Vehicle variable acceleration determination planning method, product, equipment and medium

Abstract

The invention relates to the technical field of automatic driving, in particular to a vehicle variable acceleration determination planning method, a product, equipment and a medium. The method comprises the steps that the real-time position of a current vehicle is acquired, and a plurality of bumpy road sections of a current road where the current vehicle is located are determined from a preset road section database according to the real-time position; determining a target bumpy road section closest to the real-time position on the current lane from the plurality of bumpy road sections, and acquiring target point cloud data on the current road; according to the plurality of bumpy road sections and the target point cloud data, judging whether adjacent lanes meet preset lane changing conditions or not; and if the adjacent lane does not meet the preset lane changing condition, determining a target acceleration based on the target bumpy road section, and controlling the current vehicle to slow down according to the target acceleration. Accurate regulation and control of the vehicle driving acceleration can be achieved, and the driving safety and comfort are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a vehicle variable acceleration determination planning method, product, device and medium. Background Art

[0002] With the rapid progress of autonomous driving technology, the level of vehicle intelligence is constantly improving. Autonomous driving vehicles can collect and process road and environmental data in real time through various sensors. However, in the face of complex and changing road conditions, especially those with bumpy sections, how to accurately control vehicle acceleration to ensure driving comfort and safety is still a technical problem that needs to be solved. Summary of the invention

[0003] In order to solve the problem of inaccurate acceleration control when a vehicle is driving automatically through a bumpy road in the prior art, the present application provides a vehicle variable acceleration determination planning method, product, device and medium.

[0004] In a first aspect, the present application provides a vehicle variable acceleration determination planning method, which adopts the following technical solution: A vehicle variable acceleration determination planning method, comprising: Acquire the real-time position of the current vehicle, and determine, according to the real-time position, from a preset road section database, a number of bumpy sections of the current road where the current vehicle is located, wherein the current road includes a current lane where the current vehicle is located and an adjacent lane in the same direction as the current lane; Determine a target bumpy road section on the current lane that is closest to the real-time position from the plurality of bumpy road sections, and obtain target point cloud data on the current road, the target point cloud data including point cloud data from a position that is a preset distance from the real-time position in a direction opposite to the current vehicle driving direction to the target bumpy road section; determining, according to the plurality of bumpy road sections and the target point cloud data, whether the adjacent lane meets a preset lane change condition; If the adjacent lane does not satisfy the preset lane change condition, a target acceleration is determined based on the target bumpy road section, and the current vehicle is controlled to decelerate according to the target acceleration.

[0005] By adopting the above technical solution, the real-time position of the current vehicle is obtained, which can ensure that subsequent decisions are based on the latest vehicle status. By querying the preset road section database, the vehicle can predict the bumpy section on the road ahead, which helps the vehicle to take safety measures such as deceleration in advance and reduce safety hazards caused by bumps. The nearest target bumpy section is determined and the target point cloud data is obtained, which provides the vehicle with a detailed perception of the road environment ahead, which helps to more accurately judge the road conditions and make decisions. Combining multi-source data (bumpy section information and point cloud data), the vehicle can intelligently judge whether the adjacent lane is suitable for lane changing, avoiding traffic accidents that may be caused by blind lane changing, and further improving driving safety. When it is impossible to change lanes, the vehicle can adaptively adjust the acceleration to pass the bumpy section in the best state, reducing vehicle vibration and passenger discomfort caused by bumps, improving driving stability, and realizing precise control of the vehicle's driving acceleration, significantly improving driving safety and comfort.

[0006] In a preferred example, the present application may be further configured as follows: judging whether the adjacent lane meets the preset lane change condition according to the plurality of bumpy road sections and the target point cloud data includes: Determining whether a road section adjacent to the target bumpy road section in the same direction is a bumpy road section according to the plurality of bumpy road sections; Determining whether the current vehicle can change lanes safely according to the target point cloud data; If the adjacent road section in the same direction of the target bumpy road section is not a bumpy road section, and the current vehicle can change lanes safely, then determining that the adjacent lane meets the preset lane change condition; If the adjacent road section in the same direction of the target bumpy road section is a bumpy road section, or the current vehicle cannot change lanes safely, it is determined that the adjacent lane does not meet the preset lane change condition.

