Driving comfort optimization control method, device, equipment and system

Abstract

This application discloses a driving comfort optimization control method, apparatus, device, and system, relating to the field of automotive control technology. The driving comfort optimization control method comprises: obtaining sensor data of a target vehicle; obtaining a driving bump coefficient based on 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 based on the sensor data, the driving bump coefficient, and an initial bump coefficient map; and determining a target lane and target speed based on the target bump coefficient map to achieve driving comfort optimization control for the target vehicle. This application, through the collaborative work of an on-board system and a remote server, collects and analyzes the bump coefficient of a vehicle in real time during driving, constructs a bump data map, and optimizes the vehicle's target lane and target speed based on the map, thereby improving driving comfort and safety.

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 automotive technology and the increasing demand for driving experience, driving comfort has become an important indicator of vehicle performance. Under complex road conditions, such as bumpy roads and uneven lanes, the driving comfort of a vehicle directly affects the passenger experience.

[0003] Currently, some vehicles install sensors to monitor bumpy conditions during driving and attempt to mitigate the discomfort caused by bumps by adjusting vehicle speed. However, these methods are often limited to data from a single vehicle, failing to fully understand the bumpy conditions in surrounding lanes. Furthermore, they primarily rely on data from 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, which includes:

[0007] Obtain sensor data from the target vehicle;

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

[0009] Uploading the sensor data and the ride 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 ride bump coefficient and the initial bump coefficient map;

[0010] 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.

[0011] In one embodiment, obtaining a ride bump coefficient based on the sensor data includes:

[0012] Get the preset time threshold;

[0013] obtaining a target longitudinal acceleration, a target lateral acceleration, and a target vertical acceleration based on the preset time threshold and the sensor data;

[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 ride bump coefficient to a remote server includes:

[0016] Correlating 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, determining a target lane and a target vehicle speed to complete the target vehicle driving comfort optimization control includes:

[0020] Get the preset turbulence coefficient threshold;

[0021] Obtaining a current lane bump coefficient and a first reference vehicle speed based on 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 driving comfort optimization control is completed according to the bump coefficient of the same-direction lane.

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

[0025] Obtaining a 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 speed limit is obtained according to the sensor data, and the target vehicle travels in the same-direction lane at the lane speed limit;

[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 in the same-direction lane at the second reference speed.

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

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

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

[0034] fusing the current position bump data with 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;

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

[0036] The target bump 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, which includes:

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

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

[0040] an uploading module, configured to upload 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;

[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 on the memory and runnable 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. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the driving comfort optimization control method as 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. 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 on-board 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 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1A flowchart of the first embodiment of the driving comfort optimization control method of the present application applied to a vehicle-mounted system is provided;

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

[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 explained 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 merely used to explain the technical solutions of the present application and are not intended 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 advancement of automotive technology and increasing demand for a superior driving experience, ride comfort has become a crucial indicator of vehicle performance. Under complex road conditions, such as bumpy surfaces and uneven lanes, a vehicle's ride comfort directly impacts the passenger experience. Currently, some vehicles use sensors to monitor bumps during driving and attempt to mitigate the discomfort by adjusting vehicle speed. However, these methods are often limited to data from a single vehicle, failing to fully understand the bumpiness of surrounding lanes. Furthermore, they primarily rely on data from the vehicle itself, lacking a global perspective.

[0058] The main solution of the embodiment of the present application is: 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 degree of bumpiness of the road. The sensor data and the calculated bump coefficient are then uploaded to a 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 embodiment 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.

[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 execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device, vehicle-mounted system, remote server, etc. capable of implementing the above functions. The following uses 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 This is a flow chart of a first embodiment of a driving comfort optimization control method for a vehicle-mounted system according to the present application.

[0061] In this embodiment, the driving comfort optimization control method applied to the vehicle 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 the specific vehicle for which comfort optimization control is required, i.e., the vehicle targeted by this method. The sensor data can be real-time data collected by various sensors installed on the vehicle, including but not limited to position, speed, acceleration, attitude, and environmental data. By collecting this sensor data, the onboard system can gain a comprehensive understanding of the vehicle's driving state, thereby calculating the ride bump coefficient and 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 vehicle's position, speed, acceleration, etc. during driving are collected in real time. These data provide basic information for subsequent analysis of the vehicle's driving bumpiness 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 is a quantitative indicator calculated by analyzing sensor data collected while the vehicle is in motion, used to describe the degree of bumpiness experienced by the vehicle on a specific road section. This coefficient is typically calculated based on the vehicle's acceleration data and can reflect the vehicle's longitudinal, lateral, and vertical bumpiness. This coefficient can be calculated using a variety of methods, such as calculating the root mean square (RMS) value of the acceleration data or using other statistical methods to assess the degree of bumpiness. The calculated results of the ride bump coefficient are used in subsequent driving comfort optimization control to help the vehicle select the optimal lane and speed.

