An assisted driving control planning method and device, and a vehicle

Abstract

The application discloses an assisted driving control planning method and device and a vehicle, and belongs to the technical field of intelligent driving. A specific embodiment of the method comprises the following steps: determining that a vehicle is executing a partial automatic driving program and a navigation assisted driving system is not started, and acquiring data corresponding to a current position and a preset condition dimension; calling an identification model, and obtaining a virtual destination according to the current position and the data; generating a virtual planning driving route through the current position and the virtual destination, and controlling the vehicle to travel by using the virtual planning driving route. The method can reduce the driving takeover rate and obviously improve the technical effect of user driving experience.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and in particular to an assisted driving control planning method, device, and vehicle. Background Technology

[0002] Currently, when using some autonomous driving programs (Level 2 assisted driving), the lack of navigation guidance means that the vehicle can only perform simple functions such as following the car in front and keeping in the lane. It cannot even slow down when passing through intersections or turn or go straight when passing through traffic lights, resulting in a high rate of driver takeover and a poor driving experience for users. Summary of the Invention

[0003] In view of this, the present invention provides an assisted driving control planning method, device and vehicle, which can reduce the rate of driver takeover and significantly improve the user's driving experience.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides an assisted driving control planning method, comprising: determining that the vehicle is executing a partially automated driving program and the navigation assisted driving system is not activated; acquiring data corresponding to the current location and preset condition dimensions; invoking a recognition model to obtain a virtual destination based on the current location and the data; generating a virtual planned driving route through the current location and the virtual destination; and controlling the vehicle's driving using the virtual planned driving route.

[0005] Optionally, it further includes: in response to a driving intervention command, identifying an intervention signal to control vehicle driving; reacquiring data corresponding to the current location and preset condition dimensions, and calling an identification model to determine a new virtual destination; generating a new virtual planned driving route based on the reacquisitioned current location and the new virtual destination, and controlling vehicle driving.

[0006] Optionally, it further includes: in response to a driving takeover command, stopping the acquisition of data corresponding to the current location and preset condition dimensions, or stopping the acquisition of a virtual destination based on the current location and the data, or stopping the generation of a virtual planned driving route based on the current location and the virtual destination, or stopping the control of vehicle driving using the virtual planned driving route.

[0007] Optionally, this includes: acquiring historical driving data of the vehicle and training a preset recognition model using the origin, destination, and preset condition dimensions.

[0008] Optionally, the preset condition dimension includes condition attributes and corresponding condition attribute values; and the historical driving data stores a mapping relationship: origin, destination and at least one condition dimension.

[0009] Optionally, it further includes: monitoring the incremental status of the vehicle's historical driving data; in response to the incremental status meeting a preset quantity threshold, acquiring the current historical driving data, and training a preset recognition model using the origin, destination, and preset condition dimensions.

[0010] Optionally, it further includes: counting the number of driving intervention commands; in response to the number of commands exceeding a preset threshold, acquiring current historical driving data, and training a preset recognition model using the origin, destination, and preset condition dimensions.

[0011] Optionally, after invoking the recognition model, the method further includes: obtaining at least two virtual destinations based on the current location and the data, and outputting the at least two virtual destinations; in response to the selection operation of the virtual destination, generating a virtual planned driving route based on the current location and the selected virtual destination.

[0012] Optionally, after obtaining the data corresponding to the preset condition dimension, the method further includes: determining that the data corresponding to the preset condition dimension is empty, pulling another preset condition dimension in the same cluster as the preset condition dimension from the preset condition dimension pool, and then obtaining the data corresponding to the other preset condition dimension.

[0013] Secondly, embodiments of the present invention provide an assisted driving control planning device, comprising: an acquisition unit, a processing unit, and a control unit, wherein the acquisition unit is configured to determine that the vehicle is executing a partial autonomous driving program and the navigation assisted driving system is not activated, and acquire data corresponding to the current location and preset condition dimensions; the processing unit is configured to invoke a recognition model and obtain a virtual destination based on the current location and the data; the control unit is configured to generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's driving using the virtual planned driving route.

[0014] Thirdly, embodiments of the present invention provide a vehicle including a processor, a memory, and a display, wherein the processor is configured to acquire and execute code in the memory to perform the method provided in the first aspect embodiments described above.

[0015] Fourthly, embodiments of the present invention provide an in-vehicle electronic device for assisted driving control planning, comprising: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement an assisted driving control planning method as described in the above embodiments of the present invention.