[0007] By adopting the above technical solution and considering whether the target bumpy road section and its adjacent road section in the same direction are bumpy roads, the vehicle can choose a smoother and safer driving path. The point cloud data provides detailed three-dimensional information about the vehicle's surrounding environment, including the position, speed and driving direction of other vehicles in adjacent lanes. By analyzing this information, the vehicle can accurately determine whether there is a collision risk during the lane change process, thereby avoiding accidents. The combination of the two key steps of road condition assessment and safe lane change judgment enables the vehicle to make intelligent decisions based on real-time data and preset conditions, which helps the vehicle better cope with complex and changeable road environments, reduce emergency braking or lane change operations caused by emergencies, and improve the overall driving stability and safety.

[0008] In a preferred example, the present application may be further configured as follows: judging whether the current vehicle can change lanes safely according to the target point cloud data includes: Acquiring historical lane change data of the current vehicle, the historical lane change data including lane change duration and lane change distance; Determine whether the distance between the real-time position and the target bumpy road section is greater than the lane change distance, and if so, determine the lane change area as an area corresponding to the lane change distance extending from the real-time position along the adjacent lane in the current vehicle driving direction; Predicting, based on the target point cloud data, whether there are other vehicles passing through the lane change area on the adjacent lane within the lane change duration; If yes, it is determined that the current vehicle cannot change lanes safely.

[0009] By adopting the above technical solution and combining the historical lane change data of the current vehicle (such as lane change duration and lane change distance), the feasibility of the vehicle changing lanes under current road conditions can be more accurately evaluated. After confirming that the distance from the real-time position to the target bumpy road section is greater than the lane change distance, the lane change area is further defined, and the vehicle dynamics on adjacent lanes in the area are predicted based on the target point cloud data. When it is predicted that other vehicles will pass through the lane change area within the lane change duration, the vehicle can make a timely judgment that it cannot change lanes safely, thereby avoiding potential collision risks.

[0010] In a preferred example, the present application can be further configured as follows: the method further includes: Obtain vehicle vibration data of vehicles that have historically passed through each road; Comparing the vehicle vibration data with a preset bumpy condition, taking a road section corresponding to the vehicle vibration data that meets the preset bumpy condition as an initial bumpy road section, and obtaining a plurality of initial bumpy road sections; Obtain point cloud data corresponding to each of the multiple initial bumpy road sections, input the point cloud data corresponding to each of the multiple initial bumpy road sections into a preset recognition model, obtain multiple bumpy road sections represented by longitude and latitude coordinates, and store the multiple bumpy road sections represented by longitude and latitude coordinates in a preset road section database.

[0011] By adopting the above technical solution, the vehicle vibration data of vehicles that have historically passed through various roads are obtained, and these data are compared with the preset bumpy conditions, so that accurate identification of bumpy sections can be achieved. The sections corresponding to the vehicle vibration data that meet the preset bumpy conditions are used as initial bumpy sections, and the point cloud data corresponding to these sections are obtained. The point cloud data corresponding to multiple initial bumpy sections are input into the preset recognition model, and the point cloud data are deeply analyzed. Finally, multiple bumpy sections represented by longitude and latitude coordinates are obtained, which improves the accuracy and reliability of identification. The bumpy sections are stored in the preset section database, forming a data resource that can be queried and used in real time.

[0012] In a preferred example, the present application may be further configured as follows: the vehicle vibration data includes vibration frequency and amplitude; The vehicle vibration data is compared with the preset bumpy condition, and the road section corresponding to the vehicle vibration data that meets the preset bumpy condition is used as the initial bumpy road section to obtain multiple initial bumpy road sections, including: Comparing the vibration frequency in the vehicle vibration data corresponding to each road with a preset vibration frequency threshold, and comparing the amplitude in the vehicle vibration data with a preset amplitude threshold; A section in each road whose vibration frequency exceeds the preset vibration frequency threshold and whose amplitude exceeds the preset amplitude threshold is taken as an initial bumpy section, so as to obtain a plurality of initial bumpy sections.

[0013] By adopting the above technical solution, the vibration frequency and amplitude in the vehicle vibration data are compared with the preset vibration frequency threshold and amplitude threshold respectively, thereby realizing a multi-dimensional evaluation of the road bumps, which can more comprehensively reflect the actual conditions of the road surface and improve the accuracy and reliability of bumpy road section identification.

