Human-machine co-driving control method, apparatus and device, and computer-readable storage medium

Abstract

A human-machine co-driving control method, apparatus and device, and a computer-readable storage medium. The method comprises: dividing a planned route to be travelled into M travelling road sections, wherein M is a positive integer; when a vehicle is less than a first preset distance away from entering any travelling road section, outputting prompt information of the type of any travelling road section; when the vehicle is located on any travelling road section, detecting a target function state and a road condition within a second preset distance range in front of the vehicle, and determining whether the target function state and the road condition meet a target function operation condition; and if the target function state and the road condition within the second preset distance range in front of the vehicle meet the target function operation condition, controlling the vehicle to run a target function, and outputting prompt information indicating that the target function is running.

Description

Human-machine co-driving control method, device, equipment and computer-readable storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application No. CN202311541695.X filed on November 16, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to the field of vehicle assisted driving, and in particular to a human-machine co-driving control method, device, equipment, and computer-readable storage medium. Background Art

[0004] Once a car enters Smart Cruise mode, the driver and the driving system (i.e., human and machine) collaborate, hand over tasks, and alternately perform driving operations. This represents the normalized stage of Smart Driving, or human-machine co-driving. Unlike conventional lane-level longitudinal and lateral Smart Cruise functions, such as Adaptive Cruise Control (ACC), Lane Centering Control (LCC), and Intelligent Cruise Assist (ICA), Navigate on Autopilot (NOA) allows the driver to activate assisted driving after setting a navigation route and entering a NOA-enabled road section. From point A to point B, NOA enables various functions, including automatic on- and off-ramps, automatic overtaking, automatic lane changes, and adaptive cruise control. In comparison, NOA is more intelligent. In actual application, the aforementioned functions differ in their operational domains, operating conditions, and user interface display and prompt strategies during human-machine interaction.

[0005] Therefore, how to effectively, reasonably and timely provide prompts for functional status nodes, and ensure driving safety while increasing the driver's understanding, control and experience of the functions is a technical problem that urgently needs to be solved.

[0006] Summary of the Invention

[0007] The present disclosure provides a human-machine co-driving control method, device, equipment and computer-readable storage medium, which can solve the technical problem in the prior art of being unable to effectively, reasonably and timely prompt functional status nodes.

[0008] In a first aspect, an embodiment of the present disclosure provides a human-machine co-driving control method, which includes: dividing the planned route to be driven into M driving sections, where M is a positive integer; when the distance between the vehicle and any driving section is less than a first preset distance, outputting prompt information of the type of any driving section; when the vehicle is in any driving section, detecting whether the target function status and the road conditions within a second preset distance range in front of the vehicle meet the target function operation conditions; and if the target function status and the road conditions within the second preset distance range in front of the vehicle meet the target function operation conditions, controlling the vehicle to operate the target function, and outputting prompt information that the target function is running.

[0009] In the second aspect, an embodiment of the present disclosure provides a human-machine co-driving control device, which includes: a division module for dividing the planned route to be driven into M driving sections, where M is a positive integer; an output module for outputting prompt information of the type of any driving section when the distance between the vehicle and the entry point is less than a first preset distance; a detection module for detecting whether the target function status and the road conditions within a second preset distance range in front of the vehicle meet the target function operation conditions when the vehicle is in any driving section; and the output module is also used to control the vehicle to operate the target function if the target function status and the road conditions within the second preset distance range in front of the vehicle meet the target function operation conditions, and output prompt information that the target function is running.

[0010] In a third aspect, an embodiment of the present disclosure provides a human-machine co-driving control device, which includes a processor, a memory, and a human-machine co-driving control program stored in the memory and executable by the processor, wherein when the human-machine co-driving control program is executed by the processor, the steps of the human-machine co-driving control method described above are implemented.

[0011] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a human-machine co-driving control program is stored. When the human-machine co-driving control program is executed by a processor, the steps of the human-machine co-driving control method described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG1 is a flow chart of a method for controlling human-machine co-driving according to some embodiments of the present disclosure;

[0013] FIG2 is a schematic diagram of the travel section in FIG1 ;

[0014] FIG3 is a schematic diagram of a detailed process for determining the planned route to be traveled in FIG1 ;

[0015] FIG4 is a schematic diagram of functional modules of a human-machine co-driving control device according to some embodiments of the present disclosure; and

[0016] FIG5 is a schematic diagram of the hardware structure of a human-machine co-driving control device according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] To help those skilled in the art better understand the present disclosure, the following will provide a clear and complete description of the technical solutions in the embodiments of the present disclosure, in conjunction with the accompanying drawings. It is clear that the described embodiments are only a portion of the embodiments of the present disclosure, not all of them. All other embodiments derived by those skilled in the art based on the embodiments of the present disclosure without creative effort are within the scope of protection of the present disclosure.

[0018] First, some technical terms in the present disclosure are explained to facilitate understanding of the present disclosure by those skilled in the art.