[0016] Fifthly, embodiments of the present invention provide a computer-readable storage medium for assisted driving control planning, wherein a computer program for implementing assisted driving control planning is stored thereon, and the computer program, when executed by an on-board processor, implements an assisted driving control planning method according to an embodiment of the present invention.

[0017] The technical solution of the above invention has the following advantages or beneficial effects: The present invention can obtain data corresponding to the current location and preset condition dimensions, call the recognition model to obtain the virtual destination, generate a virtual planned driving route through the current location and the virtual destination, and control the vehicle driving using the virtual planned driving route, thereby enabling the vehicle to reach the destination more intelligently under the function of partial autonomous driving program and without activating the navigation assisted driving system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the main steps in a first assisted driving control planning scenario provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a virtual driving route planning provided according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the main steps in a second assisted driving control planning scenario provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of the main steps of a third assisted driving control planning scenario provided by an embodiment of the present invention; Figure 5 This is a schematic diagram of the main steps of the fourth assisted driving control planning scenario provided by the embodiments of the present invention; Figure 6 This is a schematic diagram of the main steps of the fifth assisted driving control planning scenario provided by an embodiment of the present invention; Figure 7 This is a schematic diagram of the main steps of the sixth assisted driving control planning scenario provided by the embodiments of the present invention; Figure 8 This is a schematic diagram of the main steps of the seventh assisted driving control planning scenario provided by the embodiments of the present invention; Figure 9 This is a schematic diagram of the main steps of the eighth assisted driving control planning scenario provided by the embodiments of the present invention; Figure 10 This is a schematic diagram of the main units of an assisted driving control planning device according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of a vehicle according to an embodiment of the present invention; Figure 12 This is an exemplary vehicle system architecture diagram to which embodiments of the present invention can be applied; Figure 13 This is a schematic diagram of the structure of a computer system suitable for implementing embodiments of the present invention. Detailed Implementation

[0019] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] It should be noted that, unless otherwise specified, the embodiments of the present invention and the technical features thereof can be combined with each other.

[0021] Furthermore, the terms "first," "second," and "third," etc., included in the terminology of this invention are used to distinguish similar objects and are not necessarily used to describe a specific number or order. It should be understood that such terms can be used interchangeably where appropriate; this is merely a distinguishing method used in the embodiments of this invention when describing objects with the same attributes.

[0022] Furthermore, the vehicles involved in the embodiments of the present invention may be internal combustion engine vehicles that use an engine as a power source, hybrid vehicles that use an engine and an electric motor as power sources, electric vehicles that use an electric motor as a power source, etc.

[0023] Figure 1 This is a schematic diagram illustrating the main steps of a first assisted driving control planning method according to an embodiment of the present invention. Figure 1 As shown, this assisted driving control planning method mainly includes the following steps: Step S101: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain the data corresponding to the current location and preset condition dimensions.

[0024] In this embodiment, the present invention can automatically acquire data corresponding to the vehicle's current location and preset condition dimensions when the vehicle is in a partially automated driving program (Level 2 assisted driving) and the navigation-assisted driving system is not activated. Here, the condition dimension refers to the constraints for determining the virtual destination, and the virtual destination refers to a destination not set by the vehicle (i.e., a destination the vehicle might travel to without setting a destination). For example: Figure 2 As shown, point A is the current location of the vehicle. The obtained conditional dimensions can include conditional attributes such as time and corresponding conditional attribute value of 7:00 AM, conditional attributes such as origin and location and corresponding conditional attribute value of home.

[0025] In a preferred embodiment, the preset condition dimension may include condition attributes and corresponding condition attribute values. In a more preferred embodiment, the preset condition dimension may be stored in key-value pairs. Examples include: a condition attribute of time and its corresponding condition attribute value of 7 AM; a condition attribute of whether it is a weekday and its corresponding condition attribute value of yes; a condition attribute of origin and its corresponding condition attribute value of home; a condition attribute of destination and its corresponding condition attribute value of company, and so on.

[0026] Furthermore, it is worth noting that the embodiments of the present invention can collect historical driving data and store mapping relationships: origin, destination, and at least one conditional dimension. For each piece of historical driving data, there can be one or more conditional dimensions, which can be configured and collected according to actual scenarios and needs, and are not limited here.