[0067] It can be understood that by analyzing vehicle sensors and using a specific algorithm to calculate a quantitative indicator, namely the driving bump coefficient, this coefficient reflects the degree of bumps encountered by the vehicle during driving, providing 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] The preset time threshold can be a pre-set time range to ensure data representativeness and accuracy when calculating the ride bump coefficient. During this time range, the sensor continuously collects vehicle dynamic data for subsequent acceleration calculation and determination of the ride bump coefficient. The selection of this time threshold is typically based on factors such as road conditions, vehicle dynamic characteristics, and the sensor sampling rate. The target longitudinal acceleration can be the vehicle's acceleration along the direction of travel (usually the vehicle's forward direction), reflecting the vehicle's acceleration or deceleration in linear motion. The target lateral acceleration can be the vehicle's acceleration along a horizontal plane perpendicular to the direction of travel, typically associated with cornering or sideslipping. The target vertical acceleration can be the vehicle's acceleration in a direction perpendicular to the ground, primarily related to road surface unevenness and vehicle bouncing and vibration, and is the primary factor affecting passenger comfort. By combining acceleration data from these three directions, the vehicle's ride bump coefficient can be comprehensively assessed and calculated accordingly, providing a basis for subsequent optimization and control of driving comfort. The specific ride bump coefficient can be calculated using 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 predefined timeframe is first determined. Within this timeframe, data collected by the vehicle's sensors is used to calculate the vehicle's acceleration in the longitudinal, lateral, and vertical dimensions. This acceleration data is then used to calculate the ride bump coefficient, which quantifies the degree of vehicle bumpiness 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 based on 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 receives sensor data and ride bump coefficients from multiple vehicles for centralized processing and analysis. This server typically possesses powerful computing capabilities, capable of processing and analyzing large amounts of data and providing feedback to the vehicles. The initial bump coefficient map can be a pre-stored bump coefficient map on the remote server. It contains historical data and previously collected bump coefficient information, serving as the basis for comparison and fusion with newly uploaded sensor data and ride bump coefficients. The target bump coefficient map can be the latest bump coefficient map generated by the remote server through data fusion and processing, after receiving the current sensor data and ride bump coefficients uploaded by the vehicles, combined with the initial bump coefficient map. This map reflects the latest road bump conditions and can more accurately guide the vehicle in optimizing ride comfort, such as selecting a more comfortable lane or adjusting the vehicle speed. The target bump coefficient map is fed back to the vehicle for use during actual driving.

[0075] It is understood that the real-time driving data collected by the vehicle and the calculated bump coefficient are sent to a remote server. The server then uses this data, combined with the existing initial bump coefficient map, to generate an updated target bump coefficient map through data processing and analysis. This map is then fed 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 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 bump data to obtain a current position bump data packet; and uploading the current position bump data packet to a remote server.

[0077] Position data can be the vehicle's specific location in geographic space, typically provided by the Global Positioning System (GPS) or other positioning technologies (such as GLONASS and Galileo). This includes longitude and latitude information, and sometimes altitude information, to determine the vehicle's position in three-dimensional space. This can be used to track the vehicle's trajectory and determine the vehicle's current road or lane. Speed ​​data can be the distance traveled by the vehicle per unit time, typically measured in kilometers per hour (km / h) or meters per second (m / s). This includes instantaneous speed and average speed. Instantaneous speed is the vehicle's speed at a specific moment, while average speed is the average speed over a period of time or distance. Speed ​​data can be unidirectional (such as the vehicle's forward speed) or multidirectional (including lateral and vertical speeds), depending on the sensor type and installation method. Current position bump data can be a set of data derived by combining the ride bump coefficient with the vehicle's real-time position and speed data. This data set specifically includes: a ride roughness coefficient, which reflects the degree of ride roughness experienced by the vehicle at a specific moment; location data, including the vehicle's GPS coordinates or road location information, used to determine the data's geographic context; and speed data, including the vehicle's current speed, which may include longitudinal and lateral speeds. Together, these data describe the vehicle's ride roughness at a specific location and speed, providing foundational information for subsequent data processing and analysis. The current position roughness data packet is a data unit formed by compressing and encapsulating the current position roughness data. Compression compresses the data to reduce the amount of data transmitted and improve transmission efficiency. Encapsulation packages the compressed data, along with any metadata (such as timestamps and vehicle identification), into a structured data packet for easier transmission and processing. The encapsulated data packet can be easily uploaded to a remote server over the network, where it can be further processed to update the roughness coefficient map or perform other data analysis. This data packet design helps improve data transmission efficiency and security, ensuring reliable data transmission across the network.

