an IMU coordinate system), and the speed state of the vehicle 300 mainly includes the longitudinal linear speed of the vehicle 300 in the traveling direction in the traveling planeAnd->Vertical transverse linear velocity +.>According to the Ackerman vehicle motion model, the lateral speed of the vehicle coordinate system +.>0, therefore, the speed state and position state to be estimated for the vehicle 300 can be defined as:

wherein,represents the longitudinal linear velocity of the vehicle 300 in the body coordinate system b at time t, +.>Represents the angular velocity of the vehicle 300 in the body coordinate system b at time t, +.>Representing the position of the vehicle 300 in the global coordinate system W at time t,/for example>Representing the position of the vehicle 300 in the x-axis direction in the global coordinate system W at time t,/->Representing the position of the vehicle 300 in the y-axis direction in the global coordinate system W at time t,/->The pose of the vehicle 300 in the global coordinate system W at time t is shown.

It is known that the body coordinate system changes continuously as the vehicle moves, but for each moment, the body coordinate system and the world coordinate system can be transformed by the movement of the vehicle or by points in the acquired point cloud, such as dynamic points, i.e. the transformation relationship between the body coordinate system and the world coordinate system is readily available, for example, by a rotation transformation matrix.

As described above, at the initial time, each state of the vehicle is 0, i.e., the speed of the vehicle Here, the initial time is represented by a time t of 0, but the present invention is not limited to this, and in other embodiments, the initial time may be represented by a time t of 1. Accordingly, whichever way the initial time is indicated, it can be considered that the first frame data can be acquired at the initial time.

In this disclosure, the estimated speed, the actual speed, or other speeds at each time may be considered as the longitudinal linear speed of the vehicle or the object in the x-axis direction under the vehicle body coordinate system.

Correspondingly, when data are acquired, namely, at the time t, the inertial measurement unit acquires current inertial navigation data at the time t, and after the millimeter wave radar acquires current millimeter wave point cloud data at the time t, if the data are first frame data, namely, the first frame data acquired at the initial time, the vehicle state of the vehicle is initialized, and if the data are not the first frame data, the vehicle state is directly calculated.

For inertial navigation data, the velocity at the current instant, i.e. time t, can be estimated using the integration of the acceleration in the x-axis direction of the IMU and its angular velocity around the z-axis, respectively:

/>

Wherein,representing the estimated speed of the vehicle at time t in the body coordinate system b,/>Representing the prior actual speed of the vehicle calculated in the body coordinate system b at time t-1,/->A rotation matrix representing the IMU coordinate system (i.e. the car body coordinate system) at time t-1, +.>Represents acceleration of the vehicle in the x-axis direction of the vehicle body coordinate system b from time t-1 to time t,rotation matrix representing IMU coordinate system (i.e. car body coordinate system) at time t, +.>The angular velocity of the vehicle in the z-axis direction of the vehicle body coordinate system b from time t-1 to time t is shown.

In calculating the estimated speed of the vehicleThereafter, the position and Doppler velocity of each acquisition point in the point cloud are combinedAn error function can be constructed:

wherein e ⁱ A speed error parameter representing the i-th acquisition point,indicating the Doppler velocity of the ith acquisition point in the car body coordinate system at time t,/->Represents the point position of the ith acquisition point in the vehicle body coordinate system at the time T, sigma represents a preset threshold, wherein T (e) ⁱ ) An acquisition point of 0 can be divided into a current frame dynamic point, T (e ⁱ ) The acquisition point of 1 can be divided into static points of the current frame, for example, M dynamic points of the current frame and N static points of the current frame.

In an alternative embodiment, after step S204, the method includes:

Here, after obtaining the plurality of current frame static points, the actual speed of the vehicle may be calculated in combination with information of the plurality of current frame static points.

Exemplary, following the above example, the estimated speed is obtainedAfter the static points of the current frame are divided, N static points of the current frame can be used, and the actual speed of the vehicle can be solved by combining corresponding algorithms, such as iterative solution (e.g. RANSAC algorithm) and the like, for example, the following solving function can be used:

wherein i represents an i-th current frame static point among the N current frame static points,indicating the Doppler velocity of the ith current frame stationary point S in the car body coordinate system at time t,/>Representing that the ith current frame static point S is on the car at the time tPoint location in the volumetric coordinate system, +.>The current actual speed of the vehicle in the vehicle body coordinate system at the time t, which needs to be solved, is represented.

Further, by the above equation, the least square method is used to solve the least square problem to obtain the accurate current actual speed of the vehicle.

