Vehicle dynamic property self-learning method, device, equipment and medium

Abstract

The invention discloses a vehicle dynamic property self-learning method, device and equipment and a medium, and belongs to the technical field of automobiles. The vehicle dynamic property self-learning method comprises the steps that after a vehicle dynamic property self-learning instruction is received, terrain data and driving behavior data of a road through which a vehicle passes within a preset duration are acquired; based on the topographic data and the driving behavior data, determining a target road type on which the user often travels; based on the target road type, adjusting a dynamic property index in the dynamic property scheme of the current version to obtain a dynamic property scheme of a new version, the dynamic property indexes comprise the engine torque corresponding to the opening degree of the accelerator pedal in the accelerator pedal map, the torque establishment rate in the torque filtering map, the gear shifting point in the gearbox gear shifting map and the brake pressure corresponding to the opening degree of the brake pedal, the vehicle dynamic property can better adapt to the terrain characteristics of the road where a user often drives, and the driving experience of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a vehicle dynamics self-learning method, device, equipment and computer-readable storage medium. Background Art

[0002] With the rapid development of automobile technology, users have higher and higher demands for intelligent vehicles. Users of each model often drive on different types of roads. For example, some users often drive on mountain roads, some users often drive on urban roads, and some users often drive on highways. For different road types, the corresponding users have different demands for vehicle power. How to adapt the vehicle power to the road types that users often drive on has become an urgent problem to be solved. In related technologies, vehicle power development is developed and solidified according to the market positioning of the model, that is, the power indicators of the same model are exactly the same. However, the existing vehicle power development method cannot adapt to the road types that users often drive on, resulting in user complaints. Summary of the invention

[0003] The present application provides a vehicle dynamics self-learning method, device, equipment and computer-readable storage medium, which can solve the technical problem in the prior art that the vehicle dynamics development method cannot be adapted to the types of roads that users often drive on, resulting in user complaints.

[0004] In a first aspect, an embodiment of the present application provides a vehicle dynamics self-learning method, the vehicle dynamics self-learning method comprising:

[0005] After receiving the vehicle dynamics self-learning instruction, obtaining terrain data and driving behavior data of the road traveled by the vehicle within a preset time period;

[0006] Determining a target road type that the user frequently travels on based on the terrain data and the driving behavior data;

[0007] Based on the target road type, the dynamic indicators in the current version of the dynamic scheme are adjusted to obtain a new version of the dynamic scheme, where the dynamic indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque buildup rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening.

[0008] In combination with the first aspect, in one implementation, the terrain data includes slope, altitude change, ramp length and number of ramps.

[0009] In combination with the first aspect, in one implementation, the driving behavior data includes engine speed, engine torque, brake pressure, transmission gear switching frequency, brake switch triggering frequency and vehicle speed.

[0010] In combination with the first aspect, in one implementation, determining the target road type that the user frequently travels on based on the terrain data and the driving behavior data includes:

[0011] respectively determining average values ​​of the slope, the altitude change, the ramp length, and the number of ramps;

[0012] respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed;

[0013] When the average slope is greater than the slope threshold, the average altitude change is greater than the altitude change threshold, the average slope length is greater than the slope length threshold, and the average number of ramps is greater than the ramp length threshold,

[0014] When the average value of the engine speed is greater than the engine speed threshold, the average value of the engine torque is greater than the engine torque threshold, the average value of the brake pressure is greater than the brake pressure threshold, the transmission gear switching frequency is greater than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is greater than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is a mountain road type.

[0015] In combination with the first aspect, in one implementation, adjusting the dynamics index in the current version of the dynamics solution based on the target road type includes:

[0016] When the target road type is a mountainous road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted to the engine torque corresponding to the mountainous road type, the torque building rate in the torque filter map is adjusted to the torque building rate corresponding to the mountainous road type, the shift point in the transmission shift map is advanced to the shift point corresponding to the mountainous road type, and the brake pressure corresponding to the brake pedal opening is adjusted to the brake pressure corresponding to the mountainous road type.