[0027] In a further embodiment of the present invention, the data corresponding to the preset condition dimension obtained in step S101 may be empty. Therefore, in order to ensure the normal operation of the assisted driving control function, the present invention pulls another preset condition dimension from the preset condition dimension pool that belongs to the same cluster as the preset condition dimension, and then obtains the data corresponding to the other preset condition dimension. In this embodiment, the preset condition dimensions in historical driving data can be stored in the condition dimension pool and clustered. For example: Determining the data corresponding to the condition dimension: If the condition attribute value corresponding to the origin attribute is empty, then the origin building features belonging to the same cluster as the origin attribute are pulled from the condition dimension pool, and the corresponding condition attribute value is "residential building".

[0028] Step S102: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0029] In some embodiments, step S102 can acquire historical driving data of the vehicle and train a preset recognition model using the origin, destination, and preset condition dimensions. The historical driving data storage mapping relationship includes the origin, destination, and at least one condition dimension. The recognition model may include, but is not limited to, convolutional neural network models, recurrent neural network models, attention-based models, etc. Examples include: Figure 2 As shown, the recognition model is invoked. Input: vehicle location at point A, conditional attributes are time and corresponding conditional attribute value of 7:00 AM, conditional attributes are origin and corresponding conditional attribute value of home, output virtual destination: company location at point B.

[0030] In a further embodiment of the present invention, step S102 not only trains the recognition model before using it, but also triggers training of the recognition model when historical driving data is updated to a certain extent, thereby dynamically updating the recognition model. The historical driving data includes, but is not limited to, origin, destination, and preset condition dimensions. The specific implementation process may include: monitoring the incremental status of the vehicle's historical driving data; in response to the incremental status meeting a preset quantity threshold, acquiring the current historical driving data, and training a preset recognition model using the origin, destination, and preset condition dimensions.

[0031] In another further embodiment of the present invention, step S102 can also statistically analyze the driver's satisfaction with the automatic control of the vehicle (i.e., whether it meets the driver's expectations for the automatic control of the vehicle) to update and train the recognition model. The specific implementation process includes: counting the number of driving intervention commands; in response to the number exceeding a preset threshold, acquiring current historical driving data, and training a preset recognition model using the origin, destination, and preset condition dimensions. It can be seen that the embodiments of the present invention use the number of driving intervention commands as an indicator of the driver's satisfaction with the automatic control of the vehicle. For example, if the number of driving intervention commands is 6 times in half an hour, which is greater than the preset threshold of 5 times in half an hour, then current historical driving data can be acquired to update and train the recognition model. The driving intervention commands refer to the driver's control commands on the vehicle's movement during automatic control. For example, if the vehicle is currently moving straight during automatic control, but the driver turns the steering wheel to the left.

[0032] Furthermore, in a further embodiment of the present invention, step S102, where the identification model may obtain multiple virtual destinations, can output at least two virtual destinations obtained based on the current location and the data, allowing the driver to select one. In response to the selection of a virtual destination, a virtual planned driving route is generated using the current location and the selected virtual destination. It can be seen that the embodiments of the present invention effectively combine and consider the driver's needs with intelligent assisted driving, thereby providing a superior driving experience.

[0033] Step S103: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0034] In embodiments, the present invention can generate virtual planned driving routes using, but not limited to, global path planning models, behavioral decision models, etc., thereby controlling vehicle driving based on the virtual planned driving routes. Examples include: Figure 2As shown, the driving route from starting point A to virtual destination B can be represented by dashed lines, passing through points C, D, and E. That is, control the vehicle to slow down and turn on the right turn signal before approaching point C, slow down and turn on the left turn signal before approaching point D, and choose the straight lane to pass through point E.

[0035] In a further embodiment of the present invention, step S103 can dynamically adjust the virtual planned driving route. The specific implementation process includes: responding to a driving intervention command, identifying an intervention signal to control vehicle movement, and re-acquiring data corresponding to the current location and preset condition dimensions; calling an identification model to determine a new virtual destination; and generating a new virtual planned driving route using the re-acquired current location and the new virtual destination, thereby controlling vehicle movement. It can be seen that the embodiments of the present invention can regenerate the virtual planned driving route while responding to driving intervention commands in a timely manner, thereby maximizing the satisfaction of driving needs.

[0036] In another further embodiment of the present invention, step S103 can flexibly and at any time stop the generation of virtual destinations and virtual planned driving routes, and can flexibly and at any time stop controlling vehicle driving based on virtual planned driving routes. The specific implementation process includes: in response to a driving takeover command, stopping the acquisition of data corresponding to the current location and preset condition dimensions; or in response to a driving takeover command, stopping the generation of virtual planned driving routes based on the current location and the data; or in response to a driving takeover command, stopping the control of vehicle driving using the virtual planned driving routes. The driving takeover command refers to the command to transfer vehicle driving control to the driver.