In an alternative embodiment, for constructing the dynamic map of the next moment by using the plurality of current frame dynamic points in step S205, for each current frame dynamic point, the current actual speed, the point position of the current frame dynamic point, and the doppler speed may be used first, the current dynamic speed and the current dynamic angular speed of the current frame dynamic point may be calculated, then, according to the distance between any two current frame dynamic points and the current dynamic speed, the clustering process may be performed on the plurality of current frame dynamic points, to obtain at least one dynamic object, and the current object position and the current object posture of the dynamic object under the vehicle body coordinate system, then, using the time interval between the current moment and the next moment, and the current object position and the current actual speed, calculating the predicted object position of the dynamic object at the next moment, and using the time interval, the current object posture of the dynamic object, and the current dynamic angular speed corresponding to the dynamic object, calculating the current dynamic object position of the dynamic object at the next moment, and predicting the predicted object posture of the dynamic object at the next moment, and constructing the predicted object at the current object position and the predicted object position at the next moment, based on the predicted object position and the predicted object at the next moment.

The method comprises the steps of carrying out clustering processing on a plurality of current frame dynamic points according to the distance between any two current frame dynamic points and the current dynamic speed to obtain at least one dynamic object, dividing the points with the same or similar current dynamic speed into candidate classes in a clustering mode, and respectively calculating the distance between the two current frame dynamic points for the current frame dynamic points in the candidate classes.

In actual use, the current actual speed of the vehicle is calculatedThen, the current dynamic speed of each current frame dynamic point can be calculated by combining the point position and Doppler speed of each current frame dynamic point by using the following formula respectively>And the current dynamic angular velocity +.>

Where i represents the i-th current frame dynamic point of the M current frame static points,representing the point position of the ith current frame dynamic point D in the car body coordinate system at time t, a +.>Indicating the Doppler velocity of the ith current frame dynamic point D in the car body coordinate system at time t,/ >Representation->In-carComponent of x-axis direction in the body coordinate system, < >>Representation->A component in the y-axis direction in the vehicle body coordinate system.

Definition of each dynamic object D _L Is as followsInitial posture is +.>Based on the calculation result, the predicted object position +.of the dynamic object at the next time, i.e., time t+1, can be performed>And predict posture->And (3) predicting:

further, the predicted object position and predicted attitude of the dynamic object can be predicted in real time, and the predicted dynamic map can be output at the same time. On this basis, a high-frequency predictive dynamic map can be updated and output, the map is updated along with the arrival of IMU data until the next frame of point cloud data arrives, and the dynamic map is reset.

According to the vehicle data processing method provided by the embodiment of the disclosure, only inertial navigation data and millimeter wave point cloud data are used, so that the number of sensors required to be used can be effectively reduced, the vehicle cost is reduced, the used data volume is small, the data volume required to be processed and the generated data volume are reduced, the dependence on calculation performance and calculation resources is reduced, is improved in calculation effect, the last acquired point cloud data can be used when the millimeter wave radar is not in monitoring data, then the dynamic point and the static point are screened out through the inertial navigation data and the millimeter wave point cloud data, a dynamic map at the next moment and a static map at the current moment are further constructed, a prediction environment map at the next moment is obtained through fusion, road conditions at the next moment can be known in advance, vehicle control and road condition coping are facilitated to be carried out in advance, the variety of the required processing data is small in the process, the map construction speed is high, dependence on complex algorithms and multiple algorithms is reduced, the method is high in applicability, and the robustness is high.

It will be appreciated by those skilled in the art that in the above-described method of the specific embodiments, the written order of steps is not meant to imply a strict order of execution but rather should be construed according to the function and possibly inherent logic of the steps.

Based on the same inventive concept, the embodiments of the present disclosure further provide a vehicle data processing device corresponding to the vehicle data processing method, and since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to that of the vehicle data processing method in the embodiments of the present disclosure, the implementation of the device may refer to the implementation of the method, and the repetition is omitted.

Referring to fig. 5 and 6, fig. 5 is a schematic diagram of a vehicle data processing device according to an embodiment of the disclosure, and fig. 6 is a schematic diagram of a vehicle data processing device according to an embodiment of the disclosure. Here, the vehicle data processing device is applied to a vehicle mounted with an inertial measurement unit and a millimeter wave radar, and the vehicle data processing device may be embedded in a vehicle-mounted terminal of the vehicle, may be a device independent from the vehicle-mounted terminal, may be disposed in a background or a cloud server, and may communicate with the vehicle-mounted terminal. As shown in fig. 5, the vehicle data processing apparatus 500 provided by the embodiment of the present disclosure includes:

the data acquisition module 510 is configured to acquire inertial navigation data and millimeter wave point cloud data of the vehicle, which are acquired at a current moment.

The data processing module 520 is configured to determine, based on the inertial navigation data, a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points included in the point cloud.

The map construction module 530 is configured to construct a dynamic map of a next moment using the plurality of current frame dynamic points, and construct a static map of the current moment using the plurality of current frame static points.

And the map fusion module 540 is configured to fuse the dynamic map and the static map to obtain a predicted environment map of the environment where the vehicle is located at the next moment.

