Driving comfort optimization control method, device, equipment and system

Abstract

The invention discloses a driving comfort optimization control method, device, equipment and system, and relates to the technical field of automobile control, and the driving comfort optimization control method comprises the steps: obtaining sensor data of a target vehicle; obtaining a driving bump coefficient according to the sensor data; uploading the sensor data and the driving bumping coefficient to a remote server, so that the remote server generates and feeds back a target bumping coefficient map according to the sensor data, the driving bumping coefficient and the initial bumping coefficient map; and based on the target bump coefficient map, determining a target lane and a target vehicle speed, and completing target vehicle driving comfort optimization control. Through cooperative work of the vehicle-mounted system and the remote server, the bumping coefficient in the vehicle driving process is collected and analyzed in real time, the bumping data map is constructed, and the target driving lane and the target speed of the vehicle are optimized based on the map, so that the driving comfort and safety are improved.

Description

Technical Field

[0001] The present application relates to the field of automobile control technology, and in particular to a driving comfort optimization control method, device, equipment and system. Background Art

[0002] With the development of automobile technology and the improvement of people's requirements for driving experience, driving comfort has become one of the important indicators to measure vehicle performance. Under complex road conditions, such as bumpy roads and uneven lanes, the driving comfort of the vehicle directly affects the passengers' riding experience.

[0003] At present, some vehicles install sensors to monitor the bumps during driving and try to alleviate the discomfort caused by bumps by adjusting the speed. However, these methods are often limited to the data of a single vehicle, unable to fully understand the bumps in the surrounding lanes, and mainly rely on the data of the vehicle itself, lacking a global perspective.

[0004] Therefore, how to intelligently optimize driving comfort based on global bump data has become an urgent problem to be solved. Summary of the invention

[0005] The purpose of this application is to provide a driving comfort optimization control method, device, equipment and system, aiming to solve the technical problem of intelligently optimizing driving comfort based on global bump data.

[0006] To achieve the above objectives, the present application proposes a driving comfort optimization control method, the method comprising:

[0007] Obtain sensor data of the target vehicle;

[0008] Obtaining a driving bump coefficient according to the sensor data;

[0009] Uploading the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map according to the sensor data, the driving bump coefficient and the initial bump coefficient map;

[0010] Based on the target bump coefficient map, the target lane and target vehicle speed are determined to complete the target vehicle driving comfort optimization control.

[0011] In one embodiment, obtaining the ride bump coefficient according to the sensor data includes:

[0012] Get the preset time threshold;

[0013] Based on the preset time threshold and the sensor data, a target longitudinal acceleration, a target lateral acceleration and a target vertical acceleration are obtained;

[0014] A ride bump coefficient is obtained based on the target longitudinal acceleration, the target lateral acceleration, and the target vertical acceleration.

[0015] In one embodiment, the sensor data includes position data and speed data, and uploading the sensor data and the driving bump coefficient to a remote server includes:

[0016] Associating the driving bump coefficient, the position data and the speed data to obtain current position bump data;

[0017] Compressing and encapsulating the current position turbulence data to obtain a current position turbulence data packet;

[0018] The current position bump data packet is uploaded to a remote server.

[0019] In one embodiment, based on the target bump coefficient map, a target lane and a target vehicle speed are determined to complete the target vehicle driving comfort optimization control, including:

[0020] Get the preset turbulence coefficient threshold;

[0021] Obtaining a current lane bump coefficient and a first reference vehicle speed according to the sensor data and the target bump coefficient map;

[0022] If the current lane bump coefficient is less than or equal to the preset bump coefficient threshold, the target vehicle travels in the current lane at the first reference speed;

[0023] If the current lane bump coefficient is greater than the preset bump coefficient threshold, the target vehicle's driving comfort optimization control is completed according to the same-direction lane bump coefficient.

[0024] In one embodiment, the target vehicle driving comfort optimization control is completed according to the same-direction lane bump coefficient, including:

[0025] Obtaining the same-direction lane bump coefficient and a second reference vehicle speed of the target vehicle according to the target bump coefficient map and the sensor data;

[0026] If the bump coefficient of the same-direction lane is less than the bump coefficient of the current lane, the target vehicle is traveling in the same-direction lane;

[0027] If the bump coefficient of the same-direction lane is greater than or equal to the bump coefficient of the current lane, the target vehicle travels in the current lane at the first reference speed.

[0028] In one embodiment, the target vehicle is traveling in a lane in the same direction, including:

[0029] If the bump coefficient of the same-direction lane is less than the preset bump coefficient threshold, a lane limit speed is obtained according to the sensor data, and the target vehicle travels on the same-direction lane at the lane limit speed;

[0030] If the bump coefficient of the same-direction lane is greater than or equal to the preset bump coefficient threshold, the target vehicle travels on the same-direction lane at the second reference speed.