[0019] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0020] In a first aspect, an embodiment of the present disclosure provides a method for controlling human-machine co-driving.

[0021] FIG1 is a flow chart of a method for controlling human-machine co-driving according to some embodiments of the present disclosure.

[0022] In some embodiments, as shown in FIG1 , the human-machine co-driving control method may include the following steps S10 to S40 .

[0023] In step S10, the planned route to be traveled is divided into M travel sections, where M is a positive integer.

[0024] Before the vehicle departs, the vehicle controller obtains the user's planned route from the onboard navigation software and divides the planned route into M driving sections, where M is a positive integer. This helps the vehicle controller match the vehicle with the optimal intelligent driving function based on the actual conditions of any driving section, bringing great convenience to the user's travel. Taking M as an example, the planned route can be divided into 3 driving sections. It should be noted that the numerical values ​​listed in the embodiments of the present disclosure, such as "3", are only used to illustrate the content of the embodiments of the present disclosure and are not specific limiting values.

[0025] In some embodiments, before the step of dividing the planned route to be traveled into M driving sections, it may also include: obtaining N planned routes based on the starting point parameters and end point parameters of the planned route to be traveled, where N is a positive integer; for each planned route, calculating the length ratio of all target sections included in the planned route in the planned route, where the target sections are in the high-precision map coverage area.

[0026] It can be understood that high-precision maps, in layman's terms, are electronic maps with higher precision and more data dimensions. The higher precision is reflected in the accuracy to the centimeter level, and the data dimensions are more reflected in the fact that it includes surrounding static information related to traffic in addition to road information.

[0027] In some embodiments, before the vehicle departs, the user can manually or by voice input and determine the starting point parameters and end point parameters of the trip on the in-vehicle navigation software. The vehicle controller obtains N planned routes based on the input starting point parameters and end point parameters in combination with the map data on the in-vehicle navigation software, where N is a positive integer; for each planned route, the high-precision map data is matched with the traditional map data to obtain the target road sections in the planned route that are in the high-precision map coverage area and the non-target road sections in the planned route that are not in the high-precision map coverage area. After further calculation, the length ratio of all target road sections included in the planned route can be obtained. Figure 2 is a schematic diagram of the driving section in Figure 1. As shown in Figure 2, A and E represent the starting point and end point, respectively, section BC and section DE represent sections in the high-precision map coverage area, and then section AB and section CD represent sections that are not in the high-precision map coverage area. Take sections BC and DE as target sections, sections AB and CD as non-target sections, and calculate the length ratio of all target sections (i.e., section BC + section DE) included in the planned route to the length ratio of the planned route (i.e., section AE).

[0028] The planned route with the largest length ratio is selected from all planned routes, and the planned route with the largest length ratio is determined as the planned route to be traveled.

[0029] The Autopilot navigation feature meets the needs of users in a variety of driving scenarios, including highways, urban elevated ring roads, and urban expressways. It reduces the burden of long driving hours, alleviates driver fatigue, and enhances the driving experience, making driving safer, more efficient, and more comfortable. Therefore, most users prefer Autopilot navigation during their trips. However, Autopilot navigation requires high-precision map data to operate, and the target road segment is located within high-precision map coverage. Therefore, the route with the largest percentage of target road segments within all planned routes must be selected from all planned routes. This route is then designated as the route to be driven. For example, if there are three planned routes, with the first route accounting for 50% of the total length, the second route accounting for 40%, and the third route accounting for 30%. Of these three routes, the first route, which accounts for 50% of the total length, is designated as the route to be driven. It should be noted that the numerical values ​​listed in the embodiments of the present disclosure, such as "3", "50%", "40%", and "30%", are only used to illustrate the contents of the embodiments of the present disclosure and are not specific limiting values.

[0030] In the disclosed embodiment, N planned routes are obtained based on the starting point parameters and end point parameters of the planned route to be traveled, where N is a positive integer; for each planned route, the length proportion of all target sections contained in the planned route is calculated, where the target sections are in the high-precision map coverage area; the planned route with the largest length proportion is selected from all planned routes, and the planned route with the largest length proportion is determined as the planned route to be traveled, which solves the technical problem in the related art of how to select a better planned route to be traveled for the user to provide the user with a better driving experience.

[0031] Figure 3 is a schematic diagram of a detailed process for determining the planned route to be traveled in Figure 1. As shown in Figure 3, in some embodiments, before the step of dividing the planned route to be traveled into M travel sections, the following steps S01 to S06 may also be included.

[0032] In step S01, N planned routes are obtained according to the starting point parameters and the end point parameters of the planned route to be traveled, where N is a positive integer.

[0033] In step S02, for each planned route, the length ratio of all target road sections included in the planned route is calculated, wherein the target road sections are in the high-precision map coverage area.

[0034] In step S03, the planned route with the largest length ratio is selected from all planned routes.

[0035] In step S04 , it is detected whether the congestion index of the planned route with the largest length ratio is less than a preset congestion index.