[0078] Specifically, the vehicle's ride bump coefficient is combined with its corresponding position and speed data to generate a dataset describing the vehicle's bumpy conditions at a specific time and location. This data is then compressed and packaged into a data package for easy transmission. This data package is then uploaded to a remote server for further analysis and processing, thereby achieving optimized control of vehicle ride comfort. This process ensures efficient data transmission and subsequent accurate analysis, providing real-time road condition information for intelligent transportation systems.

[0079] Step S40: 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.

[0080] It should be noted that the target lane can be selected based on the bump coefficients of each lane displayed on a target bump coefficient map, with the lane with the lowest bumpiness or that meets specific comfort requirements being selected. The purpose of selecting a target lane is to reduce vehicle bumpiness during driving and improve passenger comfort. The target speed can be the most appropriate speed determined based on the average speed or recommended speed for a specific lane recorded on the target bump coefficient map, as well as the vehicle's current bumpiness. The target speed is determined based on safety, comfort, and efficiency, minimizing discomfort caused by bumps while ensuring driving safety. By determining the target lane and target speed, the vehicle can optimize its route and speed to adapt to varying road conditions, ultimately achieving the goal 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 bumpiness 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 speed are determined, and the target vehicle driving comfort optimization control is completed, including: obtaining a preset bump coefficient threshold; obtaining the current lane bump coefficient and the 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 driving comfort optimization control is completed according to the bump coefficient of the same-direction lane.

[0083] The preset bumpiness threshold may be a pre-set bumpiness value used to determine whether the vehicle's driving comfort reaches an acceptable level. If the actual bumpiness is lower than or equal to this threshold, the road conditions are considered comfortable; if it is higher than this threshold, the road conditions are considered bumpy and measures are needed to improve comfort. The current lane bumpiness may be the bumpiness of the vehicle's current lane, reflecting the degree of bumpiness encountered while driving in that lane. The first reference speed may be a recommended speed based on the bumpiness of the current lane and road conditions, designed to provide a comfortable driving experience. The current lane may be the lane the vehicle is currently traveling in. The same-direction lane bumpiness may be the bumpiness of other lanes in the same direction as the vehicle. When considering lane changes to optimize comfort, these factors are used to compare the bumpiness of different lanes to determine whether and to which lane to change. By comparing the bumpiness of the current lane with that of other lanes in the same direction, the vehicle can determine whether to change lanes for more comfortable driving conditions.

[0084] Specifically, the onboard system first obtains a preset bump coefficient threshold. It then uses sensor data and 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. By comparing the bump conditions of different lanes, the vehicle's driving comfort will be optimized, which may include changing lanes or adjusting the speed to ensure passenger comfort. This process enables the vehicle to intelligently respond to changing road conditions for 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 can be the recommended vehicle speed in the same-direction 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 bumpiness of the same-direction lane. If the vehicle decides to change to the same-direction lane, the second reference speed will serve as a reference for the vehicle's driving speed in the new lane. The same-direction lane can 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-direction lane refers to another northbound lane. On a multi-lane road, the same-direction lane may include the left lane, the middle lane, or the right lane. When considering driving comfort optimization control, the bump coefficients of the current lane and these same-direction lanes are compared to determine whether lane change is necessary to reduce bumps and thus improve driving comfort.

[0087] Specifically, the target vehicle's bump coefficient in the same lane and the corresponding second reference speed are first calculated. If the bump coefficient of the same lane is lower than that of the current lane, indicating that it is more comfortable, the vehicle is instructed to change to that lane. Conversely, if the bump coefficient of the same lane is not lower than that of the current lane, meaning that the comfort level of the same lane is not significantly better or is worse, the vehicle is advised to continue driving in the current lane at the first reference speed 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 passenger experience.

[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 is the maximum legal speed allowed in a particular lane. This speed limit is usually set by traffic regulations or road authorities and may vary depending on factors such as road conditions, traffic conditions, and weather conditions. The lane speed limit is clearly marked on road signs and is the upper limit that drivers must adhere to.