[0017] In combination with the first aspect, in one implementation, determining the target road type that the user frequently travels on based on the terrain data and the driving behavior data includes:

[0018] respectively determining average values ​​of the slope, the altitude change, the ramp length, and the number of ramps;

[0019] respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed;

[0020] When the average slope is less than the slope threshold, the average altitude change is less than the altitude change threshold, the average slope length is less than the slope length threshold, and the average number of ramps is less than the ramp length threshold,

[0021] When the average value of the engine speed is less than the engine speed threshold, the average value of the engine torque is less than the engine torque threshold, the average value of the brake pressure is less than the brake pressure threshold, the transmission gear switching frequency is less than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is less than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the second vehicle speed threshold and greater than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is an urban road type.

[0022] In combination with the first aspect, in one implementation, adjusting the dynamics index in the current version of the dynamics solution based on the target road type includes:

[0023] When the target road type is an urban road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted down to the engine torque corresponding to the urban road type, the torque buildup rate in the torque filter map is adjusted down to the torque buildup rate corresponding to the urban road type, the shift point in the transmission shift map is pushed back to the shift point corresponding to the urban road type, and the brake pressure corresponding to the brake pedal opening is adjusted down to the brake pressure corresponding to the urban road type.

[0024] In a second aspect, an embodiment of the present application provides a vehicle dynamics self-learning device, the vehicle dynamics self-learning device comprising:

[0025] An acquisition module, for acquiring terrain data and driving behavior data of a road traveled by the vehicle within a preset time period after receiving a vehicle dynamics self-learning instruction;

[0026] a determination module, configured to determine a target road type that the user frequently travels on based on the terrain data and the driving behavior data;

[0027] The adjustment module is used to adjust the dynamic indicators in the current version of the dynamic plan based on the target road type to obtain a new version of the dynamic plan, wherein the dynamic indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque build-up rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening.

[0028] In a third aspect, an embodiment of the present application provides a vehicle dynamics self-learning device, which includes a processor, a memory, and a vehicle dynamics self-learning program stored in the memory and executable by the processor, wherein when the vehicle dynamics self-learning program is executed by the processor, the steps of the vehicle dynamics self-learning method as described in any one of the first aspects are implemented.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a vehicle dynamics self-learning program is stored. When the vehicle dynamics self-learning program is executed by a processor, the steps of the vehicle dynamics self-learning method as described in any one of the first aspects are implemented.

[0030] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0031] After receiving the vehicle dynamics self-learning instruction, the terrain data and driving behavior data of the road that the vehicle travels within a preset time period are obtained; based on the terrain data and the driving behavior data, the target road type that the user frequently travels is determined; based on the target road type, the dynamics indicators in the current version of the dynamics solution are adjusted to obtain a new version of the dynamics solution, wherein the dynamics indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque buildup rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening, so that the vehicle dynamics can be better adapted to the terrain characteristics of the roads that the user frequently travels, thereby improving the user's driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of the first embodiment of the vehicle dynamics self-learning method of the present application;

[0033] Figure 2 This is a functional module diagram of an embodiment of a vehicle dynamics self-learning device of the present application;

[0034] Figure 3 This is a schematic diagram of the hardware structure of the vehicle dynamics self-learning device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0037] In a first aspect, an embodiment of the present application provides a vehicle dynamics self-learning method.

[0038] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle dynamics self-learning method of the present application. Figure 1 As shown, the vehicle dynamics self-learning method includes:

[0039] Step 110: after receiving the vehicle dynamics self-learning instruction, obtaining the terrain data and driving behavior data of the road that the vehicle travels within a preset time period;

[0040] In specific implementation, the user can input the vehicle dynamics self-learning instruction through voice broadcast or in the vehicle interface. After receiving the vehicle dynamics self-learning instruction, the vehicle starts the vehicle dynamics self-learning. First, the terrain data and driving behavior data of the road that the vehicle travels within the preset time are obtained, wherein the preset time is flexibly set by the user according to the needs, for example, one week. If the user does not set it, the default initial value is used. The driving behavior data includes but is not limited to engine speed, engine torque, brake pressure, gearbox gear switching frequency, brake switch trigger frequency and vehicle speed. The terrain data includes but is not limited to slope, altitude change, ramp length and number of ramps.

[0041] Step 120: determining a target road type that the user frequently travels on based on the terrain data and the driving behavior data;

[0042] Furthermore, when the preset time is reached, the vehicle controller self-learns the terrain data and driving behavior data within the preset time to determine the target road type that the user often travels on. The target road type includes but is not limited to mountain road types and urban road types.