[0031] In addition, to achieve the above purpose, the present application also proposes a driving comfort optimization control method, which is applied to a remote server and includes:

[0032] Obtain the initial turbulence coefficient map and the current position turbulence data packet sent by the vehicle system;

[0033] Parsing the current position turbulence data packet to obtain current position turbulence data;

[0034] The current position bump data and the historical bump position data are merged and outliers are removed to obtain target position bump data, wherein the historical bump position data is obtained based on historical data uploaded by other vehicles;

[0035] constructing a target bump coefficient map based on the target position bump data and the initial bump coefficient map;

[0036] The target roll coefficient map is sent to the vehicle-mounted system.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a driving comfort optimization control device, the device comprising:

[0038] An acquisition module, used to acquire sensor data of a target vehicle;

[0039] An obtaining module, used for obtaining a driving bump coefficient according to the sensor data;

[0040] an uploading module, used for uploading the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map according to the sensor data, the driving bump coefficient and the initial bump coefficient map;

[0041] The completion module is used to determine the target lane and target vehicle speed based on the driving bump coefficient and the target bump coefficient map, and complete the driving comfort optimization control of the target vehicle.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a driving comfort optimization control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the driving comfort optimization control method as described above.

[0043] In addition, to achieve the above objectives, the present application also proposes a driving comfort optimization control system, which includes the vehicle-mounted system and the remote server mentioned above.

[0044] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the driving comfort optimization control method described above are implemented.

[0045] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the driving comfort optimization control method as described above.

[0046] One or more technical solutions proposed in this application have at least the following technical effects:

[0047] This application first collects the position and speed data of the target vehicle through the sensors of the vehicle-mounted system to provide basic information for the subsequent calculation of the bump coefficient. Secondly, the collected sensor data is used to calculate the bump coefficient of the vehicle during driving and quantify the bumpiness of the road. The sensor data and the calculated bump coefficient are then uploaded to the remote server so that the server can integrate multi-source data to generate a more accurate bump coefficient map. Finally, based on the received target bump coefficient map, the optimal driving lane and speed are intelligently determined to optimize driving comfort. This application can monitor and respond to road bumps in real time, and use cloud data processing capabilities to achieve intelligent optimization of driving paths and speeds, thereby improving driving comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1A flow chart of a first embodiment of a driving comfort optimization control method for a vehicle-mounted system provided by the present application;

[0051] Figure 2 A flow chart of a second embodiment of the driving comfort optimization control method applied to a remote server of the present application;

[0052] Figure 3 This is a schematic diagram of the module structure of the driving comfort optimization control device according to an embodiment of the present application;

[0053] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the driving comfort optimization control method in the embodiment of the present application.

[0054] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0056] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0057] With the development of automobile technology and the improvement of people's requirements for driving experience, driving comfort has become one of the important indicators to measure vehicle performance. Under complex road conditions, such as bumpy roads and uneven lanes, the driving comfort of the vehicle directly affects the riding experience of passengers. At present, some vehicles install sensors to monitor the bumps during driving and try to alleviate the discomfort caused by bumps by adjusting the speed. However, these methods are often limited to the data of a single vehicle, unable to fully understand the bumps of the surrounding lanes, and mainly rely on the data of the vehicle itself, lacking a global perspective.

[0058] The main solution of the embodiment of the present application is: the embodiment first collects the position and speed data of the target vehicle through the sensors of the vehicle-mounted system to provide basic information for the subsequent calculation of the bump coefficient. Secondly, the collected sensor data is used to calculate the bump coefficient of the vehicle during driving and quantify the bumpiness of the road. The sensor data and the calculated bump coefficient are then uploaded to the remote server so that the server can generate a more accurate bump coefficient map by integrating multi-source data. Finally, based on the received target bump coefficient map, the optimal driving lane and speed are intelligently determined to optimize driving comfort. The present embodiment can monitor and respond to the bumpiness of the road in real time, and use the cloud data processing capabilities to achieve intelligent optimization of the driving path and speed, thereby improving driving comfort.

[0059] It should be noted that the execution subject of the embodiments of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a vehicle-mounted system, a remote server, etc. The following takes the vehicle-mounted system as an example to illustrate this embodiment and the following embodiments.

[0060] Based on this, the embodiment of the present application provides a driving comfort optimization control method, referring to Figure 1 , Figure 1 A flow chart of a first embodiment of a driving comfort optimization control method for a vehicle-mounted system according to the present application is provided.

