Intelligent driving method, control system and device integrating personalized driving styles

Abstract

The invention provides an intelligent driving method, control system and device integrating personalized driving styles, and the device comprises an intelligent driving controller, a human-computer interface, a positioning sensing device, a perception sensing device, an Internet of Vehicles control unit and a remote server. An intelligent driving control system carried by the intelligent driving controller is provided with a driving style identification and matching algorithm unit, a sensing algorithm unit, a planning algorithm unit and a control algorithm unit; the intelligent driving method comprises the steps of generating one or more kinds of personalized driving style information according to autonomous driving data collected during autonomous driving of a driver, generating a driving style form for a user to select in combination with pre-stored default driving style information, and generating set driving style information in response to a user instruction. According to the device and the method, the corresponding driving style can be generated according to the autonomous driving data, so that the driving style the same as that of a driver can be achieved after the intelligent driving function is activated.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent driving design, and specifically relates to an intelligent driving method, control system and device integrating personalized driving style. Background Art

[0002] In recent years, intelligent driving has become a major trend in the automotive field. Various types of driving assistance and automatic driving functions have also been widely used in mass-produced models. Similar to human-driven cars, intelligent driving cars also have their own driving styles. Different driving styles have different performances in improving traffic safety, improving road traffic efficiency, and improving user driving comfort.

[0003] At present, the intelligent driving car is still driven by the driver most of the time. Only in specific scenarios can the intelligent driving function be activated to realize the autonomous operation of the vehicle. Different drivers have different driving styles when driving their own cars; however, the specific intelligent driving function of a specific brand and model has the same driving style; this will make the driver have different driving experience in the same car before and after the intelligent driving function is activated, which will have a negative impact on the user experience of the vehicle. When the driver drives the vehicle autonomously, he will appropriately adjust his driving style in different regions and different scenarios to adapt to the specific conditions of the local area. For example, in large cities with a fast pace of life and dense vehicles, in congested conditions, most drivers tend to adopt a smaller following distance to prevent jamming; while in cities with a slow pace of life and sparse traffic, even if they encounter congestion, most drivers will not have a too aggressive driving style. If a certain intelligent driving car can only have a single driving style after the intelligent driving function is activated, or the driving style cannot be switched according to the region and scenario, then it is possible that in large cities with a fast pace of life, it will be frequently jammed in congested conditions; or in cities with a slow pace of life, relatively aggressive driving will cause additional pressure on other traffic participants.

[0004] For the above problems, those skilled in the art have made the following improvements:

[0005] Patent 1: The Chinese patent application publication number is CN 113581188A, and the application publication date is November 2, 2021. The name of the invention is "A method for identifying the driving style of commercial vehicle drivers based on Internet of Vehicles data". The invention collects commercial vehicle driving data and vehicle status data in natural driving scenarios; performs outlier detection, missing value filling and elimination, feature value extraction and establishment, and data dimension reduction on the collected data; clusters the data after data dimension reduction based on the k-means clustering algorithm, and defines the driver's driving type according to the clustering results; comprehensively considers the driver's characteristics and the driver's driving type, establishes a driving style recognition model based on the random forest algorithm, and trains and tests the driving style recognition model to complete the effective identification of the driving style of commercial vehicle drivers.

[0006] Patent 2: The Chinese patent application publication number is CN 113942521A, the application publication date is January 18, 2022, and the name of the invention is "A method for driver style identification under an intelligent vehicle-road system". The invention obtains first driving data and image information of a moving vehicle; performs data preprocessing and data fusion on the first driving data to obtain the average speed, driving time, driver ID and vehicle longitude and latitude information of the moving vehicle within a preset time period; performs DB-LSTM calculation on the driving image information to obtain the driver's driving behavior type and obtain a driver style feature data set; determines the driver style type based on the driver style feature data set.

[0007] Patent Three: The Chinese patent authorization announcement number is CN 113895464B, and the authorization announcement date is April 8, 2022. The name of the invention is "Smart Car Driving Map Generation Method and System Integrating Personalized Driving Style". The invention obtains a driving map based on encoded vehicle and target information and driving style information; based on the driving map and the global path planning generated by the vehicle map data, outputs personalized driving decisions.

[0008] In the solutions of the above patents, Patents 1 and 2 only complete the recognition of driving style. Patent 3 uses specific driving style as input and output personalized decision-making, but does not mention how to obtain different driving styles. In general, the existing solutions cannot solve the problem of inconsistent driving styles between autonomous driving and intelligent driving, and the problem of adapting driving styles in different regions and scenarios.

[0009] Invention patent content

[0010] In view of the above problems, the present invention provides an intelligent driving method, control system and device integrating personalized driving style, which generates driving style information adapted to the driver's driving style and / or regional scene style by collecting data of the driver during autonomous driving, and provides the user with optional driving style solutions, so that the intelligent driving car has a suitable driving style and improves the user experience of the vehicle. The purpose of the present invention is achieved through the following technical solutions:

[0011] The present invention first provides an intelligent driving method integrating personalized driving style, which comprises:

[0012] Step S100: generating one or more personalized driving style information according to autonomous driving data collected by the driver during autonomous driving, generating a driving style form for user selection in combination with pre-stored default driving style information, and generating set driving style information in response to user instructions;

[0013] Step S200: providing optional driving path information according to driving environment information, wherein the optional driving path information includes a drivable area, a target list and lane lines;

[0014] Step S300: Generate a driving planning trajectory according to the vehicle positioning information, the optional driving path information and the set driving style information, wherein the driving planning trajectory includes the vehicle longitudinal acceleration range, the speed difference of the following road speed limit, the following longitudinal safety distance and the following lateral safety distance;

[0015] Step S400: Generate driving control instructions according to vehicle positioning information, driving planning trajectory and set driving style information.