[0043] Step 130: Based on the target road type, the dynamics indicators in the current version of the dynamics solution are adjusted to obtain a new version of the dynamics solution, wherein the dynamics indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque build-up rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening.

[0044] Among them, the relationship between the accelerator pedal opening and the output torque of the internal combustion engine is used to define the torque characteristics of traditional internal combustion engine vehicles, which is called the accelerator pedal map; the rate at which the output torque of the internal combustion engine is established is called the torque filter map; the transmission control unit determines the shift point according to the range of engine speed, that is, the shift rule, which is called the transmission shift map.

[0045] In specific implementation, after determining the target road type, the dynamic indicators such as the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution, the torque establishment rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening are adjusted to obtain a new version of the dynamic solution.

[0046] After the step of obtaining the target version of the dynamics solution, a confirmation request for whether to update the dynamics program may be triggered, and the triggering methods include but are not limited to voice broadcast and pop-up reminders on the vehicle interface. After receiving the confirmation request for whether to update the dynamics program, the user responds to the confirmation request to agree to the update or disagree to the update. If a confirmation instruction for agreeing to update is received, the new version of the dynamics solution will take effect. At this time, according to the values ​​of the dynamics indicators in the new version of the dynamics solution, the values ​​of each dynamics indicator in the dynamics program are updated, so that the vehicle dynamics can better adapt to the terrain characteristics of the roads that users often travel on, and improve the user's driving experience.

[0047] It can be understood that during the vehicle dynamics self-learning process, the terrain data and driving behavior data of the roads that the vehicle travels within the preset time are obtained and stored. When the storage space is insufficient, they are overwritten in chronological order. Similarly, the generated new version of the dynamics plan is also stored. When the storage space is insufficient, it is overwritten in chronological order. It should be noted that the initial version of the dynamics plan determined by the car company is not allowed to be overwritten or manually deleted, while the remaining versions of the dynamics plan can be overwritten and manually deleted.

[0048] The user can also be reminded to pay attention to driving safety and experience the driving experience. After the user experience is approved, the user can check the solidification power solution; if the driving experience is not improved, the user can return to the initial power solution or the previously stored power solution version. The vehicle power self-learning instruction can also be triggered, that is, the user returns to step 110 to carry out a new round of power self-learning.

[0049] It can be understood that if the confirmation instruction received by the vehicle is not in agreement with the update, the dynamic self-learning is terminated and the current dynamic program continues to be executed.

[0050] In this embodiment, after receiving the vehicle dynamics self-learning instruction, the terrain data and driving behavior data of the road that the vehicle travels within a preset time period are obtained; based on the terrain data and the driving behavior data, the target road type that the user frequently travels is determined; based on the target road type, the dynamics indicators in the current version of the dynamics solution are adjusted to obtain a new version of the dynamics solution, wherein the dynamics indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque buildup rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening, so that the vehicle dynamics can better adapt to the terrain characteristics of the roads that the user frequently travels, thereby improving the user's driving experience.

[0051] Furthermore, in one embodiment, determining the target road type that the user frequently travels on based on the terrain data and the driving behavior data includes:

[0052] Step 210, respectively determining the average values ​​of the slope, the altitude change, the ramp length, and the number of ramps;

[0053] Step 220, respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed;

[0054] In specific implementation, after obtaining the terrain data and driving behavior data of the road that the vehicle travels through within a preset time period, the average values ​​of each parameter in the terrain data and the driving behavior data are determined respectively, specifically, the average values ​​of the slope, the altitude change, the ramp length and the number of ramps are determined respectively, and the average values ​​of the engine speed, the engine torque, the brake pressure, the transmission gear switching frequency, the brake switch trigger frequency and the vehicle speed are determined respectively.

[0055] Step 230: When the average value of the slope is greater than the slope threshold, the average value of the altitude change is greater than the altitude change threshold, the average value of the slope length is greater than the slope length threshold, the average value of the number of ramps is greater than the ramp length threshold, and the average value of the engine speed is greater than the engine speed threshold, the average value of the engine torque is greater than the engine torque threshold, the average value of the brake pressure is greater than the brake pressure threshold, the transmission gear switching frequency is greater than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is greater than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is a mountain road type.