[0036] In the embodiment of the present disclosure, determining the planned route to be traveled not only needs to consider the length ratio of all target road sections included in the planned route in the planned route, but also needs to consider the congestion index of the planned route. Therefore, after selecting the planned route with the largest length ratio from all the planned routes, it is also necessary to detect whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index. When the congestion index of the planned route with the largest length ratio is less than the preset congestion index, it means that the road of the planned route with the largest length ratio does not have congestion, and meets the user's travel requirements, so the planned route with the largest length ratio can be determined as the planned route to be traveled; when the congestion index of the planned route with the largest length ratio is not less than the preset congestion index, it means that the road of the planned route with the largest length ratio has congestion, and does not meet the user's travel requirements, and it is necessary to reselect a planned route as the planned route to be traveled.

[0037] In step S05 , if the congestion index of the planned route with the largest length ratio is less than the preset congestion index, the planned route with the largest length ratio is determined as the planned route to be traveled.

[0038] In the disclosed embodiment, if the congestion index of the planned route with the largest length ratio is less than the preset congestion index, it means that the road of the planned route with the largest length ratio is not congested and meets the user's travel requirements. Therefore, the planned route with the largest length ratio can be determined as the planned route to be traveled. For example, the congestion indexes released by map software and traffic management are usually 0, 1, 2...10, 10+. According to different value ranges, the congestion index can be divided into five levels, among which 0~2, 2~4, 4~6, 6~8, and 8~10+ correspond to the five levels of "smooth", "basically smooth", "mild congestion", "moderate congestion", and "severe congestion" respectively. The higher the congestion index value, the more serious the traffic congestion situation. The preset congestion index is 3, and the congestion index of the planned route with the largest length ratio is 0. At this time, the congestion index of the planned route with the largest length ratio is less than the preset congestion index, which means that the planned route with the largest length ratio is currently "smooth", which meets the user's travel requirements. Therefore, the planned route with the largest length ratio is determined as the planned route to be traveled. It should be noted that the numerical values ​​listed in the embodiments of the present disclosure, such as "0~10+", "0~2", "2~4", "4~6", "6~8", "8~10+", "3" and "0", are only used to illustrate the contents of the embodiments of the present disclosure and are not specific limiting values.

[0039] In step S06, if the congestion index of the planned route with the largest length ratio is not less than the preset congestion index, the planned route with the largest length ratio is selected from the remaining planned routes, and the process returns to the step of detecting whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index.

[0040] In the embodiment of the present disclosure, if the congestion index of the planned route with the largest length ratio is not less than the preset congestion index, for example: the preset congestion index is 3, and the congestion index of the planned route with the largest length ratio is 8, then the congestion index of the planned route with the largest length ratio is greater than the preset congestion index, indicating that the road of the planned route with the largest length ratio is congested, which does not meet the user's travel requirements, and it is necessary to reselect a planned route as the planned route to be traveled. Therefore, it is necessary to select another planned route with the largest length ratio from the remaining planned routes for detection, and the planned route with the largest length ratio among the remaining planned routes will be used as the planned route with the largest length ratio, and return to the step of executing the detection to see whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index. It should be noted that the numerical values ​​listed in the embodiment of the present disclosure, such as "3" and "8", are only used to illustrate the content of the embodiment of the present disclosure, and are not specific limiting values.

[0041] In the embodiment of the present disclosure, the planned route with the largest length ratio is selected from all planned routes; whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index is detected; if the congestion index of the planned route with the largest length ratio is less than the preset congestion index, the planned route with the largest length ratio is determined as the planned route to be traveled; if the congestion index of the planned route with the largest length ratio is not less than the preset congestion index, the planned route with the largest length ratio is selected from the remaining planned routes, and the step of detecting whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index is returned. This solves the technical problem in the related art of how to select a better planned route to be traveled for the user to provide the user with a better driving experience.

[0042] In some embodiments, the step of dividing the planned route to be traveled into M driving sections may include: dividing the planned route to be traveled into M driving sections based on the target section, wherein a target section is a first-class driving section, a non-target section is a second-class driving section, and M is a positive integer, and the non-target route is not in the high-precision map coverage area.

[0043] In the embodiment of the present disclosure, the planned route to be traveled is divided into M driving sections based on the target section, wherein one target section is a first-class driving section, one non-target section is a second-class driving section, and M is a positive integer, and the non-target route is not in the high-precision map coverage area. For example: Please continue to refer to Figure 2. As shown in Figure 2, A and E represent the starting point and the end point respectively, and sections BC and DE are target sections and are in the high-precision map coverage area, and then sections AB and CD are non-target sections and are not in the high-precision map coverage area. Therefore, the planned route to be traveled is divided into 4 driving sections based on the target section, and one target section is regarded as a first-class driving section (that is, section BC or section DE is both a target section and a first-class driving section of the planned route to be traveled), and one non-target section is regarded as a second-class driving section (that is, section AB or section CD is both a non-target section and a second-class driving section of the planned route to be traveled). It should be noted that the numerical values ​​listed in the embodiments of the present disclosure, such as "4", are only used to illustrate the content of the embodiments and are not specific limiting values.