[0014] In a preferred example, the present application may be further configured as follows: determining the target acceleration based on the target bumpy road section includes: Acquiring the current speed of the current vehicle, and determining a target speed for passing through the target bumpy road section according to the preset road section database; Determining a distance value from the real-time position to the target bumpy road section; The target acceleration is calculated according to the current speed, the target speed and the distance value.

[0015] By adopting the above technical solution, the current speed of the current vehicle is obtained, and the target speed for passing the target bumpy section is determined according to the preset road section database. The system can plan a smooth transition from the current speed to the target speed, which helps to reduce the discomfort caused by sudden acceleration or deceleration, improve the passenger riding experience, and may reduce the energy consumption and wear of the vehicle. Combining the current speed, target speed and distance value, the system can accurately calculate the required acceleration to ensure that the vehicle can reach the target speed before reaching the bumpy section.

[0016] In a preferred example, the present application may be further configured as follows: determining the target speed for passing through the target bumpy road section according to the preset road section database includes: Determining the bumpiness level of the target bumpy road section from a preset road section database; A target speed for passing through the target bumpy road section is determined according to the bumpiness level.

[0017] By adopting the above technical solution, the bumpiness level of the target bumpy section is determined from the preset section database, and different speed planning strategies can be formulated for different degrees of bumpy road conditions, which helps to reduce driving instability and potential risks caused by improper speed. Whether it is slight bumps or severe bumps, the system can adjust the target speed according to the specific situation to ensure that the vehicle can pass through the bumpy section at the most suitable speed.

[0018] In a second aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program. When the computer program is executed by a processor, the vehicle variable acceleration determination planning method as described in any one of the first aspects is implemented.

[0019] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: one or more processors; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the vehicle variable acceleration determination planning method as described in any one of the first aspects.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program thereon, which, when executed in a computer, causes the computer to execute the vehicle variable acceleration determination planning method as described in any one of the first aspects.

[0021] In summary, this application includes the following beneficial technical effects: By obtaining the real-time position of the current vehicle, this application can ensure that subsequent decisions are based on the latest vehicle status. By querying the preset road section database, the vehicle can predict the bumpy section on the road ahead, which helps the vehicle to take safety measures such as deceleration in advance and reduce safety hazards caused by bumps. It determines the nearest target bumpy section and obtains target point cloud data, providing the vehicle with a detailed perception of the road environment ahead, which helps to more accurately judge the road conditions and make decisions. By combining multi-source data (bumpy section information and point cloud data), the vehicle can intelligently determine whether the adjacent lane is suitable for lane changing, avoiding traffic accidents that may be caused by blind lane changing, and further improving driving safety. When it is impossible to change lanes, the vehicle can adaptively adjust the acceleration to pass the bumpy section in the best state, reducing vehicle vibration and passenger discomfort caused by bumps, improving driving stability, and realizing precise control of the vehicle's driving acceleration, significantly improving driving safety and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a vehicle variable acceleration determination planning method provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figure 1 -Attached Figure 2 This application is described in further detail.

[0024] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0027] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0028] The present application embodiment provides a vehicle variable acceleration determination planning method, such as Figure 1As shown, the method provided in the embodiment of the present application is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a car console, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S104, wherein: S101. Obtain the real-time position of the current vehicle of the automatic driving, and determine a number of bumpy sections of the current road where the current vehicle is located from a preset road section database according to the real-time position, where the current road includes a current lane where the current vehicle is located and an adjacent lane in the same direction as the current lane.

[0029] In this embodiment, a positioning module (such as GPS, RTK-GPS) is pre-installed on the current vehicle of automatic driving. The electronic device obtains the real-time position of the current vehicle collected by the positioning module. The real-time position can be expressed in longitude and latitude. Combining the real-time position and the map library, the road information of the current road where the current vehicle is located can be determined. The road information includes the road name and the location of the road.

[0030] For the preset database, the vehicle vibration data of vehicles that have passed through various roads in the past can be obtained. For any road, the corresponding vehicle vibration data is compared with the preset bumpy conditions, and the road section corresponding to the vehicle vibration data that meets the preset bumpy conditions on the road is taken as the bumpy road section, so as to obtain the bumpy road section on each road and store it in the preset database. For the current road, several bumpy road sections on the current road can be obtained from the preset database, including the bumpy road sections respectively included in the current lane and adjacent lanes where the current vehicle is located.