[0061] In this embodiment, the driving comfort optimization control method applied to the vehicle-mounted system includes steps S10 to S40:

[0062] Step S10, acquiring sensor data of the target vehicle;

[0063] It should be noted that the target vehicle can be a specific vehicle that needs to be subjected to comfort optimization control, that is, the vehicle served by the method. The sensor data can be real-time data collected by various sensors installed on the vehicle, including but not limited to: position data, speed data, acceleration data, attitude data, and environmental data. By collecting these sensor data, the vehicle-mounted system can have a comprehensive understanding of the vehicle's driving state, and then calculate the driving bump coefficient, providing data support for subsequent comfort optimization control.

[0064] It is understandable that by utilizing various sensors installed on the target vehicle, key parameters such as the position, speed, acceleration, etc. of the vehicle during driving are collected in real time. These data provide basic information for subsequent analysis of the bumpiness of the vehicle's driving and optimization of driving comfort.

[0065] Step S20, obtaining a driving bump coefficient according to the sensor data;

[0066] It should be noted that the ride bump coefficient can be a quantitative indicator calculated by analyzing the sensor data of the vehicle during driving, which is used to describe the degree of bumpiness of the vehicle when driving on a specific road section. This coefficient is usually calculated based on the acceleration data of the vehicle and can reflect the bumpiness of the vehicle in the longitudinal, lateral and vertical directions. This coefficient can also be calculated in a variety of ways. For example, the root mean square (RMS) value of the acceleration data can be calculated, or other statistical methods can be used to evaluate the degree of bumpiness. The calculation results of the ride bump coefficient will be used for subsequent driving comfort optimization control to help the vehicle select the best driving lane and speed.

[0067] It is understandable that a quantitative indicator, namely the ride bump coefficient, is calculated by analyzing vehicle sensors using a specific algorithm. This coefficient reflects the degree of bumps encountered by the vehicle during driving and provides key reference data for subsequent optimization of driving comfort.

[0068] As an example, obtaining a ride bump coefficient based on the sensor data includes: obtaining a preset time threshold; obtaining a target longitudinal acceleration, a target lateral acceleration and a target vertical acceleration based on the preset time threshold and the sensor data; and obtaining a ride bump coefficient based on the target longitudinal acceleration, the target lateral acceleration and the target vertical acceleration.

[0069] Among them, the preset time threshold can be a time range pre-set in order to ensure the representativeness and accuracy of the data when calculating the driving bump coefficient. Within this time range, the sensor will continuously collect dynamic data of the vehicle for subsequent acceleration calculation and determination of the bump coefficient. The selection of this time threshold is usually based on factors such as road conditions, vehicle dynamic characteristics, and sensor sampling rate. The target longitudinal acceleration can be the acceleration of the vehicle along the driving direction (usually the forward direction of the vehicle), reflecting the acceleration or deceleration of the vehicle in linear motion. The target lateral acceleration can be the acceleration of the vehicle along the horizontal plane perpendicular to the driving direction, which is usually related to the turning or side sliding of the vehicle. The target vertical acceleration can be the acceleration of the vehicle in the direction perpendicular to the ground, which is mainly related to the unevenness of the road surface and the jumping and vibration of the vehicle, and is the main factor affecting passenger comfort. By combining the acceleration data in these three directions, the bumpiness of the vehicle during driving can be comprehensively evaluated, and the driving bump coefficient can be calculated accordingly, providing a basis for the subsequent driving comfort optimization control. The specific driving bump coefficient can be calculated by the following formula:

[0070] D = f(A x , A y , A z , N)

[0071] Where D is the ride bump coefficient, A x is the longitudinal acceleration of the vehicle, A y is the lateral acceleration of the vehicle, A z is the vertical acceleration of the vehicle, and N is the number of data samples.

[0072] Specifically, a preset time range is first determined, and then within this time range, the data collected by the vehicle sensors are selected to calculate the acceleration of the vehicle in the longitudinal, lateral and vertical dimensions. These acceleration data are then used to calculate the driving bump coefficient, which quantifies the degree of bumpiness of the vehicle during driving and provides an important reference for optimizing driving comfort.

[0073] Step S30, uploading the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map according to the sensor data, the driving bump coefficient and the initial bump coefficient map;

[0074] It should be noted that the remote server can be a centralized data processing facility that can receive sensor data and driving bump coefficients from multiple vehicles for centralized processing and analysis. This server usually has powerful computing capabilities, can process and analyze large amounts of data, and feed back the processing results to the vehicle. The initial bump coefficient map can be a bump coefficient map pre-stored on the remote server, which contains historical data and previously collected bump coefficient information, and is used as basic data to compare and fuse with newly uploaded sensor data and driving bump coefficients. The target bump coefficient map can refer to the latest bump coefficient map generated by the remote server through data fusion and processing after receiving the current sensor data and driving bump coefficient uploaded by the vehicle, combined with the initial bump coefficient map. This map reflects the latest road bump conditions and can more accurately guide the vehicle to optimize driving comfort control, such as selecting a more comfortable lane or adjusting the vehicle speed. The target bump coefficient map will be fed back to the vehicle for use in actual driving.