[0016] Furthermore, the personalized driving style information includes vehicle driver driving style information and regional scenario driving style information.

[0017] Further, the method for generating the vehicle-mounted driver driving style information includes:

[0018] Step S111: reading autonomous driving data of a driver's autonomous driving once, wherein the autonomous driving data includes autonomous driving driving data, autonomous driving environment data and autonomous driving positioning data, wherein the autonomous driving driving data includes accelerator pedal opening and its changes, brake pedal opening and its changes, steering wheel angle and its changes, and the autonomous driving environment data includes information such as a target list and lane lines; and the autonomous driving positioning data includes information such as the vehicle's speed, position, acceleration, azimuth angle, and yaw angular velocity;

[0019] Step S112: judging whether a driving style boundary is formed according to the autonomous driving data collected in step S111, if the judgment result is yes, jumping to step S150; if the judgment result is no, jumping to step S113;

[0020] Step S113: Determine whether the current reading times exceeds the preset times M, if it exceeds the preset times M, jump to step S114; if it does not exceed the preset times M, jump to step S111;

[0021] Step S114: plotting the M autonomous driving data read to the same coordinate system, solving the boundary Limit, so that a certain preset proportion of the M data is within the boundary Limit, and generating the on-board driver's driving style information according to the boundary Limit;

[0022] Step S115: reading the current real-time driving data, determining whether the real-time driving data falls within the boundary Limit, and counting the data exceeding the boundary Limit;

[0023] Step S116: Determine whether the amount of data currently exceeding the boundary Limit exceeds a preset threshold value N. If the determination result is that it exceeds the preset threshold value N, jump to step S114; if the determination result is that it does not exceed, jump to step S111.

[0024] Furthermore, the method for generating the regional scene driving style information includes:

[0025] Step S121: Determine whether there is a regional scene driving style boundary. If the determination result is no, jump to step S122; if the determination result is yes, jump to step S126;

[0026] Step S122: reading the regional scene data, wherein the regional scene data includes information on geographical range, time period and number of vehicles;

[0027] Step S123: Determine whether the regional scene data exceeds the preset scale ZS. If it exceeds the preset scale ZS, jump to step S124; if it does not exceed the preset scale, jump to step S122;

[0028] Step S124: Divide the regional scene into zones according to the regional scene data obtained in step S122;

[0029] Step S125: according to the regional driving style partitions divided in step S124, respectively solve the driving style boundaries LimitZ in different regional scenarios, so as to define the regional scenario driving style;

[0030] Step S126: Determine whether the preset update time interval has been exceeded, if so, jump to step S122, if not, jump to end.

[0031] Further, the driving style boundary includes at least two of a longitudinal acceleration boundary, a speed limit deviation boundary, a longitudinal safety distance boundary and a lateral safety distance boundary.

[0032] A second aspect of the present invention is to provide an intelligent driving control system integrating personalized driving style, which has:

[0033] A driving style recognition and matching algorithm unit generates personalized driving style information based on the manual driving information transmitted by the vehicle actuator during autonomous driving and the positioning information transmitted by the vehicle positioning sensor device, and generates a driving style form for user selection in combination with the pre-stored default driving style information;

[0034] A perception algorithm unit, providing optional driving path information according to driving environment information transmitted by the vehicle perception sensor device, wherein the optional driving path information includes a drivable area, a target list and a lane line;

[0035] A planning algorithm unit generates a driving planning trajectory according to the vehicle positioning information transmitted by the vehicle positioning sensor device, the optional driving path information transmitted by the perception algorithm unit, and the selected driving style information transmitted by the driving style recognition and matching algorithm unit, wherein the driving planning trajectory includes a vehicle longitudinal acceleration range, a speed difference of a following road speed limit, a following vehicle longitudinal safety distance, and a following vehicle lateral safety distance;

[0036] The control algorithm unit generates a driving control instruction according to the vehicle positioning information transmitted by the vehicle positioning sensor device and the driving planning trajectory transmitted by the planning algorithm unit.

[0037] Furthermore, the driving style recognition and matching algorithm unit includes:

[0038] The vehicle-mounted driver data collection module collects autonomous driving data information of the driver during autonomous driving, wherein the autonomous driving data information includes the action information of the vehicle actuator, the positioning information of the positioning sensor device, and the driving environment information transmitted by the perception sensor device;

[0039] An on-board driver style recognition module, which forms on-board driver driving style information based on the information from the data collection module;

[0040] The regional scene information collection module collects the time information, the positioning information of the positioning sensor device, the speed information and the action information of the vehicle actuator during driving;

[0041] The driving style adaptation module provides a form containing the vehicle driver's driving style information, regional scenario driving style information and default driving style information to the human-machine interface for user selection, generates a set driving style in response to user instructions, and transmits the set driving style parameters to the control algorithm unit.