[0031] S102. Determine a target bumpy road section on the current lane that is closest to the real-time position from among a number of bumpy road sections, and obtain target point cloud data on the current road, wherein the target point cloud data includes point cloud data from a position that is a preset distance from the real-time position in a direction opposite to the current vehicle's driving direction to the target bumpy road section.

[0032] In this embodiment, according to the current real-time position of the current vehicle, the target bumpy road section closest to the real-time position in the current vehicle's driving direction is searched from the obtained bumpy road sections, and the target bumpy road section is located on the current lane. The target point cloud data can be collected by the laser radar device installed on the current vehicle, and can also be retrieved from a pre-built preset point cloud database, which contains three-dimensional point cloud data of various roads. The section between the position with a preset distance from the real-time position in the opposite direction of the current vehicle's driving direction and the target bumpy road section is taken as the target section, and the point cloud data corresponding to the target section in the current lane and the adjacent lanes are taken as the target point cloud data.

[0033] S103. Determine whether an adjacent lane satisfies a preset lane change condition based on a number of bumpy road sections and target point cloud data.

[0034] In this embodiment, it is possible to search among several bumpy road sections to find out whether the adjacent road section in the same direction of the target bumpy road section includes a bumpy road section. If it does, the adjacent road section in the same direction is determined to be a bumpy road section; and based on the target point cloud data, it is determined whether the current vehicle can safely change lanes from the current lane to the adjacent lane, that is, the current vehicle will not collide with the vehicle in the adjacent lane during the lane change process.

[0035] In one possible scenario, the adjacent road section in the same direction of the target bumpy road section is not a bumpy road section, and the current vehicle can safely change lanes from the current lane to the adjacent lane. In this case, it is determined that the adjacent lane meets the preset lane change condition. In another possible scenario, the adjacent road section in the same direction of the target bumpy road section is a bumpy road section, or the current vehicle cannot safely change lanes from the current lane to the adjacent lane. In this case, it is determined that the adjacent lane does not meet the preset lane change condition.

[0036] S104: If the adjacent lane does not meet the preset lane change condition, determine a target acceleration based on the target bumpy road section, and control the current vehicle to decelerate according to the target acceleration.

[0037] In this embodiment, if the adjacent lane does not meet the preset lane change conditions, the current speed of the current vehicle, the target speed in the target bumpy road section, and the distance value from the real-time position to the target bumpy road section are obtained, and then the target acceleration can be calculated based on the current speed, target speed and distance value.

[0038] If the adjacent lane meets the preset lane change conditions, the current vehicle is controlled to change lanes from the current lane to the adjacent lane. At this time, the adjacent lane before the lane change becomes the current lane where the current vehicle is located, and the vehicle continues to decelerate or change lanes according to the vehicle acceleration change planning method provided in this application.

[0039] By obtaining the real-time position of the current vehicle, the embodiment of the present application can ensure that subsequent decisions are based on the latest vehicle status. By querying the preset road section database, the vehicle can predict the bumpy section on the road ahead, which helps the vehicle to take safety measures such as deceleration in advance and reduce safety hazards caused by bumps. The nearest target bumpy road section is determined and target point cloud data is obtained, which provides the vehicle with a detailed perception of the road environment ahead, helps to more accurately judge the road conditions and make decisions. By combining multi-source data (bumpy road section information and point cloud data), the vehicle can intelligently determine whether the adjacent lane is suitable for lane changing, avoiding traffic accidents that may be caused by blind lane changing, and further improving driving safety. In the case of unable to change lanes, the vehicle can adaptively adjust the acceleration to pass the bumpy section in the best state, reducing vehicle vibration and passenger discomfort caused by bumps, improving driving stability, and realizing precise control of the vehicle's driving acceleration, significantly improving driving safety and comfort.

[0040] A possible implementation of the embodiment of the present application is to determine whether an adjacent lane meets a preset lane change condition based on a number of bumpy road sections and target point cloud data, including: determining whether a road section adjacent to a target bumpy road section in the same direction is a bumpy road section according to a plurality of bumpy road sections; Determine whether the current vehicle can change lanes safely based on the target point cloud data; If the adjacent road section in the same direction of the target bumpy road section is not a bumpy road section, and the current vehicle can change lanes safely, it is determined that the adjacent lane meets the preset lane change conditions; If the adjacent road section in the same direction of the target bumpy road section is a bumpy road section, or the current vehicle cannot change lanes safely, it is determined that the adjacent lane does not meet the preset lane change conditions.