[0090] Specifically, when the bump coefficient of the same-direction lane is below a preset bump coefficient threshold, the legal speed limit for that lane is determined based on sensor data, and the target vehicle is instructed to travel at this speed limit to improve traffic efficiency. Conversely, if the bump coefficient of the same-direction lane reaches or exceeds the preset bump coefficient threshold, the target vehicle is instructed to travel at a previously calculated second reference speed (a more cautious speed) in the same-direction lane to ensure driving safety and passenger comfort. This decision-making process is designed to dynamically adjust driving strategies to adapt to varying 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 degree of bumpiness of the road. The sensor data and the calculated bump coefficient are then uploaded to a 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 embodiment 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.

[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 embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 , Figure 2 This is a flow chart of a second embodiment 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 the remote server includes steps S50 to S90:

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

[0094] It should be noted that an onboard system can be a complete electronic system installed within a vehicle, comprising various hardware and software components used to control and manage different vehicle functions. In the context of the driving comfort optimization control method, the onboard system specifically refers to those components 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] This step involves the remote server receiving two key data sources: an initial bump coefficient map containing historical bump data, providing basic information about road bumps; and a current location bump data packet sent by the vehicle's onboard system. This packet contains 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 updates and optimizes the bump coefficient map, providing more accurate guidance for optimizing driving comfort.

[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 bump data packet uploaded by the vehicle, it decompresses and parses it to extract key information such as the specific bump coefficient, position information and speed data. This information describes in detail the vehicle's driving bump conditions 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 with 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 in data analysis, outliers are data points that differ significantly from the majority of the data, possibly due to measurement error, data entry errors, or real-world extreme conditions. In the context of bumpiness, outliers may be inaccurate bump readings due to sensor failure, data transmission errors, and other factors. Data fusion is the process of combining data from multiple sources to obtain more accurate and comprehensive information or conclusions. In this example, data fusion involves combining current location bumpiness data with historical bumpiness location data to construct a more complete bumpiness map. Target location bumpiness data is the final dataset obtained after data fusion and outlier removal, representing the bumpiness characteristics of a specific location or road section. This data is used to update the bumpiness map and guide the vehicle's driving strategy based on road bumpiness. Historical data is previously collected and stored data, which can be uploaded by multiple vehicles at different times. This data provides foundational information for analysis and prediction. Historical bumpiness location data is data previously uploaded by other vehicles on bumpiness at different locations and road sections. This data reflects the vehicle's past experience with bumpy roads and can be used together with the vehicle's current real-time bump 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 smoother.

[0100] As you can understand, this step describes a data processing process in which the remote server combines the current vehicle's uploaded location bump data with historical bump location data previously uploaded and stored by other vehicles. This process is called data fusion. During the fusion process, outliers—data points that deviate significantly from the normal data and may be erroneous or inaccurate—are identified and eliminated. The resulting accurate and comprehensive data is called target location bump data. This data reflects the bump conditions at a specific location and can be used to update the bump coefficient map, providing more precise guidance for optimizing ride comfort.

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

[0102] As you can understand, this step describes how to construct an updated target bump coefficient map using target location bump data and the initial bump coefficient map. Specifically, this involves combining the real-time target location bump data with the existing initial bump coefficient map. By analyzing and processing this data, a new map is generated that more accurately and comprehensively reflects the current road bump conditions. This new map can provide the vehicle with more accurate control guidance for optimized ride comfort, helping the vehicle select the optimal driving path and speed to enhance passenger comfort.

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

[0104] This step, understandably, describes an action whereby the remote server, after completing the construction of the target rollover map, transmits this updated map data back to the vehicle's onboard system via the network. Upon receiving this data, the onboard system utilizes the latest rollover map to optimize the vehicle's route and speed, minimizing driving bumps and enhancing passenger comfort and safety.

[0105] This embodiment first obtains an initial bump coefficient map and a current location bump data packet sent by the vehicle system. It then parses these packets to extract the bump data for the current location. This real-time data is then fused with historical bump location data uploaded by other vehicles, while removing outliers to generate more accurate target location bump data. Using this target location 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 completes a complete cycle from data collection, processing, fusion, map construction, and information feedback, enabling vehicles to optimize their routes and speeds based on 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 includes:

[0107] An acquisition module 10 is 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 based on the sensor data, the driving bump coefficient and the initial bump coefficient map;