[0044] In the embodiment of the present disclosure, the planned route to be driven is divided into M driving segments based on the target segment, wherein a target segment is a first-class driving segment, a non-target segment is a second-class driving segment, and M is a positive integer. The non-target route is not in the high-precision map coverage area, thereby solving the technical problem of how to divide the planned driving route into driving segments in the related art.

[0045] In step S20 , when the distance from the vehicle to any driving section is less than a first preset distance, prompt information of the type of any driving section is output.

[0046] In the embodiment of the present disclosure, when the distance between the vehicle and any driving section is less than a first preset distance, it is necessary to promptly output prompt information of the type of any section to the driver to remind the user of the type of driving section the vehicle is about to enter, so that the user can understand the current environmental conditions and increase trust.

[0047] During the implementation process, the types of any of the above-mentioned driving sections may include high-precision map sections and non-high-precision map sections, that is, for high-precision map sections, prompt information related to the high-precision map sections is output, and for non-high-precision map sections, prompt information related to the non-high-precision map sections is output. For example: Please continue to refer to Figure 2. As shown in Figure 2, A and E represent the starting point and the end point respectively, sections BC and DE are target sections, sections AB and CD are non-target sections, and the first preset distance is 100 meters. When the distance between the vehicle and the section BC is less than 100 meters, the user needs to be prompted by voice or text, such as "100 meters ahead, you will enter the high-precision map section, please pay attention!", so that the user can understand the current environmental conditions and increase their sense of trust.

[0048] In step S30, when the vehicle is in any driving section, the target function state and the road condition within a second preset distance range in front of the vehicle are detected to see whether they meet the target function operation conditions.

[0049] In the disclosed embodiment, when a vehicle is on any driving section, it is necessary to detect whether the target state and the road conditions within a second preset distance range ahead of the vehicle meet the target function operating conditions. Constantly monitoring the target function state and the road conditions within the second preset distance range ahead of the vehicle enables early determination of whether the vehicle can operate the target function, thereby ensuring the user's driving safety and avoiding traffic accidents. If both the target function state and the road conditions within the second preset distance range ahead of the vehicle meet the target function operating conditions, the vehicle can operate the target function within the second preset distance range ahead. If either the target function state or the road conditions within the second preset distance range ahead of the vehicle do not meet the target function operating conditions, the vehicle cannot operate the target function within the second preset distance range ahead. Any driving section includes sections within high-precision map coverage areas and sections outside of high-precision map coverage areas. Target functions include automatic assisted navigation driving, adaptive cruise control, and smart cruise assist plus adaptive cruise control.

[0050] In some embodiments, when any driving section is a first-class driving section, the target function operating conditions may be: the automatic assisted navigation driving function state is normal, the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the congestion index within the second preset distance range in front of the vehicle is less than the preset congestion index.

[0051] In the disclosed embodiment, when any driving section is a first-class driving section, the operating conditions of the automatic assisted navigation driving function are obtained from the local database (Note: the first-class driving section is in the high-precision map coverage area, and the automatic assisted navigation driving function is implemented based on high-precision map data, so it can only be operated in the high-precision map coverage area), wherein the operating conditions of the automatic assisted navigation driving function are that the automatic assisted navigation driving function state is normal (Note: the associated systems and components of the automatic assisted navigation driving function are normal and fault-free, which is a normal state; conversely, a fault is an abnormal state), the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the congestion index within the second preset distance range in front of the vehicle is less than the preset congestion index. In this way, the automatic assisted navigation driving function state of the vehicle and the road conditions within the second preset distance range in front of the vehicle are constantly monitored, so that it can be determined in advance whether the vehicle can run the automatic assisted navigation driving function, while ensuring the user's driving safety and avoiding traffic accidents. For example, referring to Figure 2, A and E represent the starting point and end point, respectively. Sections BC and DE are target sections, and sections AB and CD are non-target sections. If any of the driving sections is a first-class driving section, and a first-class driving section is section BC, the vehicle is currently in section BC, the vehicle's automatic assisted navigation driving function is in a normal state, the lane line clarity within 50 meters ahead of the vehicle is 100%, which is greater than the preset clarity of 95%, and the congestion index within 50 meters ahead of the vehicle is 0, which is less than the preset congestion index of 3. Therefore, it is determined that the operating conditions of the automatic assisted navigation driving function are met. It should be noted that the numerical values ​​listed in the embodiments of this disclosure, such as "section BC," "50 meters," "100%," "95%," "0," and "3," are merely for illustrative purposes and are not intended to be limiting values.

[0052] In some embodiments, when any driving section is a second-category driving section, the target function operating condition may be: the adaptive cruise control function state is in a normal state, and the driving characteristics of other vehicles within a second preset distance range in front of the vehicle are preset driving characteristics.