[0042] On this basis, the third aspect of the present invention is to provide an intelligent driving device that integrates personalized driving style, which includes an intelligent driving controller, a human-machine interface, a positioning sensor device, a perception sensor device, a vehicle networking control unit and a remote server: the intelligent driving controller adopts a domain controller, which is equipped with the aforementioned intelligent driving control system; the vehicle networking control unit is used to connect the vehicle to the Internet, send information and receive networked data; the positioning sensor device includes a global positioning system and an inertial measurement unit; the perception sensor device includes at least two of a camera, a millimeter-wave radar, and a lidar; the intelligent driving controller adopts a domain controller; the remote server communicates with the vehicle networking control unit through a wireless network device.

[0043] Preferably, the positioning sensor device adopts a fusion positioning solution of the global positioning system and the inertial measurement unit to provide more accurate and reliable positioning information; the perception sensor device adopts a fusion perception solution of multiple cameras + millimeter wave radar + lidar to provide more comprehensive and reliable driving environment information data for control system decisions.

[0044] Furthermore, the remote server is loaded with a regional scene driving style recognition module; the information sent by the Internet of Vehicles control unit includes the information collected by the regional scene information collection module; and the received networked data includes the results of recognition by the regional scene driving style recognition module.

[0045] Preferably, the specific process of implementing step S100 using the above-mentioned intelligent driving device is as follows:

[0046] Step S110: the intelligent driving domain controller uses the vehicle information, perception information and positioning information obtained by the vehicle driver data collection module after a preset number of M readings as input, and generates the driving style information of the current driver through the vehicle driver style recognition algorithm in the vehicle driver style recognition module;

[0047] Step S120: The regional scene information collection module transmits the collected information to the remote server, and the remote server takes the regional scene information of all vehicles of a preset scale ZS as input, and calculates the current regional scene driving style information through the algorithm of the regional scene driving style recognition module; preferably, the preset scale ZS includes a preset geographical range P1, a preset time period T3, and a preset number of vehicles K;

[0048] Step S130: The human-machine interface receives the driving style Limit of the current onboard driver sent by the onboard driver style recognition module and the current regional scene driving style LimitZ sent by the regional scene driving style recognition module, and prompts the user of the currently available driving style through the display screen of the human-machine interface 4. Generally, the driving styles are divided into three types: default driving style, onboard driver driving style and regional scene driving style; the driver operates on the human-machine interface 4 to select a specific driving style, and the human-machine interface 4 sends the driving style selected by the driver to the intelligent driving domain controller 3 through the CAN bus;

[0049] Step S140: The intelligent driving controller 3 receives the driving style selected by the user, and adjusts the parameter values ​​corresponding to the planning algorithm and the control algorithm to the values ​​corresponding to the style according to the corresponding driving style information, so as to reasonably control the vehicle to switch the style in the intelligent driving mode. The adjusted parameter values ​​include: Limit for the driver driving style; LimitZ for the regional scene driving style.

[0050] The beneficial effects of the present invention are:

[0051] 1. The present invention solves the problem of inconsistency between the driver's autonomous driving and intelligent driving styles; it allows the user to set it up autonomously so that the intelligent driving has the same driving style as the driver when the driver autonomously drives the vehicle after the intelligent driving function is activated;

[0052] 2. The present invention solves the problem of adapting driving styles to different regions and scenarios; it allows users to make independent settings so that after the intelligent driving function is activated, the intelligent driving has the same driving style as other vehicles in the area where the current vehicle is located. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 FIG. 1 is a schematic diagram showing the structure of an intelligent driving device in Embodiment 1 of the present invention;

[0054] Figure 2 The figure shows the overall process diagram of the intelligent driving method in Embodiment 2 of the present invention;

[0055] Figure 3 FIG. 2 is a flow chart of a driving style recognition and matching algorithm in Embodiment 2 of the present invention;

[0056] Figure 4 The figure is a schematic diagram of a process of generating the driving style information of the vehicle driver in the second embodiment of the present invention;

[0057] Figure 5 The figure is a schematic diagram of a process of generating driving style information for a regional scenario in Embodiment 2 of the present invention;

[0058] Figure 6The figure shows an example of generating a vehicle-mounted driver style boundary in Embodiment 2 of the present invention;

[0059] Figure 7 The figure shows an example of generating a driving style boundary for a regional scenario in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0060] The technical scheme of the preferred embodiment of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1:

[0062] This embodiment provides an intelligent driving device integrating personalized driving style, which is configured as follows: Figure 1 As shown, it includes an intelligent driving controller 3, a human-machine interface 4, a positioning sensor device 1, a perception sensor device 2, a vehicle network control unit 5 and a remote server 6.

[0063] In a preferred embodiment, the positioning sensor device 1 adopts a combined positioning solution commonly used in the field of intelligent driving. A specific embodiment is a combined positioning solution using GPS (Global Positioning System) + IMU (Inertial Measurement Unit). The output port of the positioning sensor device 1 is a CAN (Controller Area Network) interface, and the output signal is the current positioning information of the vehicle.