[0056] In a specific implementation, by combining the average value of each parameter in the terrain data and the average value of each parameter in the driving behavior data, it is determined that the target road type that the user often travels on is a mountain road type.

[0057] For example, the slope threshold is 30°, the altitude change threshold is 50m, the slope length threshold is 100m, the engine speed threshold is 2300rpm, the engine torque threshold is 150N.m, the brake pressure threshold is 3MPa, and the first vehicle speed threshold is 40km / h...

[0058] When the average slope is greater than 30°, the average altitude change is greater than 50m, the average slope length is greater than 100m, the average number of ramps is greater than the ramp length threshold, and the average engine speed is greater than 2300rpm, the average engine torque is greater than 150N.m, the average brake pressure is often greater than 3MPa (and the brake pressure is maintained for a long time), the transmission gear switching frequency is greater than the transmission gear switching frequency threshold, the average brake switch trigger frequency is greater than the brake switch trigger frequency threshold, and the average vehicle speed is less than 40km / h, it can be determined that the target road type that the user frequently drives on is a mountain road type, which means that the vehicle has a greater demand for power.

[0059] It should be noted that the various thresholds in this application can be flexibly set according to actual needs, and this application does not make any specific limitations on this.

[0060] In this embodiment, it is described how to combine the two dimensions of terrain data and driving behavior data to determine that the target road type that the user frequently travels on is a mountainous road type, which can improve the accuracy of determining the mountainous road type.

[0061] Furthermore, in one embodiment, the adjusting of the dynamics index in the current version of the dynamics solution based on the target road type includes:

[0062] When the target road type is a mountainous road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted to the engine torque corresponding to the mountainous road type, the torque building rate in the torque filter map is adjusted to the torque building rate corresponding to the mountainous road type, the shift point in the transmission shift map is advanced to the shift point corresponding to the mountainous road type, and the brake pressure corresponding to the brake pedal opening is adjusted to the brake pressure corresponding to the mountainous road type.

[0063] In specific implementation, when the target road type is a mountainous road type, it means that there is a great demand for the vehicle's power. At this time, the accelerator pedal map can be adjusted. At the same pedal opening, the engine torque can be increased to the engine torque corresponding to the mountainous road type; the torque filter map can be adjusted to increase the torque establishment rate to the torque establishment rate corresponding to the mountainous road type; the transmission shift map can be adjusted to advance the shift point and adjust the engine speed to 1200-1500rpm; the transmission shift time can be adjusted to shorten the shift time; the brake pressure can be adjusted, and the brake pressure corresponding to the brake pedal opening can be increased to the brake pressure corresponding to the mountainous road type, so that the braking is more sensitive and produces a greater braking deceleration under the same pedaling force.

[0064] In this embodiment, it is described how to adjust the dynamic indicators in the current version of the dynamic solution when the target road type is a mountain road type, in preparation for determining the dynamic solution for the mountain road type, thereby further providing a better driving experience for users who often travel on mountain roads.

[0065] Furthermore, in one embodiment, determining the target road type that the user frequently travels on based on the terrain data and the driving behavior data includes:

[0066] Step 310, respectively determining the average values ​​of the slope, the altitude change, the ramp length, and the number of ramps;

[0067] Step 320, respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed;

[0068] In specific implementation, after obtaining the terrain data and driving behavior data of the road that the vehicle travels through within a preset time period, the average values ​​of each parameter in the terrain data and the driving behavior data are determined respectively, specifically, the average values ​​of the slope, the altitude change, the ramp length and the number of ramps are determined respectively, and the average values ​​of the engine speed, the engine torque, the brake pressure, the transmission gear switching frequency, the brake switch trigger frequency and the vehicle speed are determined respectively.

[0069] Step 330: When the average value of the slope is less than the slope threshold, the average value of the altitude change is less than the altitude change threshold, the average value of the slope length is less than the slope length threshold, the average value of the number of ramps is less than the ramp length threshold, and the average value of the engine speed is less than the engine speed threshold, the average value of the engine torque is less than the engine torque threshold, the average value of the brake pressure is less than the brake pressure threshold, the transmission gear switching frequency is less than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is less than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the second vehicle speed threshold and greater than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is an urban road type.