[0053] In the disclosed embodiment, when any driving section is a second-category driving section, the operating conditions of the adaptive cruise control function are obtained from the local database (Note: second-category driving sections are not in the high-precision map coverage area, and the adaptive cruise control function does not need to be combined with high-precision map data to operate). The operating conditions of the adaptive cruise control function are that the adaptive cruise control function is in a normal state (Note: the associated systems and components of the adaptive cruise control function are normal and without faults, which is a normal state; conversely, a fault is an abnormal state), and the driving characteristics of other vehicles within a second preset distance range in front of the vehicle are preset driving characteristics. In this way, by constantly monitoring the status of the vehicle's adaptive cruise control function and the driving characteristics of other vehicles within the second preset distance range in front of the vehicle as preset driving characteristics, it is possible to determine in advance whether the vehicle can operate the adaptive cruise control function, while ensuring the user's driving safety and avoiding traffic accidents. In some embodiments, the preset driving characteristics may include a vehicle speed within a preset speed range, a vehicle distance greater than a preset vehicle distance, and a driving trajectory that does not cut in. For example, referring to Figure 2, A and E represent the starting point and end point, respectively. Sections BC and DE are target sections, and sections AB and CD are non-target sections. If any of the driving sections is a second-category driving section, and one of the second-category driving sections is section CD, then the vehicle is currently in section CD, the adaptive cruise control function is in a normal state, and the driving characteristics of other vehicles within a second preset distance range ahead of the vehicle are preset driving characteristics (i.e., the speeds of other vehicles within 50m ahead of the vehicle are all between 60km / h and 80km / h, the distances between other vehicles are all greater than 20m, and the driving trajectories of other vehicles do not include any congestion). Therefore, it is determined that the operating conditions for the adaptive cruise control function are met. It should be noted that the values ​​listed in the embodiments of the present disclosure, such as "section CD," "50m," "60km / h to 80km / h," and "20m," are merely for illustrative purposes and are not intended to be limiting values.

[0054] In some embodiments, when any driving section is a second-class driving section, the target function operating conditions may be: the intelligent cruise assist function status and the adaptive cruise control function status are both in normal state, the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the driving characteristics of other vehicles within the second preset distance range in front of the vehicle are the preset driving characteristics.

[0055] In the disclosed embodiment, when any driving section is a Class II driving section, the operating conditions for the smart cruise assist function and the adaptive cruise control function are obtained from the local database (Note: Class II driving sections are not within the coverage area of ​​high-precision maps, and neither the smart cruise assist function nor the adaptive cruise control function requires high-precision map data to operate). The operating conditions for the smart cruise assist function and the adaptive cruise control function are that both the smart cruise assist function and the adaptive cruise control function are in a normal state (Note: The normal state is defined as the state where the associated systems and components of the smart cruise assist function and the adaptive cruise control function are functioning normally; conversely, the abnormal state is defined as the state where the associated systems and components of the smart cruise assist function and the adaptive cruise control function are functioning normally and fault-free, respectively), the lane line clarity within a second preset distance range ahead of the vehicle is greater than a preset clarity, and the driving characteristics of other vehicles within the second preset distance ahead of the vehicle are preset driving characteristics. By constantly monitoring the smart cruise assist function status, the adaptive cruise control function status, the lane line clarity within the second preset distance ahead of the vehicle is greater than a preset clarity, and the driving characteristics of other vehicles within the second preset distance ahead of the vehicle are preset driving characteristics, it is possible to determine in advance whether the smart cruise assist function and the adaptive cruise control function can be operated, thereby ensuring the user's driving safety and avoiding traffic accidents. In some embodiments, the preset driving characteristics may include a vehicle speed within a preset speed range, a vehicle distance greater than a preset vehicle distance, and a driving trajectory that does not involve cutting in. For example, referring to FIG. 2 , as shown in FIG. 2 , A and E represent the starting point and end point, respectively. Sections BC and DE are target sections, and sections AB and CD are non-target sections. If any of the driving sections is a second-class driving section, and one of the second-class driving sections is section CD, then the vehicle is currently in section CD, the smart cruise assist function and the adaptive cruise control function are in normal states, the lane line clarity within 50 meters ahead of the vehicle is 100% and greater than a preset clarity of 95%, and the driving characteristics of other vehicles are preset driving characteristics (i.e., the speeds of other vehicles within 50 meters ahead of the vehicle are all between 60 km / h and 80 km / h, the distances between other vehicles are all greater than 20 meters, and the driving trajectories of other vehicles do not involve cutting in). Therefore, it is determined that the operating conditions of the smart cruise assist function and the adaptive cruise control function are met. It should be noted that the numerical values ​​listed in the embodiments of the present disclosure, such as "road section CD", "50m", "100%", "95%", "60km / h~80km / h" and "20m", are only used to illustrate the contents of the embodiments of the present disclosure and are not specific limiting values.