[0037] Regarding the assisted driving control planning method provided in the embodiments of the present invention, such as Figure 3 A schematic diagram illustrating the main steps of a second type of assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S301: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain data corresponding to the current location and preset condition dimensions.

[0038] Step S302: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0039] Step S303: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0040] Step S304: In response to a driving intervention command, identify intervention signals to control vehicle movement.

[0041] Step S305: Reacquire the data corresponding to the current location and preset condition dimensions, and call the recognition model to determine the new virtual destination.

[0042] Step S306: By re-acquiring the current location and the new virtual destination, generate a new virtual planned driving route and control the vehicle's movement.

[0043] As can be seen, this invention can not only generate virtual planned driving routes and control vehicle driving in a partially automated driving program without the navigation-assisted driving system being activated, but also respond to driving intervention commands in a timely manner during the process of controlling vehicle driving, regenerate virtual planned driving routes and control vehicle driving, thereby maximizing the satisfaction of driving needs.

[0044] like Figure 4 A schematic diagram illustrating the main steps of a third type of assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S401: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain data corresponding to the current location and preset condition dimensions.

[0045] Step S402: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0046] Step S403: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0047] Step S404: In response to the driving takeover command, stop controlling the vehicle's movement using the virtual planned driving route.

[0048] As can be seen, this invention can not only generate virtual planned driving routes and control vehicle driving in a partially automated driving program without activating the navigation-assisted driving system, but also can stop the generation of virtual destinations and virtual planned driving routes and stop controlling vehicle driving at any time and flexibly.

[0049] like Figure 5 A schematic diagram illustrating the main steps of the fourth type of assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S501: Obtain the vehicle's historical driving data and train the preset recognition model using the origin, destination, and preset condition dimensions.

[0050] Step S502: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain data corresponding to the current location and preset condition dimensions.

[0051] Step S503: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0052] Step S504: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0053] As can be seen, this invention can not only generate virtual planned driving routes using a recognition model to control the vehicle to drive to the destination in a partially automated driving program without the navigation-assisted driving system being activated, but also dynamically update the recognition model to achieve accurate generation of virtual planned driving routes.

[0054] like Figure 6 A schematic diagram illustrating the main steps of the fifth type of assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S601: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain data corresponding to the current location and preset condition dimensions.

[0055] Step S602: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0056] Step S603: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0057] Step S604: Monitor the incremental status of the vehicle's historical driving data.

[0058] Step S605: In response to the incremental situation meeting the preset quantity threshold, obtain the current historical driving data, and train the preset recognition model using the origin, destination and preset condition dimensions.

[0059] As can be seen, the present invention can not only use the recognition model to generate virtual planned driving routes to control the vehicle to the destination, but also trigger the automatic update and training of the recognition model when the historical driving data is updated to a certain extent.

[0060] like Figure 7 A schematic diagram illustrating the main steps of the sixth type of assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S701: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain the data corresponding to the current location and preset condition dimensions.

[0061] Step S702: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0062] Step S703: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0063] Step S704: Count the number of driving intervention commands.

[0064] Step S705: In response to the number of times exceeding a preset threshold, acquire the current historical driving data, and train the preset recognition model using the origin, destination, and preset condition dimensions.

[0065] As can be seen, the present invention can not only generate virtual planned driving routes and control vehicle driving using the recognition model in a partially automated driving program without the navigation-assisted driving system being activated, but also update and train the recognition model based on the driver's satisfaction with the vehicle's automatic control driving.

[0066] like Figure 8 A schematic diagram illustrating the main steps of the seventh assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S801: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain the data corresponding to the current location and preset condition dimensions.

[0067] Step S802: Invoke the recognition model to obtain at least two virtual destinations based on the current location and the data.

[0068] Step S803: Output the at least two virtual destinations.

[0069] Step S804: In response to the selection of a virtual destination, generate a virtual planned driving route using the current location and the selected virtual destination.

[0070] Step S805: Control the vehicle's movement using the virtual planned driving route.

[0071] As can be seen, this invention can effectively combine and balance the needs of the driver and intelligent assisted driving even in a partially automated driving program without activating the navigation-assisted driving system, generating a virtual driving route and controlling the vehicle's movement to provide a better driving experience.

[0072] like Figure 9 A schematic diagram illustrating the main steps of the eighth assisted driving control planning scenario is shown. In this embodiment of the invention, the steps include the following: Step S901: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and obtain data corresponding to the current location and preset condition dimensions.