[0075] It is understood that the driving data collected by the vehicle in real time and the calculated bump coefficient are sent to the remote server so that the server can use these data, combined with the existing initial bump coefficient map, to generate an updated target bump coefficient map through data processing and analysis, and feed it back to the vehicle, thereby optimizing the vehicle's driving path and speed to improve driving comfort. This process ensures that the vehicle can make intelligent decisions based on the latest road bump information.

[0076] As an example, the sensor data includes position data and speed data, and uploading the sensor data and the riding bump coefficient to a remote server includes: associating the riding bump coefficient, the position data and the speed data to obtain current position bump data; compressing and encapsulating the current position bump data to obtain a current position bump data packet; and uploading the current position bump data packet to a remote server.

[0077] Among them, the position data can be the specific position of the vehicle in the geographic space, usually provided by the Global Positioning System (GPS) or other positioning technologies (such as GLONASS, Galileo system, etc.), including longitude and latitude information, and sometimes altitude information, to determine the position of the vehicle in three-dimensional space, which can be used to track the vehicle's driving trajectory and determine the road or lane the vehicle is currently in. The speed data can be the distance the vehicle moves per unit time, usually in kilometers per hour (km / h) or meters per second (m / s), including instantaneous speed and average speed. The instantaneous speed is the speed of the vehicle at a specific moment, while the average speed is the average speed of the vehicle over a period of time or a distance. The speed data can be single-directional (such as the speed in the vehicle's forward direction) or multi-directional (including lateral speed and vertical speed), depending on the type of sensor and the installation method. The current position bump data can be a set of data obtained by combining the driving bump coefficient with the real-time position data and speed data of the vehicle. This set of data specifically includes: a driving bump coefficient, which reflects the degree of bumpiness of the vehicle at a specific moment; location data, including the vehicle's GPS coordinates or road location information, which is used to determine the geographic context of the data; speed data, including the vehicle's current speed, which may include longitudinal speed and lateral speed. Together, these data describe the vehicle's driving bumpiness at a specific location and speed, and provide basic information for subsequent data processing and analysis. The current location bump data packet can be a data unit formed by compressing and encapsulating the above-mentioned current location bump data. Among them, compression is to reduce the amount of data transmitted and improve transmission efficiency, and the data is compressed. Encapsulation is to package the compressed data and possible metadata (such as timestamps, vehicle identification, etc.) into a structured data packet for easy transmission and processing. The encapsulated data packet can be easily uploaded to a remote server via the network, and the server can further process the data to update the bump coefficient map or perform other data analysis work. Such a data packet design helps to improve the efficiency and security of data transmission and ensure the reliable transmission of data in the network.

[0078] Specifically, the vehicle's driving bump coefficient is combined with its corresponding position and speed data to generate a data set describing the vehicle's bumpy condition at a specific time and place. This data is then compressed and packaged to form a data packet that is easy to transmit, and finally the data packet is uploaded to a remote server for further analysis and processing, thereby achieving optimized control of the vehicle's driving comfort. This process ensures efficient data transmission and subsequent accurate analysis, providing real-time road condition information for the intelligent transportation system.

[0079] Step S40, based on the target bump coefficient map, determine the target lane and target vehicle speed, and complete the target vehicle driving comfort optimization control.

[0080] It should be noted that the target lane can be a lane with the lowest bumpiness or that meets specific comfort requirements based on the bumpiness coefficients of each lane displayed on the target bumpiness coefficient map. The purpose of selecting the target lane is to reduce the bumpiness of the vehicle during driving and improve the comfort of passengers. The target speed can be the most suitable driving speed determined based on the average speed or recommended speed recorded on the target bumpiness coefficient map for driving on a specific lane and the current bumpiness of the vehicle. The determination of the target speed takes into account safety, comfort and efficiency, and reduces the discomfort caused by bumps while ensuring driving safety. By determining the target lane and target speed, the vehicle can optimize its driving route and speed to adapt to different road conditions, ultimately achieving the purpose of improving driving comfort. This optimization control can be automatic or assist the driver in making more comfortable driving decisions.

[0081] It can be understood that the on-board system determines the best lane that the vehicle should select and the recommended driving speed in that lane by analyzing the target bumpy coefficient map generated and fed back by the remote server (which shows the bumpiness of different lanes in detail) to minimize the bumpy feeling during driving and improve the comfort of passengers, thereby achieving optimized control of the driving comfort of the target vehicle.