[0110] The completion module 40 is used to determine a target lane and a 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 in this application utilizes the driving comfort optimization control method described in the aforementioned embodiment to address the technical problem of intelligently optimizing driving comfort based on global bump data. Compared to the prior art, the driving comfort optimization control device provided in this application achieves the same beneficial effects as the driving comfort optimization control method described in the aforementioned embodiment. Other technical features of the driving comfort optimization control device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0112] The present application provides a driving comfort optimization control device, which includes: at least one processor; and a memory 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 structural diagram of a driving comfort optimization control device suitable for implementing an 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), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. 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 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the driving comfort optimization control device. Processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. The communication devices 1009 can allow the driving comfort optimization control device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a driving comfort optimization control device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0115] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising 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 via 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 in this application utilizes the driving comfort optimization control method described in the aforementioned embodiment to address the technical problem of intelligently optimizing driving comfort based on global bump data. Compared to the prior art, the driving comfort optimization control device provided in this application achieves the same beneficial effects as the driving comfort optimization control method described in the aforementioned embodiment. Other technical features of this driving comfort optimization control device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0117] It should be understood that the various parts disclosed in this application can be implemented using 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 description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection 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 program) 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 this 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 thereof. 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 thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction 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 thereof.

[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 is enabled to: obtain sensor data of the target vehicle; obtain a driving bumpiness coefficient based on the sensor data; upload the sensor data and the driving bumpiness coefficient to a remote server, so that the remote server generates and feeds back a target bumpiness coefficient map based on the sensor data, the driving bumpiness coefficient and the initial bumpiness coefficient map; determine a target lane and a target vehicle speed based on the target bumpiness coefficient map, and complete the 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 stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving 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., through the Internet using an Internet service provider).

[0125] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of 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 box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0126] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0127] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned driving comfort optimization control method. This computer-readable storage medium can address the technical problem of intelligently optimizing driving comfort based on global bump data. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the driving comfort optimization control method provided in the aforementioned embodiment, and are not further elaborated here.

[0128] The present application also provides a computer program product, comprising 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 in this application solves the technical problem of intelligently optimizing ride comfort based on global bump data. Compared to the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the ride comfort optimization control method provided in the aforementioned embodiment, and are not further elaborated here.

[0130] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application 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 includes: Obtain sensor data from the target vehicle; Obtaining a driving bump coefficient according to the sensor data; Uploading the sensor data and the ride 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 ride bump coefficient and the initial bump coefficient map; Based on the target bump coefficient map, a target lane and a target vehicle speed are determined to achieve optimized control of the target vehicle's driving comfort; The target lane and target vehicle speed are determined based on the target bump coefficient map to complete the target vehicle driving comfort optimization control, including: Get the preset turbulence coefficient threshold; Obtaining a current lane bump coefficient and a first reference vehicle speed based on 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 driving comfort optimization control is completed according to the bump coefficient of the same-direction lane.

2. The method according to claim 1, wherein Obtaining a ride bump coefficient based on the sensor data includes: Get the 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; 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, wherein The sensor data includes position data and speed data, and uploading the sensor data and the driving bump coefficient to a remote server includes: Correlating 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, wherein The optimizing control of the target vehicle's driving comfort according to the same-direction lane bump coefficient includes: Obtaining a 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.

5. The method according to claim 4, wherein The target vehicle is traveling in 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 speed limit is obtained according to the sensor data, and the target vehicle travels in the same-direction lane at the lane speed limit; 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.

6. A driving comfort optimization control method, characterized in that: The method is applied to a remote server and includes: Obtain the initial bump coefficient map and the current position bump data packet sent by the vehicle system; Parsing the current position turbulence data packet to obtain current position turbulence data; fusing the current position bump data with 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; constructing a target bump coefficient map based on the target position bump data and the initial bump coefficient map; sending the target bump coefficient map to the onboard system; 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: Get the preset turbulence coefficient threshold; Obtaining a current lane bump coefficient and a first reference vehicle speed based on 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 driving comfort optimization control is completed according to the bump coefficient of the same-direction lane.

7. A driving comfort optimization control device, characterized in that: The device is applied to a vehicle-mounted system, and includes: An acquisition module, used to acquire sensor data of the target vehicle; An obtaining module, configured to obtain a driving bump coefficient according to the sensor data; an uploading module, configured to upload 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; a completion module, configured to determine a target lane and a 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; The completion module is also used to obtain a preset turbulence coefficient threshold; Obtaining a current lane bump coefficient and a first reference vehicle speed based on 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 driving comfort optimization control is completed according to the bump coefficient of the same-direction lane.

8. 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. The computer program on the remote server is configured to implement the steps of the driving comfort optimization control method as described in claim 6.

9. A driving comfort optimization control system, characterized in that: The system includes the in-vehicle system according to any one of claims 1 to 5 and the remote server according to claim 6.

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