[0064] The perception sensor device 2 includes at least two of a camera, a millimeter-wave radar, and a laser radar. In a preferred embodiment, the perception sensor device 2 adopts a multi-sensor solution, and a specific embodiment is a fusion perception solution of multiple cameras + millimeter-wave radars + laser radars. The output port of the perception sensor module 2 varies according to the different sensors adopted by the perception sensor module 2, and is a common interface in the field of intelligent driving. For example, when the fusion perception solution is a fusion perception solution of multiple cameras + millimeter-wave radars + laser radars, its output interface includes an LVDS (Low Voltage Differential Signaling) interface, a CAN interface, and an Ethernet interface. The output signal of the perception sensor module 2 is the original sensor signal.

[0065] In a preferred embodiment, the intelligent driving controller 3 adopts a domain controller, and the intelligent driving controller 3 has common interfaces in the field of intelligent driving, including CAN interface, LVDS interface, Ethernet interface, etc. The domain controller carries an intelligent driving algorithm that integrates personalized driving style. In order to realize the setting function of personalized driving style, in the preferred embodiment shown in the figure, the intelligent driving control system carried by the intelligent driving controller 3 has a driving style recognition and matching algorithm unit 31, a perception algorithm unit 32, a planning algorithm unit 33 and a control algorithm unit 34, as shown in FIG. Figure 2 As shown, the functions of each unit are as follows:

[0066] The driving style recognition and matching algorithm unit 31 generates personalized driving style information based on the manual driving information transmitted by the vehicle actuator during autonomous driving and the positioning information transmitted by the vehicle positioning sensor device, and generates a driving style form for user selection in combination with the pre-stored default driving style information.

[0067] A perception algorithm unit 32, providing optional driving path information according to driving environment information transmitted by the vehicle perception sensor device, wherein the optional driving path information includes a drivable area, a target list and a lane line;

[0068] The planning algorithm unit 33 generates a driving planning trajectory according to the vehicle positioning information transmitted by the vehicle positioning sensor device, the optional driving path information transmitted by the perception algorithm unit, and the selected driving style information transmitted by the driving style recognition and matching algorithm unit, so as to adjust the algorithm performance through specific parameters, wherein the specific parameters of the driving planning trajectory include the longitudinal acceleration range of the vehicle, the speed difference of the following road speed limit, the longitudinal safety distance of the following vehicle, and the lateral safety distance of the following vehicle;

[0069] The control algorithm unit 34 generates a driving control instruction according to the vehicle positioning information transmitted by the vehicle positioning sensor device and the driving planning trajectory transmitted by the planning algorithm unit.

[0070] The human-machine interface 4 is a common device in the automotive field. A specific embodiment uses a large central control screen mounted on a vehicle; it is used to display information and read the driver's operating instructions. The human-machine interface 4 carries an intelligent driving style selection module, which first obtains the driver style recognition result from the vehicle-mounted driver style recognition module of the intelligent driving domain controller 3 and the regional scene driving style recognition result from the remote server 6, and then displays the above results to the driver through the display screen. The driver selects the desired driving style through the physical buttons in the human-machine interface 4 or the virtual buttons in the touch screen.

[0071] The vehicle networking control unit 5 is used to connect the vehicle to the network, send information and receive network data. The information sent by the vehicle networking control unit 5 includes: the information collected by the regional scene driving style recognition information collection module; the information received includes: the results of the regional scene driving style recognition module;

[0072] The remote server 6 communicates with the vehicle networking control unit through a wireless network device, and the remote server 6 establishes a connection with the vehicle networking control unit 5 through a wireless network to send and receive data. The remote server 6 carries a regional scene driving style recognition algorithm; the information it receives includes: information collected by the regional scene information collection module; the information it sends includes: the result of regional scene driving style recognition.

[0073] In a preferred embodiment, the connection relationship between the various components of the intelligent driving device is as follows: the CAN interface of the positioning sensor device 1 is connected to the CAN interface of the intelligent driving domain controller 3 through a twisted pair wire; the Ethernet interface of the perception sensor device 2 is connected to the Ethernet interface of the intelligent driving domain controller 3 through a network cable; the LVDS interface of the perception sensor device 2 is connected to the LVDS interface of the intelligent driving domain controller 3 through a wire; the CAN interface of the perception sensor device 2 is connected to the CAN interface of the intelligent driving domain controller 3 through a twisted pair wire; the CAN interface of the intelligent driving domain controller 3 and the CAN interface of the human-machine interface 4 are connected through a twisted pair wire; the CAN interface of the intelligent driving domain controller 3 and the CAN interface of the vehicle networking control unit 5 are connected through a twisted pair wire; the vehicle networking control unit 5 and the remote server 6 are connected through a wireless network;

[0074] In a preferred embodiment, the driving style recognition and matching algorithm unit includes an onboard driver data collection module 31a, an onboard driver driving style recognition module 31b, a regional scene information collection module 31c, and a driving style adaptation module 31d, etc. The functions of each module are as follows:

[0075] The on-board driver data acquisition module 31a is used to collect the information required for the on-board driver's driving style identification. It is usually necessary to collect the autonomous driving data information of the driver when driving autonomously. The autonomous driving data information includes the action information of the vehicle actuator, the positioning information of the positioning sensor device, and the driving environment information transmitted by the perception sensor device. In a specific embodiment, the on-board driver data acquisition module 31a obtains vehicle information from the vehicle through the CAN line; the perception information obtained from the perception sensor device 2 through the LVDS interface, CAN interface and Ethernet interface; the positioning information obtained from the positioning sensor device 1 through the CAN bus; the vehicle information includes: accelerator pedal opening, accelerator pedal opening change rate, brake pedal opening, brake pedal opening change rate, steering wheel angle, steering wheel angle change rate, etc.; the perception information includes target lists, lane lines, etc.; the positioning information includes the vehicle's speed, position, acceleration, azimuth angle, yaw rate, etc.