[0082] As an example, based on the target bump coefficient map, the target lane and target vehicle speed are determined to complete the target vehicle's driving comfort optimization control, including: obtaining a preset bump coefficient threshold; obtaining the current lane bump coefficient and a first reference speed according to the sensor data and the target bump coefficient map; if the current lane bump coefficient is less than or equal to the preset bump coefficient threshold, the target vehicle travels in the current lane at the first reference speed; if the current lane bump coefficient is greater than the preset bump coefficient threshold, the target vehicle's driving comfort optimization control is completed according to the bump coefficient of the same-direction lane.

[0083] The preset bump coefficient threshold may be a preset bump coefficient value, which is used to determine whether the driving comfort of the vehicle reaches an acceptable level. If the actual bump coefficient is lower than or equal to the threshold, the road condition is considered to be comfortable; if it is higher than the threshold, the road condition is considered to be bumpy and some measures need to be taken to improve the comfort. The current lane bump coefficient may be the bump coefficient of the lane where the vehicle is currently located, which reflects the degree of bumps encountered by the vehicle when driving in the current lane. The first reference speed may be a vehicle driving speed recommended based on the bump coefficient of the current lane and the road conditions, which speed is intended to provide a comfortable driving experience. The current lane may be the lane in which the vehicle is currently driving. The same-direction lane bump coefficient may be the bump coefficient of other lanes in the same direction as the vehicle. When considering changing lanes to optimize comfort, these coefficients are used to compare the bump degrees of different lanes to determine whether to change lanes and which lane to change to. By comparing the bump coefficients of the current lane with other lanes in the same direction, the vehicle can decide whether to change lanes to seek more comfortable driving conditions.

[0084] Specifically, the vehicle system first obtains a preset bump coefficient threshold, and then uses the sensor data and the data contained in the target bump coefficient map to calculate the bump coefficient of the vehicle's current lane and the recommended first reference speed. If the bump coefficient of the current lane does not exceed this preset threshold, the vehicle will travel in the current lane at the first reference speed. If the bump coefficient of the current lane exceeds the preset threshold, the bump coefficient of the same-direction lane will be referenced to optimize the vehicle's driving comfort by comparing the bump conditions of different lanes, which may include changing lanes or adjusting the speed to ensure passenger comfort. This process enables the vehicle to respond intelligently to changes in road conditions to achieve a smoother and more comfortable driving experience.

[0085] As an example, the driving comfort optimization control of the target vehicle is completed according to the bump coefficient of the same-direction lane, including: obtaining the bump coefficient of the same-direction lane and the second reference speed of the target vehicle according to the target bump coefficient map and the sensor data; if the bump coefficient of the same-direction lane is less than the bump coefficient of the current lane, the target vehicle is traveling in the same-direction lane; if the bump coefficient of the same-direction lane is greater than or equal to the bump coefficient of the current lane, the target vehicle is traveling in the current lane at the first reference speed.

[0086] The second reference speed may be a recommended vehicle speed in the same lane, calculated based on the target bump coefficient map and sensor data. This speed is intended to provide a potentially more comfortable driving speed recommendation after taking into account the bumps in the same lane. If the vehicle decides to change to the same lane, the second reference speed will serve as a reference for the vehicle's driving speed in the new lane. The same lane may be another lane in the same direction as the target vehicle's current lane. For example, if the target vehicle is traveling in the northbound lane, the same lane refers to another northbound lane. On a multi-lane road, the same lane may include a left lane, a middle lane, or a right lane, etc. When considering driving comfort optimization control, the bump coefficients of the current lane and these same lanes are compared to determine whether it is necessary to change lanes to reduce bumps, thereby improving driving comfort.

[0087] Specifically, the bump coefficient of the target vehicle in the same-direction lane and the corresponding second reference speed are first calculated. If the bump coefficient of the same-direction lane is lower than that of the current lane, it means that the same-direction lane is more comfortable, and the vehicle will be guided to change to the same-direction lane. On the contrary, if the bump coefficient of the same-direction lane is not less than that of the current lane, that is, the comfort of the same-direction lane has no obvious advantage or is worse, it is recommended that the vehicle continue to drive at the first reference speed in the current lane to maintain driving stability and comfort. This process is designed to dynamically adjust the vehicle's driving path and speed to adapt to real-time changing road conditions and ensure a comfortable experience for passengers.

[0088] As an example, the target vehicle is traveling in a same-direction lane, including: if the bump coefficient of the same-direction lane is less than the preset bump coefficient threshold, the lane limit speed is obtained according to the sensor data, and the target vehicle travels in the same-direction lane at the lane limit speed; if the bump coefficient of the same-direction lane is greater than or equal to the preset bump coefficient threshold, the target vehicle travels in the same-direction lane at the second reference speed.