[0056] In step S40, if the target function state and the road condition within the second preset distance range in front of the vehicle meet the target function operation conditions, the vehicle is controlled to operate the target function and a prompt message indicating that the target function is operating is output.

[0057] In the embodiment of the present disclosure, if the target function state and the road condition within the second preset distance range in front of the vehicle both meet the target function operation conditions, indicating that the vehicle can operate the target function within the second preset distance range in front and there are no driving safety hazards, the vehicle is controlled to operate the target function, and a prompt message indicating that the target function is operating is output. For example: if the automatic assisted navigation driving function state and the road condition within the second preset distance range in front of the vehicle meet the automatic assisted navigation driving operation conditions, the vehicle is controlled to operate the automatic assisted navigation driving function, and the automatic assisted navigation driving function is prompted to complete function activation through voice, pop-up windows, dynamic effects, buttons and other multi-dimensional methods. At the same time, the automatic assisted navigation driving function icon changes from gray to highlighted, a blue path guide line appears in front of the current lane where the vehicle is located, and the lane rendering of the actual driving environment (including lane line type, number of lanes, route name, road edge, etc.) together represent the operating status of the automatic assisted navigation driving function.

[0058] In the embodiment of the present disclosure, the planned route to be driven is divided into M driving sections, where M is a positive integer, so that the vehicle controller can match the optimal intelligent driving function for the vehicle for any driving section, which brings great convenience to the user's travel; when the distance between the vehicle and any driving section is less than a first preset distance, prompt information of the type of any driving section is output; when the vehicle is still some distance away from entering any driving section, the user is reminded in advance of the type of driving section the vehicle is about to enter, so that the user can understand the current environmental conditions and increase trust; when the vehicle is in any driving section, the target function state and the road conditions within the second preset distance range in front of the vehicle are detected to see whether they meet the target function operation conditions; when the vehicle is in any driving section, the target function state is detected at all times and the road conditions within the second preset distance range in front of the vehicle, the target function will only be run if the target function running conditions are met, which effectively ensures the user's driving safety and avoids traffic accidents; if the target function status and the road conditions within the second preset distance range in front of the vehicle meet the target function running conditions, the vehicle controller will control the vehicle to run the target function and output a prompt message that the target function is running. If the target function status and the road conditions within the second preset distance range in front of the vehicle meet the target function running conditions, the vehicle controller will control the vehicle to run the target function and always output a prompt message that can represent that the target function is running, so that the user can always know that the target function is running. At the same time, if the target function exits due to an emergency, the user can also discover it in time to avoid causing a major traffic accident. The disclosed embodiment solves the technical problem in the related art that it is impossible to effectively, reasonably and timely provide prompts for function status nodes, and that it can increase the driver's understanding, control and experience of the function while ensuring driving safety.

[0059] In a second aspect, an embodiment of the present disclosure also provides a human-machine co-driving control device.

[0060] FIG4 is a schematic diagram of the functional modules of a human-machine co-driving control device according to some embodiments of the present disclosure. As shown in FIG4 , in some embodiments, the human-machine co-driving control device may include: a division module 10 for dividing the planned route to be driven into M driving sections, where M is a positive integer; an output module 20 for outputting prompt information of the type of any driving section when the distance from the vehicle to entering any driving section is less than a first preset distance; a detection module 30 for detecting whether the target function state and the road conditions within a second preset distance range in front of the vehicle meet the target function operation conditions when the vehicle is in any driving section; and the output module 20 is further configured to control the vehicle to operate the target function if the target function state and the road conditions within the second preset distance range in front of the vehicle meet the target function operation conditions, and output a prompt information indicating that the target function is operating.

[0061] In some embodiments, the human-machine co-driving control device may further include a determination module (not shown) for: obtaining N planned routes based on the starting point parameters and end point parameters of the planned route to be traveled, where N is a positive integer; for each planned route, calculating the length proportion of all target sections contained in the planned route in the planned route, where the target sections are in the high-precision map coverage area; and selecting the planned route with the largest length proportion from all planned routes, and determining the planned route with the largest length proportion as the planned route to be traveled.

[0062] In some embodiments, the determination module can also be used to: obtain N planned routes based on the starting point parameters and end point parameters of the planned route to be traveled, where N is a positive integer; for each planned route, calculate the length ratio of all target road sections contained in the planned route in the planned route, where the target road sections are in the high-precision map coverage area; select the planned route with the largest length ratio from all planned routes; detect whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index; if the congestion index of the planned route with the largest length ratio is less than the preset congestion index, determine the planned route with the largest length ratio as the planned route to be traveled; if the congestion index of the planned route with the largest length ratio is not less than the preset congestion index, select the planned route with the largest length ratio from the remaining planned routes, and return to execute the step of detecting whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index.

[0063] In some embodiments, the division module 10 can also be used to: divide the planned route to be driven into M driving segments based on the target segment, wherein a target segment is a first-class driving segment, a non-target segment is a second-class driving segment, and M is a positive integer, and the non-target route is not in the high-precision map coverage area.