[0041] The embodiment of the present application simultaneously considers whether the target bumpy road section and its adjacent road section in the same direction are bumpy sections, which helps the vehicle to choose a smoother and safer driving path. The point cloud data provides detailed three-dimensional information about the vehicle's surroundings, including the position, speed and driving direction of other vehicles in adjacent lanes. By analyzing this information, the vehicle can accurately determine whether there is a collision risk during the lane change process, thereby avoiding accidents. It combines the two key steps of road condition assessment and safe lane change judgment, allowing the vehicle to make intelligent decisions based on real-time data and preset conditions, helping the vehicle to better cope with complex and changeable road environments, reduce emergency braking or lane change operations caused by emergencies, and improve the overall driving stability and safety.

[0042] A possible implementation of the embodiment of the present application is to determine whether the current vehicle can change lanes safely according to the target point cloud data, including: Obtain the historical lane change data of the current vehicle, including the lane change duration and lane change distance; Determine whether the distance between the real-time position and the target bumpy road section is greater than the lane change distance. If so, start from the real-time position and extend the area corresponding to the lane change distance along the adjacent lane in the current vehicle travel direction as the lane change area; Predict whether there are other vehicles passing through the lane change area in the adjacent lane within the lane change duration based on the target point cloud data; If so, it is determined that the current vehicle cannot change lanes safely.

[0043] In this embodiment, the historical lane change data of the current vehicle can be obtained from the data recording system of the current vehicle. The lane change distance is the distance traveled in the forward direction from the start of the lane change to the completion of the lane change, and the lane change duration is the driving duration corresponding to the lane change distance. If the distance between the real-time position and the target bumpy road section is not greater than the lane change distance, it indicates that the current vehicle may travel to the target bumpy road section during the lane change process, that is, there is not enough distance to change lanes. At this time, it can be determined that the current vehicle cannot change lanes safely. If the distance between the real-time position and the target bumpy road section is greater than the lane change distance, it indicates that the current vehicle will not travel to the target bumpy road section during the lane change process, that is, there is enough distance to change lanes.

[0044] Furthermore, for the target point cloud data, the accuracy of the point cloud data can be improved through preprocessing steps such as denoising and filtering. Then use a random sampling consensus algorithm or a height-based threshold segmentation algorithm to remove ground points from the point cloud to reduce the complexity of subsequent processing. Then, use a clustering algorithm (such as DBSCAN, K-means, etc.) or a deep learning-based segmentation method (such as PointNet++, VoteNet, etc.) to segment the remaining point cloud into different object clusters, each cluster representing a potential vehicle. Classifiers (such as SVM, random forests, or deep learning models) can also be further used to classify each cluster to determine whether it is a vehicle. Use a data association algorithm (such as the Hungarian algorithm, Kalman filter combined with IOU matching, etc.) to associate the vehicle in the point cloud of the current frame with the tracked vehicle in the previous frame or previous frames. For each associated vehicle, use a Kalman filter, particle filter or more complex tracking algorithm (such as extended Kalman filter, unscented Kalman filter) to estimate and update its state (including position, speed, acceleration, direction, etc.) Based on the vehicle's current state and possible motion modes (such as constant speed, acceleration, deceleration, turning, etc.), use a motion model (such as constant speed model, constant acceleration model, dynamic model, etc.) or a learning-based prediction model (such as LSTM, Transformer, etc.) to predict the vehicle's trajectory during the lane change duration.

[0045] If the trajectories of other vehicles obtained pass through the lane changing area within the lane changing time, it indicates that there is a collision risk, and it is determined that the current vehicle cannot change lanes safely; if the trajectories of other vehicles obtained do not pass through the lane changing area within the lane changing time, it indicates that there is no collision risk, and it is determined that the current vehicle can change lanes safely.

[0046] The embodiment of the present application combines the historical lane change data of the current vehicle (such as lane change duration and lane change distance) to more accurately evaluate the feasibility of the vehicle's lane change under current road conditions. After confirming that the distance between the real-time position and the target bumpy road section is greater than the lane change distance, the lane change area is further defined, and the vehicle dynamics on adjacent lanes in the area are predicted based on the target point cloud data. When it is predicted that other vehicles will pass through the lane change area within the lane change duration, the vehicle can make a timely judgment that it cannot change lanes safely, thereby avoiding potential collision risks.