[0089] The lane speed limit can be the maximum legal speed allowed in a particular lane. This speed limit is usually set by traffic regulations or road management departments and may vary depending on factors such as road conditions, traffic conditions, weather conditions, etc. The lane speed limit will be clearly marked on road signs and is the upper speed limit that drivers need to comply with.

[0090] Specifically, when the bump coefficient of the same-direction lane is lower than the preset bump coefficient threshold, the legal speed limit of the lane is determined based on the sensor data, and the target vehicle is instructed to drive at this speed limit to improve traffic efficiency. On the contrary, if the bump coefficient of the same-direction lane reaches or exceeds the preset bump coefficient threshold, the target vehicle is instructed to drive at the second reference speed (a more cautious speed) calculated previously in the same-direction lane to ensure driving safety and passenger comfort. This decision-making process is designed to dynamically adjust the driving strategy to adapt to different road conditions.

[0091] This embodiment provides a driving comfort optimization control method. This embodiment first collects the position and speed data of the target vehicle through the sensors of the vehicle-mounted system to provide basic information for the subsequent calculation of the bump coefficient. Secondly, the collected sensor data is used to calculate the bump coefficient of the vehicle during driving and quantify the bumpiness of the road. The sensor data and the calculated bump coefficient are then uploaded to a remote server so that the server can generate a more accurate bump coefficient map by integrating multi-source data. Finally, based on the received target bump coefficient map, the optimal driving lane and vehicle speed are intelligently determined to optimize driving comfort. This embodiment can monitor and respond to the bumpiness of the road in real time, and use the cloud data processing capabilities to achieve intelligent optimization of the driving path and speed, thereby improving driving comfort.

[0092] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , Figure 2 This is a flow chart of Embodiment 2 of the driving comfort optimization control method applied to a remote server of the present application. Step S50 of the driving comfort optimization control method applied to a remote server includes steps S50 to S90:

[0093] Step S50, obtaining an initial turbulence coefficient map and a current position turbulence data packet sent by the vehicle-mounted system;

[0094] It should be noted that the vehicle-mounted system can be a complete set of electronic systems installed inside the vehicle, which includes various hardware and software components for controlling and managing different functions of the vehicle. In the context of the driving comfort optimization control method, the vehicle-mounted system specifically refers to those parts related to vehicle dynamic performance monitoring, data processing and communication, including but not limited to sensor components, data processing units, communication modules, control units, and user interfaces.

[0095] It can be understood that this step is the process of the remote server receiving two key data sources: the initial bump coefficient map, which is a map containing historical bump data and is used to provide basic information on road bumps; the current position bump data packet sent by the vehicle system, which contains the vehicle's real-time sensor data and the calculated bump coefficient, reflecting the specific bump conditions at the vehicle's current location. By integrating this data, the remote server can update and optimize the bump coefficient map, thereby providing the vehicle with more accurate driving comfort optimization control guidance.

[0096] Step S60, parsing the current position turbulence data packet to obtain current position turbulence data;

[0097] It is understandable that after the remote server receives the current position bumpy data packet uploaded by the vehicle, it decompresses and parses it to extract key information such as the specific bumpy coefficient, position information and speed data. This information describes in detail the bumpy driving conditions of the vehicle at a specific time and place, providing accurate real-time data support for further data analysis and map updates.

[0098] Step S70, fusing the current position bump data and the historical bump position data and removing outliers to obtain target position bump data, wherein the historical bump position data is obtained based on historical data uploaded by other vehicles;

[0099] It should be noted that outliers can be data points that are significantly different from the majority of the data in data analysis, which may be caused by measurement errors, data entry errors, or real extreme situations. In the context of bump coefficient, outliers may be inaccurate bump readings due to sensor failure, data transmission errors, etc. Data fusion can be the process of combining data from multiple sources to obtain more accurate and comprehensive information or conclusions. In this case, data fusion involves combining the bump data of the current location with the historical bump location data in order to build a more complete bump coefficient map. The target location bump data can be the final data set representing the bump characteristics of a specific location or road section after data fusion and outlier removal. This data is used to update the bump coefficient map and can guide how the vehicle adjusts its driving strategy based on the bumpy conditions of the road. Historical data can be previously collected and stored data, which can come from data uploaded by multiple vehicles at different points in time. This data provides basic information for analysis and prediction. Historical bump location data can be data previously uploaded by other vehicles about the bumpy conditions of different locations. This data reflects the past bumpy experience of the vehicle on a specific road section, and can be used together with the current vehicle's real-time bumpy data to build a more accurate bump coefficient map. By analyzing this historical data, it is possible to identify which road sections are typically bumpy and which are relatively smooth.