[0064] In some embodiments, when any driving section is a first-class driving section, the target function operating conditions may be: the automatic assisted navigation driving function state is normal, the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the congestion index within the second preset distance range in front of the vehicle is less than the preset congestion index.

[0065] In some embodiments, when any driving section is a second-category driving section, the target function operating condition may be: the adaptive cruise control function state is in a normal state, and the driving characteristics of other vehicles within a second preset distance range in front of the vehicle are preset driving characteristics.

[0066] In some embodiments, when any driving section is a second-class driving section, the target function operating conditions may be: the intelligent cruise assist function status and the adaptive cruise control function status are both in normal state, the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the driving characteristics of other vehicles within the second preset distance range in front of the vehicle are the preset driving characteristics.

[0067] In some embodiments, the preset driving characteristics include a vehicle speed within a preset vehicle speed range, a vehicle distance greater than a preset vehicle distance, and a driving trajectory that does not involve cutting in traffic.

[0068] In some embodiments, the types of any driving segment include high-precision map segments and non-high-precision map segments.

[0069] Among them, the functional implementation of each module in the above-mentioned human-machine co-driving control device corresponds to the various steps in the above-mentioned human-machine co-driving control method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0070] In a third aspect, an embodiment of the present disclosure provides a human-machine co-driving control device, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0071] Figure 5 is a schematic diagram of the hardware structure of a human-machine co-driving control device according to some embodiments of the present disclosure. In the embodiments of the present disclosure, the human-machine co-driving control device may include a processor, a memory, a communication interface, and a communication bus.

[0072] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0073] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces, used to interconnect components within the HMI device, as well as interfaces used to interconnect the HMI device with other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber optic, and ATM interfaces; user devices can include displays and keyboards.

[0074] 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.

[0075] The processor may be a general-purpose processor, which may call the human-machine co-driving control program stored in the memory and execute the human-machine co-driving control method provided by the embodiment of the present disclosure. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the human-machine co-driving control program is called may refer to the various embodiments of the human-machine co-driving control method disclosed herein, and will not be repeated here.

[0076] Those skilled in the art will understand that the hardware structure shown in FIG5 does not constitute a limitation on the present disclosure, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0077] In a fourth aspect, an embodiment of the present disclosure also provides a computer-readable storage medium.

[0078] The computer-readable storage medium of the present invention stores a human-machine co-driving control program, wherein when the human-machine co-driving control program is executed by a processor, the steps of the human-machine co-driving control method as described above are implemented.

[0079] Among them, the method implemented when the human-machine co-driving control program is executed can refer to the various embodiments of the human-machine co-driving control method disclosed in the present invention, and will not be repeated here.

[0080] The technical solutions provided by the embodiments of the present disclosure bring beneficial effects including:

[0081] By dividing the planned route to be driven into M driving sections, where M is a positive integer, the vehicle controller can match the optimal intelligent driving function for the vehicle for any driving section, which brings great convenience to the user's travel; when the distance between the vehicle and entering any driving section is less than a first preset distance, a prompt information of the type of any driving section is output; when the vehicle is still some distance away from entering any driving section, the user is reminded in advance of the type of the driving section the vehicle is about to enter, so that the user can understand the current environmental conditions and increase the sense of trust; when the vehicle is in any driving section, the target function state and the road conditions within the second preset distance range in front of the vehicle are detected to see whether they meet the target function operation conditions; when the vehicle is in any driving section, the target function state and the vehicle are detected at all times. The target function will only be executed if the road conditions within the second preset distance range in front of the vehicle meet the target function operation conditions. This effectively ensures the user's driving safety and avoids traffic accidents. If the target function status and the road conditions within the second preset distance range in front of the vehicle meet the target function operation conditions, the vehicle controller will control the vehicle to operate the target function and output a prompt message indicating that the target function is running. If the target function status and the road conditions within the second preset distance range in front of the vehicle meet the target function operation conditions, the vehicle controller will control the vehicle to operate the target function and output a prompt message indicating that the target function is running at all times. In this way, the user can always know that the target function is running. At the same time, if the target function exits due to an emergency, the user can also discover it in time to avoid causing a major traffic accident. This solves the technical problems in related technologies that cannot effectively, reasonably and timely provide prompts for function status nodes, and that can increase the driver's understanding, control and experience of the function while ensuring driving safety.

[0082] It should be noted that the serial numbers of the above-mentioned embodiments of the present disclosure are only for description and do not represent the advantages or disadvantages of the embodiments.

[0083] The terms "including" and "having" and any variations thereof in the specification and claims of the present disclosure 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 be different types.

[0084] In the description of the embodiments of the present disclosure, the words "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 disclosure should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0085] In the description of the embodiments of the present disclosure, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “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 disclosure, “multiple” refers to two or more than two.

[0086] In some processes described in the embodiments of the present disclosure, multiple operations or steps are included that appear in a specific order. However, 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 disclosure or may be executed in parallel. The sequence numbers of the operations are only used to distinguish different operations and the sequence numbers themselves do 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.