[0070] In a specific implementation, by combining the average value of each parameter in the terrain data and the average value of each parameter in the driving behavior data, it is determined that the target road type that the user often travels on is an urban road type.

[0071] For example, the slope threshold is 30°, the altitude change threshold is 50m, the slope length threshold is 100m, the engine speed threshold is 2300rpm, the engine torque threshold is 150N.m, the brake pressure threshold is 3MPa, the first vehicle speed threshold is 40km / h, the first vehicle speed threshold is 60km / h...

[0072] When the average slope is less than 30°, the average altitude change is less than 50m, the average slope length is less than 100m, the average number of ramps is less than the ramp length threshold, and the average engine speed is less than 2300rpm (often between 1300-2300rpm), the average engine torque is less than 150N.m, the average brake pressure is less than 3MPa (often between 1-3MPa and the brake pressure is maintained for a short time), the transmission gear switching frequency is greater than the transmission gear switching frequency threshold, the average brake switch triggering frequency is greater than the brake switch triggering frequency threshold (that is, the brake switch is often triggered), and the average vehicle speed is less than 60km / h and greater than 40km / h, it can be determined that the target road type that the user often drives on is an urban road type, which means that the vehicle's power demand is relatively small.

[0073] In this embodiment, it is described how to combine the two dimensions of terrain data and driving behavior data to determine that the target road type that the user frequently travels on is an urban road type, which can improve the accuracy of determining the urban road type.

[0074] Furthermore, in one embodiment, the adjusting of the dynamics index in the current version of the dynamics solution based on the target road type includes:

[0075] When the target road type is an urban road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted down to the engine torque corresponding to the urban road type, the torque buildup rate in the torque filter map is adjusted down to the torque buildup rate corresponding to the urban road type, the shift point in the transmission shift map is pushed back to the shift point corresponding to the urban road type, and the brake pressure corresponding to the brake pedal opening is adjusted down to the brake pressure corresponding to the urban road type.

[0076] In specific implementation, when the target road type is an urban road type, it means that the demand for vehicle power is relatively small. At this time, the accelerator pedal map can be adjusted. At the same pedal opening, the engine torque can be reduced to the engine torque corresponding to the urban road type; the torque filter map can be adjusted to reduce the torque establishment rate to the torque establishment rate corresponding to the urban road type; the transmission shift map can be adjusted to push the shift point back to the engine speed between 1800 and 2300 rpm; the transmission shift time can be adjusted to keep the shift time at a general level; the brake pressure can be adjusted to reduce the brake pressure corresponding to the brake pedal opening to the brake pressure corresponding to the urban road type, so that the braking sensitivity and braking deceleration are at a general level.

[0077] In this embodiment, it is described how to adjust the dynamic indicators in the current version of the dynamic solution when the target road type is an urban road type, in preparation for determining the dynamic solution for the urban road type, thereby further providing a better driving experience for users who often travel on urban roads.

[0078] In a second aspect, an embodiment of the present application also provides a vehicle dynamics self-learning device.

[0079] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of the vehicle dynamics self-learning device of the present application. Figure 2 As shown, the vehicle dynamics self-learning device 200 includes:

[0080] The acquisition module 210 is used to acquire the terrain data and driving behavior data of the road traveled by the vehicle within a preset time after receiving the vehicle dynamics self-learning instruction;

[0081] A determination module 220, configured to determine a target road type that the user frequently travels on based on the terrain data and the driving behavior data;

[0082] The adjustment module 230 is used to adjust the dynamic indicators in the current version of the dynamic plan based on the target road type to obtain a new version of the dynamic plan, wherein the dynamic indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque build-up rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening.

[0083] Furthermore, in one embodiment, the terrain data includes slope, altitude change, ramp length and number of ramps.

[0084] Furthermore, in one embodiment, the driving behavior data includes engine speed, engine torque, brake pressure, transmission gear switching frequency, brake switch triggering frequency and vehicle speed.