[0100] It is understood that this step describes a data processing process in which the remote server combines the instant location bump data uploaded by the current vehicle with the historical bump location data previously uploaded and stored by other vehicles. This process is called data fusion. During the fusion process, outliers are identified and excluded, that is, data points that deviate significantly from normal data and may be erroneous or inaccurate. After this step, the accurate and comprehensive data obtained is called target location bump data, which reflects the bump conditions at a specific location and can be used to update the bump coefficient map, thereby providing more accurate guidance for optimizing driving comfort for the vehicle.

[0101] Step S80, constructing a target bump coefficient map based on the target position bump data and the initial bump coefficient map;

[0102] It can be understood that this step describes the process of how to use the target position bump data and the initial bump coefficient map to construct an updated target bump coefficient map. Specifically, the target position bump data collected in real time is combined with the existing initial bump coefficient map, and a new map that more accurately and comprehensively reflects the current road bump conditions is generated by analyzing and processing these data. This new map can provide the vehicle with more accurate driving comfort optimization control guidance, helping the vehicle to select the best driving path and speed to improve passenger comfort.

[0103] Step S90: sending the target bump coefficient map to the vehicle-mounted system.

[0104] It can be understood that this step describes an action, that is, after the remote server completes the construction of the target bump coefficient map, it transmits the updated map data back to the vehicle's on-board system through the network. After receiving this data, the on-board system can use the latest bump coefficient map to optimize the vehicle's driving path and speed to reduce bumps during driving and improve passenger comfort and safety.

[0105] This embodiment first obtains the initial bump coefficient map and the current position bump data packet sent by the vehicle system, and then parses these data packets to extract the bump data of the current position. Then, these real-time data are fused with the historical bump position data uploaded by other vehicles, and outliers are removed to generate more accurate target position bump data. And using these target position bump data and the initial bump coefficient map, the server constructs an updated target bump coefficient map, and finally sends this new map to the vehicle system. This embodiment realizes a complete cycle from data collection, processing, fusion, map construction to information feedback, so that the vehicle can optimize the driving route and speed according to the latest bump coefficient map to improve driving comfort and safety.

[0106] This application also provides a driving comfort optimization control device, please refer to Figure 3 , the driving comfort optimization control device comprises:

[0107] An acquisition module 10, used to acquire sensor data of a target vehicle;

[0108] An obtaining module 20 is used to obtain a driving bump coefficient according to the sensor data;

[0109] An uploading module 30, for uploading the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map according to the sensor data, the driving bump coefficient and the initial bump coefficient map;

[0110] The completion module 40 is used to determine the target lane and the target vehicle speed based on the driving bump coefficient and the target bump coefficient map, and complete the driving comfort optimization control of the target vehicle.

[0111] The driving comfort optimization control device provided by the present application adopts the driving comfort optimization control method in the above embodiment, and can solve the technical problem of intelligently optimizing driving comfort based on global bump data. Compared with the prior art, the beneficial effects of the driving comfort optimization control device provided by the present application are the same as the beneficial effects of the driving comfort optimization control method provided by the above embodiment, and the other technical features of the driving comfort optimization control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0112] The present application provides a driving comfort optimization control device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the driving comfort optimization control method in the above-mentioned embodiment one.

[0113] Reference below Figure 4 , which shows a schematic diagram of the structure of a driving comfort optimization control device suitable for implementing the embodiment of the present application. The driving comfort optimization control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The driving comfort optimization control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0114] like Figure 4As shown, the driving comfort optimization control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the driving comfort optimization control device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the driving comfort optimization control device to communicate with other devices wirelessly or by wire to exchange data. Although the driving comfort optimization control device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0115] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0116] The driving comfort optimization control device provided by the present application adopts the driving comfort optimization control method in the above embodiment, which can solve the technical problem of intelligently optimizing driving comfort based on global bump data. Compared with the prior art, the beneficial effects of the driving comfort optimization control device provided by the present application are the same as the beneficial effects of the driving comfort optimization control method provided by the above embodiment, and the other technical features in the driving comfort optimization control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0117] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0118] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0119] The present application provides a driving comfort optimization control system, which includes the vehicle-mounted system and a remote server as described above.

[0120] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the driving comfort optimization control method in the above-mentioned embodiment.

[0121] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0122] The computer-readable storage medium may be included in the driving comfort optimization control device; or may exist independently without being assembled into the driving comfort optimization control device.

[0123] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the driving comfort optimization control device, the driving comfort optimization control device: obtains sensor data of the target vehicle; obtains a driving bump coefficient based on the sensor data; uploads the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map based on the sensor data, the driving bump coefficient and the initial bump coefficient map; determines a target lane and a target vehicle speed based on the target bump coefficient map, and completes driving comfort optimization control of the target vehicle.