[0076] The vehicle-mounted driver style recognition module 31b forms the vehicle-mounted driver autonomous driving style information according to the information of the data acquisition module, and is used to recognize the driver's driving style during autonomous driving;

[0077] The regional scene information acquisition module 31c is used to collect the information required for regional scene driving style identification, specifically, the time information during driving, the positioning information of the positioning sensor device, the speed information, and the action information of the vehicle actuator, etc. In a preferred embodiment, the information collected by the regional scene information acquisition module 31c includes the time information obtained from the human-machine interface 4; the position information and speed information obtained from the positioning sensor device 1; the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening, the brake pedal opening change rate, the steering wheel angle, the steering wheel angle change rate information, etc. obtained from the vehicle can line;

[0078] The driving style adaptation module 31d provides a form containing the driving style information of the onboard driver, the driving style information of the regional scene and the default driving style information to the human-machine interface 4 for the user to select, generates a set driving style in response to the user's instruction, and transmits the set driving style parameters to the control algorithm unit. Its purpose is to adjust the corresponding parameter configuration according to the driving style selected in the intelligent driving style selection module in the human-machine interface 4, and then adjust the performance of the planning algorithm and the control algorithm, so that the vehicle has the selected style in the intelligent driving mode.

[0079] Embodiment 2:

[0080] This embodiment provides an intelligent driving method integrating personalized driving style based on the device of embodiment 1, such as Figure 2 As shown, the method includes the following process:

[0081] Step S100 - driving style recognition and matching algorithm: Generate one or more personalized driving style information based on the autonomous driving data collected by the driver during autonomous driving, generate a driving style form for user selection in combination with pre-stored default driving style information, and generate set driving style information in response to user instructions; preferably, the personalized driving style information includes on-board driver driving style information and regional scenario driving style information.

[0082] Step S200-perception algorithm: providing optional driving path information according to driving environment information, wherein the optional driving path information includes a drivable area, a target list and lane lines;

[0083] Step S300-planning algorithm: Generate a driving planning trajectory according to the vehicle positioning information, the optional driving path information and the set driving style information, wherein the driving planning trajectory includes the vehicle longitudinal acceleration range, the speed difference of the following road speed limit, the longitudinal safety distance of the following vehicle and the lateral safety distance of the following vehicle;

[0084] Step S400 - control algorithm: generating driving control instructions according to vehicle positioning information, driving planning trajectory and set driving style information.

[0085] In a preferred embodiment, the implementation process of the driving style recognition and matching algorithm is as follows: Figure 3 As shown, step S100 can be specifically decomposed into the following process:

[0086] Step S110: the intelligent driving domain controller 3 uses the vehicle information, perception information and positioning information obtained by the vehicle driver data collection module after a preset number of M readings as input, and generates the driving style information of the current driver through the vehicle driver style recognition algorithm in the vehicle driver style recognition module;

[0087] Step S120: The regional scene information collection module 31c transmits the collected information to the remote server 6. The remote server 6 takes the regional scene information of all vehicles of the preset scale ZS as input, and calculates the current regional scene driving style information through the regional scene driving style recognition algorithm; in a preferred embodiment, the preset scale ZS includes a preset geographical range P1, a preset time period T3, and a preset number of vehicles K; an embodiment of the preset geographical range P1 is: Changchun City; an embodiment of the preset time period T3 is: winter; an embodiment of the preset number of vehicles K is: 100,000 vehicles;

[0088] Step S130: The human-machine interface 4 receives the driving style Limit of the current on-board driver sent by the on-board driver style recognition module 31b and the current regional scene driving style LimitZ sent by the regional scene driving style recognition module, and prompts the user of the currently available driving style through the display screen of the human-machine interface 4. Generally, the driving style is divided into three types: default driving style, driver driving style and regional scene driving style; wherein the default driving style is the style determined by the parameters set by the algorithm developer in the intelligent driving algorithm; the driver driving style is the driving style identified in the on-board driver driving style recognition; the regional scene driving style is the driving style identified in the server 6 through the regional scene driving style algorithm recognition; the driver operates on the human-machine interface 4 to select a specific driving style, and the human-machine interface 4 sends the driving style selected by the driver to the intelligent driving domain controller 3 through the CAN bus.

[0089] Step S140: The intelligent driving controller 3 receives the driving style selected by the user, and adjusts the parameter values ​​corresponding to the planning algorithm and the control algorithm to the values ​​corresponding to the style according to the set driving style information, so as to reasonably control the vehicle to switch the style in the intelligent driving mode. The adjusted parameter values ​​include: Limit for the driver driving style; LimitZ for the regional scene driving style.