[0085] Furthermore, in one embodiment, the determining module is specifically used for:

[0086] respectively determining average values ​​of the slope, the altitude change, the ramp length, and the number of ramps;

[0087] respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed;

[0088] When the average slope is greater than the slope threshold, the average altitude change is greater than the altitude change threshold, the average slope length is greater than the slope length threshold, and the average number of ramps is greater than the ramp length threshold,

[0089] When the average value of the engine speed is greater than the engine speed threshold, the average value of the engine torque is greater than the engine torque threshold, the average value of the brake pressure is greater than the brake pressure threshold, the transmission gear switching frequency is greater than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is greater than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is a mountain road type.

[0090] Furthermore, in one embodiment, the adjustment module is specifically used to:

[0091] When the target road type is a mountainous road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted to the engine torque corresponding to the mountainous road type, the torque building rate in the torque filter map is adjusted to the torque building rate corresponding to the mountainous road type, the shift point in the transmission shift map is advanced to the shift point corresponding to the mountainous road type, and the brake pressure corresponding to the brake pedal opening is adjusted to the brake pressure corresponding to the mountainous road type.

[0092] Furthermore, in one embodiment, the determining module is specifically used to:

[0093] respectively determining average values ​​of the slope, the altitude change, the ramp length, and the number of ramps;

[0094] respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed;

[0095] When the average slope is less than the slope threshold, the average altitude change is less than the altitude change threshold, the average slope length is less than the slope length threshold, and the average number of ramps is less than the ramp length threshold,

[0096] When the average value of the engine speed is less than the engine speed threshold, the average value of the engine torque is less than the engine torque threshold, the average value of the brake pressure is less than the brake pressure threshold, the transmission gear switching frequency is less than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is less than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the second vehicle speed threshold and greater than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is an urban road type.

[0097] Furthermore, in one embodiment, the adjustment module is specifically used to:

[0098] When the target road type is an urban road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted down to the engine torque corresponding to the urban road type, the torque buildup rate in the torque filter map is adjusted down to the torque buildup rate corresponding to the urban road type, the shift point in the transmission shift map is pushed back to the shift point corresponding to the urban road type, and the brake pressure corresponding to the brake pedal opening is adjusted down to the brake pressure corresponding to the urban road type.

[0099] Among them, the functional implementation of each module in the above-mentioned vehicle dynamics self-learning device corresponds to the various steps in the above-mentioned vehicle dynamics self-learning method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0100] On the third aspect, an embodiment of the present application provides a vehicle dynamics self-learning device, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0101] Reference Figure 3 , Figure 3Schematic diagram of the hardware structure of the vehicle dynamics self-learning device involved in the embodiment of the present application. In the embodiment of the present application, the vehicle dynamics self-learning device may include a processor, a memory, a communication interface and a communication bus.

[0102] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0103] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the vehicle dynamic self-learning device, and an interface used to interconnect the vehicle dynamic self-learning device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0104] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0105] The processor may be a general-purpose processor, which may call the vehicle dynamics self-learning program stored in the memory and execute the vehicle dynamics self-learning method provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the vehicle dynamics self-learning program is called may refer to the various embodiments of the vehicle dynamics self-learning method of the present application, which will not be repeated here.

[0106] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0107] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0108] The computer-readable storage medium of the present application stores a vehicle dynamics self-learning program, wherein when the vehicle dynamics self-learning program is executed by a processor, the steps of the vehicle dynamics self-learning method as described above are implemented.

[0109] Among them, the method implemented when the vehicle dynamics self-learning program is executed can refer to the various embodiments of the vehicle dynamics self-learning method of the present application, and will not be repeated here.

[0110] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0111] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0112] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0113] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0114] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0115] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0116] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A vehicle dynamics self-learning method, characterized in that: The vehicle dynamics self-learning includes: After receiving the vehicle dynamics self-learning instruction, obtaining terrain data and driving behavior data of the road traveled by the vehicle within a preset time period; Determining a target road type that the user frequently travels on based on the terrain data and the driving behavior data; Based on the target road type, the dynamic indicators in the current version of the dynamic scheme are adjusted to obtain a new version of the dynamic scheme, where the dynamic indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque buildup rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening.

2. The vehicle dynamics self-learning method according to claim 1, characterized in that: The terrain data includes slope, altitude change, ramp length and number of ramps.

3. The vehicle dynamics self-learning method according to claim 2, characterized in that: The driving behavior data includes engine speed, engine torque, brake pressure, transmission gear switching frequency, brake switch triggering frequency and vehicle speed.