[0047] In a possible implementation manner of the embodiment of the present application, the method further includes: Obtain vehicle vibration data of vehicles that have historically passed through various roads, and compare the vehicle vibration data with preset bumpy conditions; The road section corresponding to the vehicle vibration data that meets the preset bumpy condition is used as an initial bumpy road section to obtain multiple initial bumpy road sections; The point cloud data corresponding to each of the multiple initial bumpy road sections are obtained, the point cloud data corresponding to each of the multiple initial bumpy road sections are input into a preset recognition model, a plurality of bumpy road sections represented by longitude and latitude coordinates are obtained, and the plurality of bumpy road sections represented by longitude and latitude coordinates are stored in a preset road section database.

[0048] In this embodiment, a vibration sensor such as an acceleration sensor can be pre-installed on the vehicle to collect vehicle vibration data. During the driving process of the vehicle, the vibration sensor will collect vehicle vibration data in real time, including vibration frequency and amplitude, and these vibration data will be recorded synchronously with the driving time and vehicle position (latitude and longitude) of the vehicle. For any road, the corresponding vehicle vibration data includes vehicle vibration data collected from multiple vehicles passing through the road, and the average value of the vehicle vibration data of each vehicle passing through the road is used as the vehicle vibration data of the road. For example, a road corresponds to vehicle vibration data of three vehicles passing through. At any position on the road, the average value of the vibration frequency (or amplitude) in the vehicle vibration data corresponding to each of the three vehicles at the position can be used as the vibration frequency (or amplitude) corresponding to the position, thereby obtaining the vibration frequency and amplitude at each position on each road.

[0049] The preset bumpy conditions can be determined based on experience or experimental data, including a preset vibration frequency threshold and a preset amplitude threshold. The vehicle vibration data is compared with the preset bumpy conditions. Optionally, a road section in the vehicle vibration data whose vibration frequency exceeds the preset vibration frequency threshold and whose amplitude exceeds the preset amplitude threshold can be set as an initial bumpy section. A road section in the vehicle vibration data whose vibration frequency exceeds the preset vibration frequency threshold or whose amplitude exceeds the amplitude threshold can also be set as an initial bumpy section. This embodiment does not make specific limitations.

[0050] Pre-train a machine learning or deep learning model for identifying bumpy sections. The training process includes: collecting point cloud data of bumpy sections and non-bumpy sections, annotating the bump type (bumpy section / non-bumpy section) on the point cloud data, and further annotating the bump level on the point cloud data of the bumpy section. The bump level can be set according to actual needs. Optionally, the bump level can include mild bumps, moderate bumps, and severe bumps. The model is trained using the annotated point cloud data to obtain a preset recognition model. After determining the initial bumpy section, the point cloud data corresponding to the initial bumpy section can be retrieved from the preset point cloud database according to the position of the initial bumpy section. The point cloud data contains the geometric shape, texture, and obstacle information of the road surface. The point cloud data of the initial bumpy section is input into the preset recognition model, which can further accurately identify whether the initial bumpy section is an accurate bumpy section and the bump level of the bumpy section. Design a preset section database to store information about bumpy sections, including longitude and latitude coordinates, section length, and bump level.

[0051] The embodiments of the present application can realize accurate identification of bumpy road sections by acquiring vehicle vibration data of vehicles that have historically passed through various roads, and using these data to compare with preset bumpy conditions. The road sections corresponding to the vehicle vibration data that meet the preset bumpy conditions are used as initial bumpy road sections, and the point cloud data corresponding to these road sections are acquired. The point cloud data corresponding to multiple initial bumpy road sections are input into a preset recognition model, and the point cloud data are deeply analyzed to finally obtain multiple bumpy road sections represented by longitude and latitude coordinates, thereby improving the accuracy and reliability of recognition, and storing the bumpy road sections in a preset road section database, forming a data resource that can be queried and used in real time.