[0090] In a preferred embodiment, the method steps for generating the vehicle-mounted driver driving style information in step S110 are as follows: Figure 4 As shown, the specific process includes the following:

[0091] Step S111: reading autonomous driving data of a driver's autonomous driving once, wherein the autonomous driving data includes autonomous driving driving data, autonomous driving environment data and autonomous driving positioning data, wherein the autonomous driving driving data includes accelerator pedal opening and its changes, brake pedal opening and its changes, steering wheel angle and its changes, and the autonomous driving environment data includes a target list and lane line information; the autonomous driving positioning data includes vehicle speed, position, acceleration, azimuth angle, and yaw rate information;

[0092] Step S112: judging whether a driving style boundary is formed according to the autonomous driving data collected in step S111, if the judgment result is yes, jumping to step S150; if the judgment result is no, jumping to step S113;

[0093] Step S113: Determine whether the current reading times exceeds the preset times M, if it exceeds the preset times M, jump to step S114; if it does not exceed the preset times M, jump to step S111;

[0094] Step S114: Plot the M autonomous driving data read to the same coordinate system, solve the boundary Limit, so that a certain preset proportion of the M data is within the boundary Limit, and generate the on-board driver driving style information according to the boundary Limit; accordingly, the intelligent driving domain controller 3 can send the calculated current driver's driving style to the human-machine interface 4 through the CAN bus, where the driver style is the above boundary Limit;

[0095] Step S115: reading the current real-time driving data, determining whether the real-time driving data falls within the boundary Limit, and counting the data exceeding the boundary Limit;

[0096] Step S116: Determine whether the amount of data currently exceeding the boundary Limit exceeds a preset threshold value N. If the determination result is that it exceeds the preset threshold value N, jump to step S114; if the determination result is that it does not exceed, jump to step S111.

[0097] In a preferred embodiment, the method steps for generating the regional scene driving style information in step S120 are as follows: Figure 5 As shown, it specifically includes the following process steps:

[0098] Step S121: Determine whether there is a regional scene driving style boundary. If the determination result is no, jump to step S122; if the determination result is yes, jump to step S126;

[0099] Step S122: reading the regional scene data, wherein the regional scene data includes information on geographical range, time period and number of vehicles;

[0100] Step S123: Determine whether the regional scene data exceeds a preset scale ZS. If so, jump to step S124; if not, jump to step S122; the preset scale ZS includes a preset geographical range P1, a preset time period T3, and a preset number of vehicles K;

[0101] Step S124: Divide the regional driving style into zones according to the regional scene data obtained in step S122; some typical scenes of the regional driving style zones include zones divided according to location, time period, vehicle size and road section, and the scenes are exemplified as: Changchun winter congested expressway, Changchun winter unobstructed expressway, Changchun expressway, etc.; the corresponding data obtained are Changchun winter congested expressway data, Changchun winter unobstructed expressway data, Changchun expressway data, etc.;

[0102] Step S125: According to the regional driving style partitions divided in step S124, the driving style boundaries LimitZ in different regional scenes are solved respectively, so as to define the regional scene driving style; usually, the remote server 6 sends the calculated current regional scene driving style to the Internet of Vehicles control unit 5 through the wireless network; the Internet of Vehicles control unit 5 sends the information to the human-machine interface 4 through the CAN bus. The regional scene driving style is the above-mentioned boundary LimitZ;

[0103] Step S126: Determine whether the preset update time interval has been exceeded, if so, jump to step S122, if not, jump to end.

[0104] In a preferred embodiment, when generating the driving style information of the onboard driver, the lateral and longitudinal acceleration range, the speed difference of the following road speed limit, the longitudinal safety distance of the following vehicle, and the lateral safety distance of the following vehicle in the planning control algorithm unit are adjusted according to the lateral and longitudinal acceleration boundaries, the speed limit deviation boundary, the longitudinal safety distance boundary, and the lateral safety distance boundary. The boundary parameters used include the lateral acceleration boundary, the speed limit deviation boundary, the longitudinal safety distance boundary, the lateral safety distance boundary, etc. The solution process of the parameters is as follows (see Figure 6 ):

[0105] (1) Transverse and longitudinal acceleration boundaries

[0106] See also Figure 6 As shown in a, the intelligent driving domain controller 3 reads the maximum longitudinal acceleration and the lateral acceleration corresponding to the maximum longitudinal acceleration, the minimum longitudinal acceleration and the lateral acceleration corresponding to the minimum lateral acceleration, the minimum lateral acceleration and the longitudinal acceleration corresponding to the minimum lateral acceleration, the maximum lateral acceleration and the longitudinal acceleration corresponding to the maximum lateral acceleration in a driving behavior of the intelligent driving car in the driver's autonomous driving mode, from vehicle startup to vehicle shutdown, and draws these four points as primary data in a coordinate system with the horizontal coordinate being the lateral acceleration alat and the vertical coordinate being the longitudinal acceleration alon.