In an alternative embodiment, the data processing module 520 is specifically configured to:

In an alternative embodiment, the data processing module 520 is specifically configured to, when configured to screen a plurality of current frame dynamic points and a plurality of current frame static points from the plurality of acquisition points based on the estimated speed:

In an alternative embodiment, as shown in fig. 6, the vehicle data processing apparatus 500 further includes a vehicle speed calculation module 550, and the vehicle speed calculation module 550 is configured to:

In an alternative embodiment, the map construction module 530 is specifically configured to, when configured to construct a dynamic map of a next moment using the plurality of current frame dynamic points:

In an alternative embodiment, the map construction module 530 is specifically configured to, when configured to construct the static map of the current time using the plurality of static points of the current frame:

In an alternative embodiment, the map fusion module 540 is specifically configured to:

In an alternative embodiment, as shown in fig. 6, the vehicle data processing apparatus 500 further includes a data confirmation module 560, the data confirmation module 560 being configured to:

In an alternative embodiment, as shown in fig. 6, the vehicle data processing apparatus 500 further includes an initialization module 570, where the initialization module 570 is configured to:

In an alternative embodiment, as shown in fig. 6, the vehicle data processing device 500 further includes a path planning module 580, and the path planning module 580 is configured to:

The process flow of each module in the apparatus and the interaction flow between the modules may be described with reference to the related descriptions in the above method embodiments, which are not described in detail herein.

According to the vehicle data processing device provided by the embodiment of the disclosure, only inertial navigation data and millimeter wave point cloud data are used, so that the number of sensors required to be used can be effectively reduced, the vehicle cost is reduced, the used data volume is small, the data volume required to be processed and the generated data volume are reduced, the dependence on calculation performance and calculation resources is reduced, the calculation effect is improved, dynamic points and static points are screened out through the inertial navigation data and the millimeter wave point cloud data, a dynamic map at the next moment and a static map at the current moment are further constructed, a prediction environment map at the next moment is obtained through fusion, the road condition at the next moment can be known in advance, the vehicle control and road condition coping are facilitated to be carried out in advance, the variety of the data required to be processed in the process is small, the map construction speed is high, the dependence on complex algorithms and multiple algorithms is reduced, the applicability is high, and the robustness is high.

Referring to fig. 7, fig. 7 is a schematic structural diagram of a computer device according to an embodiment of the disclosure. Corresponding to the vehicle data processing method shown in fig. 1 and 2, the embodiment of the present disclosure further provides a computer apparatus 700 including: a processor 710, a memory 720 and a bus 730, said memory 720 storing machine readable instructions executable by said processor 710, said processor 710 and said memory 720 communicating via bus 730 when the electronic device 700 is running, said machine readable instructions when executed by said processor 710 performing the steps of the vehicle data processing method as shown in fig. 1 and 2.

The Memory 720 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

Processor 710 may be an integrated circuit chip with signal processing capabilities. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the electronic device 700. In other embodiments of the present application, electronic device 700 may include more or fewer components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The disclosed embodiments also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a vehicle data processing method as shown in fig. 1 and 2.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus and device described above may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again. In several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus, device, and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed.

In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present disclosure, and are not intended to limit the scope of the disclosure, but the present disclosure is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, it is not limited to the disclosure: any person skilled in the art, within the technical scope of the disclosure of the present disclosure, may modify or easily conceive changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features thereof; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A vehicle data processing method, characterized in that the method comprises:

2. The method of claim 1, wherein the determining, based on the inertial navigation data, a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud, comprises:

3. The method of claim 2, wherein the screening the plurality of current frame dynamic points and the plurality of current frame static points from the plurality of acquisition points based on the estimated speed comprises:

4. The method of claim 2, wherein after the determining, based on the inertial navigation data, a point cloud indicated by the millimeter wave point cloud data, and a plurality of current frame dynamic points and a plurality of current frame static points contained in the point cloud, the method further comprises:

5. The method of claim 4, wherein constructing a dynamic map of a next moment using the plurality of current frame dynamic points comprises:

6. The method of claim 1, wherein the constructing a static map of the current time using the plurality of current frame static points comprises:

7. The method of claim 6, wherein the fusing the dynamic map and the static map to obtain a predicted environment map of the environment in which the vehicle is located at the next time comprises:

8. The method according to claim 1, characterized in that the method comprises:

9. The method according to claim 1, characterized in that after the acquisition of inertial navigation data acquired by the inertial measurement unit at the current moment and the current frame point cloud acquired by the millimeter wave radar, the method comprises:

10. The method according to claim 1, wherein the method further comprises:

11. A vehicle data processing apparatus, characterized in that the apparatus comprises:

12. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the vehicle data processing method according to any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the vehicle data processing method according to any one of claims 1 to 10.