[0052] In a possible implementation of the embodiment of the present application, the vehicle vibration data includes vibration frequency and amplitude; The vehicle vibration data is compared with the preset bumpy conditions, and the road section corresponding to the vehicle vibration data that meets the preset bumpy conditions is used as the initial bumpy road section to obtain multiple initial bumpy road sections, including: Comparing the vibration frequency in the vehicle vibration data corresponding to each road with a preset vibration frequency threshold, and comparing the amplitude in the vehicle vibration data with a preset amplitude threshold; A section in each road where the vibration frequency exceeds a preset vibration frequency threshold and the amplitude exceeds a preset amplitude threshold is taken as an initial bumpy section, and a plurality of initial bumpy sections are obtained.

[0053] The embodiment of the present application achieves a multi-dimensional evaluation of road bumps by comparing the vibration frequency and amplitude in the vehicle vibration data with preset vibration frequency thresholds and amplitude thresholds, which can more comprehensively reflect the actual conditions of the road surface and improve the accuracy and reliability of bumpy road section identification.

[0054] A possible implementation of the embodiment of the present application is to determine the target acceleration based on the target bumpy road section, including: Obtain the current speed of the current vehicle, and determine the target speed for passing the target bumpy road section according to the preset road section database; Determine the distance value from the real-time position to the target bumpy road section; Calculate the target acceleration based on the current speed, target speed and distance value.

[0055] In this embodiment, the current speed u of the current vehicle can be obtained through the speed sensor or GPS data, the bumpiness level of the target bumpy road section is searched from the preset road section database, and then the target speed v passing through the target bumpy road section is determined according to the bumpiness level, and the distance value from the real-time position to the target bumpy road section is expressed as s. According to the formula a=(v 2 -u 2 ) / 2s to calculate the target acceleration a.

[0056] The embodiment of the present application obtains the current speed of the current vehicle and determines the target speed for passing the target bumpy section based on a preset road section database. The system can plan a smooth transition from the current speed to the target speed, which helps to reduce the discomfort caused by sudden acceleration or deceleration, improve the passenger's riding experience, and may reduce the vehicle's energy consumption and wear. Combined with the current speed, target speed and distance value, the system can accurately calculate the required acceleration to ensure that the vehicle can reach the target speed before reaching the bumpy section.

[0057] A possible implementation of the embodiment of the present application is to determine a target speed for passing through a target bumpy road section according to a preset road section database, including: Determine the bumpiness level of the target bumpy road section from a preset road section database; The target speed for passing the target bumpy road section is determined according to the bumpiness level.

[0058] In this embodiment, the correspondence between the bumpiness level and the target speed can be determined in advance through experiments. Optionally, when the bumpiness level is mild bumpiness, the target speed can be 90% of the road speed limit; when the bumpiness level is moderate bumpiness, the target speed can be 80% of the road speed limit; when the bumpiness level is severe bumpiness, the target speed can be 60% of the road speed limit. The bumpiness level of the target bumpy road section is used as input, and the target speed for passing the target bumpy road section is determined according to the pre-positioned correspondence.

[0059] The embodiment of the present application determines the bumpiness level of the target bumpy road section from a preset road section database, and can formulate different speed planning strategies for different degrees of bumpy road conditions, which helps to reduce driving instability and potential risks caused by improper speed. Whether it is slight bumps or severe bumps, the system can adjust the target speed according to the specific situation to ensure that the vehicle can pass through the bumpy section at the most suitable speed.

[0060] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the contents shown in the above-mentioned vehicle variable acceleration determination planning method embodiment are implemented.

[0061] An electronic device is provided in an embodiment of the present application, such as Figure 2 As shown, Figure 2 The electronic device 200 shown includes: a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, such as through a bus 202. Optionally, the electronic device 200 may also include a transceiver 204. It should be noted that in actual applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the present application.

[0062] The processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 201 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0063] The bus 202 may include a path to transmit information between the above components. The bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0064] The memory 203 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0065] The memory 203 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 201. The processor 201 is used to execute the application code stored in the memory 203 to implement the contents shown in the above-mentioned vehicle variable acceleration determination planning method embodiment.

[0066] Figure 2 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0067] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the contents shown in the aforementioned vehicle variable acceleration determination planning method embodiment.