[0107] Read M times of data and draw them in the same coordinate system, take the y-axis as the symmetry center, and solve the ellipse combination aLimit as the boundary. The ellipse combination can cover all points drawn by the M times of data at a preset ratio Mpercent. A specific embodiment of the preset ratio is 90%;

[0108] (2) Speed ​​limit deviation boundary

[0109] See also Figure 6As shown in Figure b, the intelligent driving domain controller 3 reads the road speed limit Vlimit and the maximum speed Vmax of the vehicle under the speed limit in a driving behavior of the intelligent driving car from vehicle startup to vehicle shutdown in the driver's autonomous driving mode. With the road speed limit Vlimit as the horizontal coordinate and the difference between the road speed limit and the maximum speed under the speed limit Vlimit-Vmax as the vertical coordinate, the data points in a driving behavior are plotted into the coordinate system.

[0110] Read M times of data and draw them in the same coordinate system, and solve a straight line bLimit as a boundary. The straight line can divide all points drawn by the M times of data to one side of the boundary at a preset ratio Mpercent. A specific embodiment of the preset ratio is 90%;

[0111] (3) Longitudinal safety distance boundary

[0112] See also Figure 6 As shown in Figure c, the intelligent driving domain controller 3 reads the longitudinal distance between the vehicle and the front vehicle at different speeds in a driving behavior of the intelligent driving car from vehicle startup to vehicle shutdown in the driver's autonomous driving mode. With the vehicle speed v as the horizontal coordinate and the longitudinal distance distLon between the vehicle and the front vehicle as the vertical coordinate, the data points in a driving behavior are plotted into the coordinate system.

[0113] Read M times of data and draw them in the same coordinate system, and solve a straight line cLimit as a boundary. The straight line can divide all points drawn by the M times of data to one side of the boundary at a preset ratio Mpercent. A specific embodiment of the preset ratio is 90%;

[0114] (4) Horizontal safety distance boundary

[0115] See also Figure 6 d, the intelligent driving domain controller 3 reads the lateral distance between the vehicle and the side vehicle at different vehicle speeds in a driving behavior from vehicle startup to vehicle shutdown in the driver's autonomous driving mode. With the vehicle speed v as the horizontal coordinate and the lateral distance distLat between the vehicle and the side vehicle as the vertical coordinate, the data points in a driving behavior are plotted into the coordinate system.

[0116] Read the M data and draw them in the same coordinate system, and solve a straight line dLimit as a boundary. The straight line can divide all points drawn by the M data into one side of the boundary at a preset ratio Mpercent. A specific embodiment of the preset ratio is 90%.

[0117] In a preferred embodiment, for regional scene driving style recognition, the following road speed limit difference and the following vehicle longitudinal safety distance in the planning control module are adjusted according to the longitudinal safety distance boundary and the speed limit deviation boundary. In a specific embodiment, the example of defining the regional scene driving style in step S125 is divided into the following two aspects (see Figure 7 ):

[0118] (1) See Figure 7 As shown in a, taking the solution of driving style boundary of Changchun winter congested expressway as an example, in the Changchun winter congested expressway data, the vehicle speed v is used as the horizontal coordinate, and the longitudinal distance distLon between the vehicle and the front vehicle is used as the vertical coordinate. The N data read are plotted in the same coordinate system, and a straight line eLimitZ is solved as the boundary. The straight line can divide all points drawn by the N data to one side of the boundary at a preset ratio Npercent. A specific embodiment of the preset ratio is 85%;

[0119] (2) See Figure 7 As shown in FIG. 2 , taking the solution of driving style boundary of Changchun winter congested expressway as an example, in the Changchun winter congested expressway data, the road speed limit Vlimit is used as the horizontal coordinate, and the difference between the road speed limit and the maximum speed under the speed limit Vlimit-Vmax is used as the vertical coordinate. The N data read are plotted in the same coordinate system, and a straight line fLimit is solved as the boundary. The straight line can divide all points drawn by the N data to one side of the boundary at a preset ratio Npercent. A specific embodiment of the preset ratio is 85%.

[0120] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention. The dimensions described in the drawings and embodiments are not related to the specific objects and are not used to limit the protection scope of the present invention. The dimensions of the objects can be selected and changed according to actual needs.

Claims

1. An intelligent driving method integrating personalized driving style, comprising: Step S100: generating one or more personalized driving style information according to autonomous driving data collected by the driver during autonomous driving, generating a driving style form for user selection in combination with pre-stored default driving style information, and generating set driving style information in response to user instructions; Step S200: providing optional driving path information according to driving environment information, wherein the optional driving path information includes a drivable area, a target list and lane lines; Step S300: Generate a driving planning trajectory according to the vehicle positioning information, the optional driving path information and the set driving style information, wherein the driving planning trajectory includes the vehicle longitudinal acceleration range, the speed difference of the following road speed limit, the following longitudinal safety distance and the following lateral safety distance; Step S400: Generate driving control instructions according to vehicle positioning information, driving planning trajectory and set driving style information.

2. The intelligent driving method integrating personalized driving style according to claim 1, characterized in that: The personalized driving style information includes the vehicle driver's driving style information and the regional scenario driving style information.