4. The vehicle dynamics self-learning method according to claim 3, characterized in that: The determining, based on the terrain data and the driving behavior data, the target road type that the user frequently travels on, comprises: respectively determining average values ​​of the slope, the altitude change, the ramp length, and the number of ramps; respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed; When the average slope is greater than the slope threshold, the average altitude change is greater than the altitude change threshold, the average slope length is greater than the slope length threshold, and the average number of ramps is greater than the ramp length threshold, When the average value of the engine speed is greater than the engine speed threshold, the average value of the engine torque is greater than the engine torque threshold, the average value of the brake pressure is greater than the brake pressure threshold, the transmission gear switching frequency is greater than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is greater than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is a mountain road type.

5. The vehicle dynamics self-learning method according to claim 4, characterized in that: The dynamics index in the current version of the dynamics solution is adjusted based on the target road type, including: When the target road type is a mountainous road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted to the engine torque corresponding to the mountainous road type, the torque building rate in the torque filter map is adjusted to the torque building rate corresponding to the mountainous road type, the shift point in the transmission shift map is advanced to the shift point corresponding to the mountainous road type, and the brake pressure corresponding to the brake pedal opening is adjusted to the brake pressure corresponding to the mountainous road type.

6. The vehicle dynamics self-learning method according to claim 3, characterized in that: The determining, based on the terrain data and the driving behavior data, the target road type that the user frequently travels on, comprises: respectively determining average values ​​of the slope, the altitude change, the ramp length, and the number of ramps; respectively determining an average value of the engine speed, the engine torque, the brake pressure, the gearbox shift frequency, the brake switch trigger frequency and the vehicle speed; When the average slope is less than the slope threshold, the average altitude change is less than the altitude change threshold, the average slope length is less than the slope length threshold, and the average number of ramps is less than the ramp length threshold, When the average value of the engine speed is less than the engine speed threshold, the average value of the engine torque is less than the engine torque threshold, the average value of the brake pressure is less than the brake pressure threshold, the transmission gear switching frequency is less than the transmission gear switching frequency threshold, the average value of the brake switch trigger frequency is less than the brake switch trigger frequency threshold, and the average value of the vehicle speed is less than the second vehicle speed threshold and greater than the first vehicle speed threshold, it is determined that the target road type that the user frequently travels on is an urban road type.

7. The vehicle dynamics self-learning method according to claim 6, characterized in that: The dynamics index in the current version of the dynamics solution is adjusted based on the target road type, including: When the target road type is an urban road type, the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map in the current version of the dynamic solution is adjusted down to the engine torque corresponding to the urban road type, the torque buildup rate in the torque filter map is adjusted down to the torque buildup rate corresponding to the urban road type, the shift point in the transmission shift map is pushed back to the shift point corresponding to the urban road type, and the brake pressure corresponding to the brake pedal opening is adjusted down to the brake pressure corresponding to the urban road type.

8. A vehicle dynamics self-learning device, characterized in that: The vehicle dynamics self-learning device comprises: An acquisition module, for acquiring terrain data and driving behavior data of a road traveled by the vehicle within a preset time period after receiving a vehicle dynamics self-learning instruction; a determination module, configured to determine a target road type that the user frequently travels on based on the terrain data and the driving behavior data; The adjustment module is used to adjust the dynamic indicators in the current version of the dynamic plan based on the target road type to obtain a new version of the dynamic plan, wherein the dynamic indicators include the engine torque corresponding to the accelerator pedal opening in the accelerator pedal map, the torque build-up rate in the torque filter map, the shift point in the transmission shift map, and the brake pressure corresponding to the brake pedal opening.

9. A vehicle dynamics self-learning device, characterized in that: The vehicle dynamics self-learning device includes a processor, a memory, and a vehicle dynamics self-learning program stored in the memory and executable by the processor, wherein when the vehicle dynamics self-learning program is executed by the processor, the steps of the vehicle dynamics self-learning method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a vehicle dynamics self-learning program, wherein when the vehicle dynamics self-learning program is executed by a processor, the steps of the vehicle dynamics self-learning method as claimed in any one of claims 1 to 7 are implemented.

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