[0068] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0069] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A vehicle variable acceleration determination planning method, characterized in that: include: Acquire the real-time position of the current vehicle, and determine, according to the real-time position, from a preset road section database, a number of bumpy sections of the current road where the current vehicle is located, wherein the current road includes a current lane where the current vehicle is located and an adjacent lane in the same direction as the current lane; Determine a target bumpy road section on the current lane that is closest to the real-time position from the plurality of bumpy road sections, and obtain target point cloud data on the current road, the target point cloud data including point cloud data from a position that is a preset distance from the real-time position in a direction opposite to the current vehicle driving direction to the target bumpy road section; Determining whether the adjacent lane meets a preset lane change condition according to the plurality of bumpy road sections and the target point cloud data; If the adjacent lane does not satisfy the preset lane change condition, a target acceleration is determined based on the target bumpy road section, and the current vehicle is controlled to decelerate according to the target acceleration.

2. The vehicle variable acceleration determination planning method according to claim 1, characterized in that: The determining, based on the plurality of bumpy road sections and the target point cloud data, whether the adjacent lane meets a preset lane change condition includes: Determining whether a road section adjacent to the target bumpy road section in the same direction is a bumpy road section according to the plurality of bumpy road sections; Determining whether the current vehicle can change lanes safely according to the target point cloud data; If the adjacent road section in the same direction of the target bumpy road section is not a bumpy road section, and the current vehicle can change lanes safely, then determining that the adjacent lane meets the preset lane change condition; If the adjacent road section in the same direction of the target bumpy road section is a bumpy road section, or the current vehicle cannot change lanes safely, it is determined that the adjacent lane does not meet the preset lane change condition.

3. The vehicle variable acceleration determination planning method according to claim 2, characterized in that: The determining, according to the target point cloud data, whether the current vehicle can change lanes safely includes: Acquiring historical lane change data of the current vehicle, the historical lane change data including lane change duration and lane change distance; Determine whether the distance between the real-time position and the target bumpy road section is greater than the lane change distance, and if so, determine the lane change area as an area corresponding to the lane change distance extending from the real-time position along the adjacent lane in the current vehicle driving direction; Predicting, based on the target point cloud data, whether there are other vehicles passing through the lane change area on the adjacent lane within the lane change duration; If yes, it is determined that the current vehicle cannot change lanes safely.

4. The vehicle variable acceleration determination planning method according to claim 1, characterized in that: The method further comprises: Obtain vehicle vibration data of vehicles that have historically passed through each road; Comparing the vehicle vibration data with a preset bumpy condition, taking a road section corresponding to the vehicle vibration data that meets the preset bumpy condition as an initial bumpy road section, and obtaining a plurality of initial bumpy road sections; Obtain point cloud data corresponding to each of the multiple initial bumpy road sections, input the point cloud data corresponding to each of the multiple initial bumpy road sections into a preset recognition model, obtain multiple bumpy road sections represented by longitude and latitude coordinates, and store the multiple bumpy road sections represented by longitude and latitude coordinates in a preset road section database.

5. The vehicle variable acceleration determination planning method according to claim 4, characterized in that: The vehicle vibration data includes vibration frequency and amplitude; The vehicle vibration data is compared with the preset bumpy condition, and the road section corresponding to the vehicle vibration data that meets the preset bumpy condition is used as the initial bumpy road section to obtain multiple initial bumpy road sections, including: Comparing the vibration frequency in the vehicle vibration data corresponding to each road with a preset vibration frequency threshold, and comparing the amplitude in the vehicle vibration data with a preset amplitude threshold; A section in each road whose vibration frequency exceeds the preset vibration frequency threshold and whose amplitude exceeds the preset amplitude threshold is taken as an initial bumpy section, so as to obtain a plurality of initial bumpy sections.

6. The vehicle variable acceleration determination planning method according to claim 1, characterized in that: The determining of the target acceleration based on the target bumpy road section comprises: Acquiring the current speed of the current vehicle, and determining a target speed for passing through the target bumpy road section according to the preset road section database; Determining a distance value from the real-time position to the target bumpy road section; The target acceleration is calculated according to the current speed, the target speed and the distance value.

7. The vehicle variable acceleration determination planning method according to claim 6, characterized in that: The step of determining the target speed for passing through the target bumpy road section according to the preset road section database comprises: Determining the bumpiness level of the target bumpy road section from a preset road section database; A target speed for passing through the target bumpy road section is determined according to the bumpiness level.

8. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the steps of the vehicle variable acceleration determination planning method described in any one of claims 1 to 7.

9. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the vehicle variable acceleration determination planning method described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the vehicle variable acceleration determination planning method described in any one of claims 1-7.

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