3. The intelligent driving method integrating personalized driving style according to claim 2, characterized in that: The method for generating the vehicle-mounted driver driving style information comprises: Step S111: reading autonomous driving data of a driver's autonomous driving once, wherein the autonomous driving data includes autonomous driving driving data, autonomous driving environment data and autonomous driving positioning data, wherein the autonomous driving driving data includes accelerator pedal opening and its changes, brake pedal opening and its changes, steering wheel angle and its changes, and the autonomous driving environment data includes a target list and lane line information; the autonomous driving positioning data includes vehicle speed, position, acceleration, azimuth angle, and yaw rate information; Step S112: judging whether a driving style boundary is formed according to the autonomous driving data collected in step S111, if the judgment result is yes, jumping to step S150; if the judgment result is no, jumping to step S113; Step S113: Determine whether the current reading times exceeds the preset times M, if it exceeds the preset times M, jump to step S114; if it does not exceed the preset times M, jump to step S111; Step S114: plotting the M autonomous driving data read to the same coordinate system, solving the boundary Limit, so that a certain preset proportion of the M data is within the boundary Limit, and generating the on-board driver's driving style information according to the boundary Limit; Step S115: reading the current real-time driving data, determining whether the real-time driving data falls within the boundary Limit, and counting the data exceeding the boundary Limit; Step S116: Determine whether the amount of data currently exceeding the boundary Limit exceeds a preset threshold value N. If the determination result is that it exceeds the preset threshold value N, jump to step S114; if the determination result is that it does not exceed, jump to step S111.

4. The intelligent driving method integrating personalized driving style according to claim 2, characterized in that: The method for generating the regional scene driving style information includes: Step S121: Determine whether there is a regional scene driving style boundary. If the determination result is no, jump to step S122; if the determination result is yes, jump to step S126; Step S122: reading the regional scene data, wherein the regional scene data includes information on geographical range, time period and number of vehicles; Step S123: Determine whether the regional scene data exceeds the preset scale ZS. If it exceeds the preset scale ZS, jump to step S124; if it does not exceed the preset scale, jump to step S122; Step S124: Divide the regional scene into zones according to the regional scene data obtained in step S122; Step S125: according to the regional driving style partitions divided in step S124, respectively solve the driving style boundaries LimitZ in different regional scenarios, so as to define the regional scenario driving style; Step S126: Determine whether the preset update time interval has been exceeded, if so, jump to step S122, if not, jump to end.

5. The intelligent driving method integrating personalized driving style according to any one of claims 1 to 4, characterized in that: The driving style boundary includes at least two of a longitudinal acceleration boundary, a speed limit deviation boundary, a longitudinal safety distance boundary, and a lateral safety distance boundary.

6. An intelligent driving control system integrating personalized driving style, which has: A driving style recognition and matching algorithm unit generates personalized driving style information based on the manual driving information transmitted by the vehicle actuator during autonomous driving and the positioning information transmitted by the vehicle positioning sensor device, and generates a driving style form for user selection in combination with the pre-stored default driving style information; A perception algorithm unit, providing optional driving path information according to driving environment information transmitted by the vehicle perception sensor device, wherein the optional driving path information includes a drivable area, a target list and a lane line; A planning algorithm unit generates a driving planning trajectory according to the vehicle positioning information transmitted by the vehicle positioning sensor device, the optional driving path information transmitted by the perception algorithm unit, and the selected driving style information transmitted by the driving style recognition and matching algorithm unit, wherein the driving planning trajectory includes a vehicle longitudinal acceleration range, a speed difference of a following road speed limit, a following vehicle longitudinal safety distance, and a following vehicle lateral safety distance; The control algorithm unit generates a driving control instruction according to the vehicle positioning information transmitted by the vehicle positioning sensor device and the driving planning trajectory transmitted by the planning algorithm unit.

7. The intelligent driving control system integrating personalized driving style according to claim 6, characterized in that: The driving style recognition and matching algorithm unit includes: The vehicle-mounted driver data collection module collects autonomous driving data information of the driver during autonomous driving, wherein the autonomous driving data information includes the action information of the vehicle actuator, the positioning information of the positioning sensor device, and the driving environment information transmitted by the perception sensor device; An on-board driver style recognition module, which forms on-board driver driving style information based on the information from the data collection module; The regional scene information collection module collects the time information, the positioning information of the positioning sensor device, the speed information and the action information of the vehicle actuator during driving; The driving style adaptation module provides a form containing the vehicle driver's driving style information, regional scenario driving style information and default driving style information to the human-machine interface for user selection, generates a set driving style in response to user instructions, and transmits the set driving style parameters to the control algorithm unit.

8. An intelligent driving device integrating personalized driving style, comprising an intelligent driving controller, a human-machine interface, a positioning sensor device, a perception sensor device, a vehicle networking control unit and a remote server: The intelligent driving controller is equipped with an intelligent driving control system, which has a driving style recognition and matching algorithm unit, a perception algorithm unit, a planning algorithm unit and a control algorithm unit; The vehicle networking control unit is used to connect the vehicle to the network, send information and receive network data; The positioning sensor device includes a global positioning system and an inertial measurement unit; The sensing device includes at least two of a camera, a millimeter wave radar, and a laser radar; The intelligent driving control system adopts a domain controller; The remote server communicates with the vehicle networking control unit via a wireless network device.

9. The intelligent driving device integrating personalized driving style according to claim 8, characterized in that: The remote server is loaded with a regional scene driving style recognition module; the information sent by the vehicle network control unit includes the information collected by the regional scene information collection module; the received networked data includes the results of recognition by the regional scene driving style recognition module.

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