[0087] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course, by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present disclosure, 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, magnetic disk, optical disk) as described above, and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of the present disclosure.

[0088] The above are only preferred embodiments of the present disclosure and are not intended to limit the patent scope of the present disclosure. Any equivalent structure or equivalent process transformation made using the contents of the present disclosure and the drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present disclosure.

Claims

1. A human-machine co-driving control method, comprising: Divide the planned route to be driven into M driving sections, where M is a positive integer; When the distance from the vehicle to any driving section is less than a first preset distance, outputting prompt information of the type of any driving section; When the vehicle is in any of the driving sections, detecting whether the target function state and the road conditions within a second preset distance range in front of the vehicle meet the target function operation conditions; and If the target function state and the road condition within a second preset distance range in front of the vehicle meet the target function operation condition, the vehicle is controlled to operate the target function, and a prompt message indicating that the target function is operating is output.

2. The human-machine co-driving control method according to claim 1, before the step of dividing the planned route to be driven into M driving sections, further comprising: According to the starting point parameters and the end point parameters of the planned route to be traveled, N planned routes are obtained, where N is a positive integer; For each planned route, calculate the length ratio of all target road sections included in the planned route in the planned route, wherein the target road sections are in the high-precision map coverage area; and The planned route with the largest length ratio is selected from all the planned routes, and the planned route with the largest length ratio is determined as the planned route to be driven.

3. The human-machine co-driving control method according to claim 1, before the step of dividing the planned route to be driven into M driving sections, further comprising: According to the starting point parameters and the end point parameters of the planned route to be traveled, N planned routes are obtained, where N is a positive integer; For each planned route, calculate the length ratio of all target road sections included in the planned route in the planned route, wherein the target road sections are in the high-precision map coverage area; Select the planned route with the largest length ratio from all planned routes; Detecting whether the congestion index of the planned route with the largest length ratio is less than a preset congestion index; If the congestion index of the planned route with the largest length ratio is less than the preset congestion index, determining the planned route with the largest length ratio as the planned route to be driven; and If the congestion index of the planned route with the largest length ratio is not less than the preset congestion index, the planned route with the largest length ratio is selected from the remaining planned routes, and the process returns to execute the step of detecting whether the congestion index of the planned route with the largest length ratio is less than the preset congestion index.

4. The human-machine co-driving control method according to claim 2 or 3, wherein: The step of dividing the planned route to be traveled into M travel sections comprises: Based on the target section, the planned route to be driven is divided into M driving sections, wherein one target section is a first-category driving section, one non-target section is a second-category driving section, and M is a positive integer, and the non-target section is not in the high-precision map coverage area.

5. The human-machine co-driving control method according to claim 4, wherein: When any driving section is a driving section of the first type, the target function operation condition is: The status of the automatic assisted navigation driving function is normal, the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the congestion index within the second preset distance range in front of the vehicle is less than the preset congestion index.

6. The human-machine co-driving control method according to claim 4, wherein: When any driving section is a driving section of the second type, the target function operation condition is: The adaptive cruise control function state is a normal state, and the driving characteristics of other vehicles within a second preset distance range in front of the vehicle are preset driving characteristics.

7. The human-machine co-driving control method according to claim 4, wherein: When any driving section is a driving section of the second type, the target function operation condition is: The smart cruise assist function and the adaptive cruise control function are both in normal state, the lane line clarity within the second preset distance range in front of the vehicle is greater than the preset clarity, and the lane lines of other vehicles within the second preset distance range in front of the vehicle are The driving characteristics are preset driving characteristics.

8. The human-machine co-driving control method according to claim 6 or 7, wherein: The preset driving characteristics include a vehicle speed within a preset vehicle speed range, a vehicle distance greater than a preset vehicle distance, and a driving trajectory that does not involve jamming.

9. The human-machine co-driving control method according to claim 1, wherein: The types of any driving section include high-precision map sections and non-high-precision map sections.

10. A human-machine co-driving control device, comprising: A division module, used to divide the planned route to be driven into M driving sections, where M is a positive integer; An output module, configured to output prompt information of the type of any driving section when the distance of the vehicle from entering any driving section is less than a first preset distance; a detection module, used for detecting whether the target function state and the road condition within a second preset distance range in front of the vehicle meet the target function operation condition when the vehicle is in any of the driving sections; and The output module is also used to control the vehicle to run the target function and output a prompt message that the target function is running if the target function state and the road conditions within a second preset distance range in front of the vehicle meet the target function running conditions.

11. A human-machine co-driving control device, comprising a processor, a memory, and a human-machine co-driving control program stored in the memory and executable by the processor, wherein when the human-machine co-driving control program is executed by the processor, the steps of the human-machine co-driving control method as described in any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium, on which a human-machine co-driving control program is stored, wherein when the human-machine co-driving control program is executed by a processor, the steps of the human-machine co-driving control method as described in any one of claims 1 to 9 are implemented.

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