[0124] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0127] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned driving comfort optimization control method, and can solve the technical problem of intelligently optimizing driving comfort based on global bump data. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the driving comfort optimization control method provided by the above-mentioned embodiment, and will not be elaborated here.

[0128] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned driving comfort optimization control method when executed by a processor.

[0129] The computer program product provided by the present application can solve the technical problem of intelligently optimizing driving comfort based on global bump data. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the driving comfort optimization control method provided by the above embodiment, which will not be repeated here.

[0130] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A driving comfort optimization control method, characterized in that: The method is applied to a vehicle-mounted system, and the method comprises: Obtain sensor data of the target vehicle; Obtaining a driving bump coefficient according to the sensor data; Uploading the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map according to the sensor data, the driving bump coefficient and the initial bump coefficient map; Based on the target bump coefficient map, the target lane and target vehicle speed are determined to complete the target vehicle driving comfort optimization control.

2. The method according to claim 1, characterized in that The step of obtaining the driving bump coefficient according to the sensor data includes: Get the preset time threshold; Based on the preset time threshold and the sensor data, a target longitudinal acceleration, a target lateral acceleration and a target vertical acceleration are obtained; A ride bump coefficient is obtained based on the target longitudinal acceleration, the target lateral acceleration, and the target vertical acceleration.

3. The method according to claim 1, characterized in that The sensor data includes position data and speed data, and uploading the sensor data and the driving bump coefficient to a remote server includes: Associating the driving bump coefficient, the position data and the speed data to obtain current position bump data; Compressing and encapsulating the current position turbulence data to obtain a current position turbulence data packet; The current position bump data packet is uploaded to a remote server.

4. The method according to claim 1, characterized in that The method of determining a target lane and a target vehicle speed based on the target bump coefficient map and completing the target vehicle driving comfort optimization control includes: Get the preset turbulence coefficient threshold; Obtaining a current lane bump coefficient and a first reference vehicle speed according to the sensor data and the target bump coefficient map; If the current lane bump coefficient is less than or equal to the preset bump coefficient threshold, the target vehicle travels in the current lane at the first reference speed; If the current lane bump coefficient is greater than the preset bump coefficient threshold, the target vehicle's driving comfort optimization control is completed according to the same-direction lane bump coefficient.

5. The method according to claim 4, characterized in that The optimizing control of the target vehicle's driving comfort according to the same-direction lane bump coefficient includes: Obtaining the same-direction lane bump coefficient and a second reference vehicle speed of the target vehicle according to the target bump coefficient map and the sensor data; If the bump coefficient of the same-direction lane is less than the bump coefficient of the current lane, the target vehicle is traveling in the same-direction lane; If the bump coefficient of the same-direction lane is greater than or equal to the bump coefficient of the current lane, the target vehicle travels in the current lane at the first reference speed.

6. The method according to claim 5, characterized in that The target vehicle is traveling on a lane in the same direction, including: If the bump coefficient of the same-direction lane is less than the preset bump coefficient threshold, a lane limit speed is obtained according to the sensor data, and the target vehicle travels on the same-direction lane at the lane limit speed; If the bump coefficient of the same-direction lane is greater than or equal to the preset bump coefficient threshold, the target vehicle travels on the same-direction lane at the second reference speed.

7. A driving comfort optimization control method, characterized in that: The method is applied to a remote server and comprises: Obtain the initial turbulence coefficient map and the current position turbulence data packet sent by the vehicle system; Parsing the current position turbulence data packet to obtain current position turbulence data; The current position bump data and the historical bump position data are merged and outliers are removed to obtain target position bump data, wherein the historical bump position data is obtained based on historical data uploaded by other vehicles; constructing a target bump coefficient map based on the target position bump data and the initial bump coefficient map; The target roll coefficient map is sent to the vehicle-mounted system.

8. A driving comfort optimization control device, characterized in that: The device is applied to a vehicle-mounted system, and comprises: An acquisition module, used to acquire sensor data of a target vehicle; An obtaining module, used for obtaining a driving bump coefficient according to the sensor data; an uploading module, used for uploading the sensor data and the driving bump coefficient to a remote server, so that the remote server generates and feeds back a target bump coefficient map according to the sensor data, the driving bump coefficient and the initial bump coefficient map; The completion module is used to determine the target lane and target vehicle speed based on the driving bump coefficient and the target bump coefficient map, and complete the driving comfort optimization control of the target vehicle.

9. A driving comfort optimization control device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program on the vehicle-mounted system is configured to implement the steps of the driving comfort optimization control method as described in any one of claims 1 to 6, and the computer program on the remote server is configured to implement the steps of the driving comfort optimization control method as described in claim 7.

10. A driving comfort optimization control system, characterized in that: The system comprises the in-vehicle system as described in any one of claims 1 to 6 and the remote server as described in claim 7.

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