[0073] Step S902: Determine that the data corresponding to the preset condition dimension is empty, and pull another preset condition dimension in the same cluster as the preset condition dimension from the preset condition dimension pool.

[0074] Step S903: Obtain the data corresponding to the other preset condition dimension.

[0075] Step S904: Invoke the recognition model to obtain the virtual destination based on the current location and the data.

[0076] Step S905: Generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's movement using the virtual planned driving route.

[0077] As can be seen, this invention ensures that even with a partially automated driving program and the navigation-assisted driving system not activated, it can generate a virtual driving route and control the vehicle's movement, thus avoiding scenarios where the virtual driving route cannot be generated.

[0078] The above steps are provided only to help understand the method, structure, and core ideas of this invention. Those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the scope of protection of the claims.

[0079] Figure 10 This is a schematic diagram of the main units of an assisted driving control planning device according to an embodiment of the present invention. Figure 10 As shown, an assisted driving control planning device 1000 according to an embodiment of the present invention includes: an acquisition unit 1001, a processing unit 1002, and a control unit 1003, wherein, The acquisition unit 1001 is used to determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and to acquire data corresponding to the current location and preset condition dimensions. The processing unit 1002 is used to invoke the recognition model to obtain a virtual destination based on the current location and the data; The control unit 1003 is used to generate a virtual planned driving route based on the current location and the virtual destination, and to control the vehicle's movement using the virtual planned driving route.

[0080] In this embodiment of the invention, the acquisition unit 1001 is further configured to respond to a driving intervention command, identify an intervention signal to control vehicle driving, and reacquire data corresponding to the current location and preset condition dimensions; the processing unit 1002 is further configured to call an identification model to determine a new virtual destination; the control unit 1003 is further configured to generate a new virtual planned driving route based on the reacquired current location and the new virtual destination, and control vehicle driving.

[0081] In this embodiment of the invention, the acquisition unit 1001 is further configured to stop acquiring data corresponding to the current location and preset condition dimensions in response to a driving takeover command; or the processing unit 1002 is further configured to stop obtaining a virtual destination based on the current location and the data in response to a driving takeover command; or the control unit 1003 is further configured to stop generating a virtual planned driving route based on the current location and the virtual destination in response to a driving takeover command; or the control unit 1003 is further configured to stop controlling vehicle driving using the virtual planned driving route in response to a driving takeover command.

[0082] In this embodiment of the invention, the acquisition unit 1001 is further configured to acquire historical driving data of the vehicle and train a preset recognition model using the origin, destination and preset condition dimensions.

[0083] In this embodiment of the invention, the preset condition dimension includes condition attributes and corresponding condition attribute values; and the historical driving data stores a mapping relationship: origin, destination and at least one condition dimension.

[0084] In this embodiment of the invention, the acquisition unit 1001 is further configured to monitor the incremental status of the vehicle's historical driving data; in response to the incremental status meeting a preset quantity threshold, acquire the current historical driving data, and train a preset recognition model using the origin, destination, and preset condition dimensions.

[0085] In this embodiment of the invention, the acquisition unit 1001 is further configured to count the number of driving intervention commands; in response to the number of commands being greater than a preset threshold, acquire current historical driving data, and train a preset recognition model using the origin, destination, and preset condition dimensions.

[0086] In this embodiment of the invention, after the processing unit 1002 calls the recognition model, it further includes: obtaining at least two virtual destinations based on the current location and the data, and outputting the at least two virtual destinations; the control unit 1003 is also used to generate a virtual planned driving route in response to the selection operation of the virtual destination, based on the current location and the selected virtual destination.

[0087] In this embodiment of the invention, after the acquisition unit 1001 acquires the data corresponding to the preset condition dimension, it further includes: determining that the data corresponding to the preset condition dimension is empty, pulling another preset condition dimension in the same cluster as the preset condition dimension from the preset condition dimension pool, and then acquiring the data corresponding to the other preset condition dimension.

[0088] like Figure 11 As shown, an embodiment of the present invention provides a vehicle 1100, which may include the assisted driving control planning device 1000 provided in the above embodiments.

[0089] Figure 12 An exemplary vehicle system architecture 1200 is shown that can be applied to the driver assistance control planning method or driver assistance control planning device of the present invention.

[0090] like Figure 12 As shown, the vehicle system architecture 1200 may include various systems, such as an intelligent driving system 1201, a powertrain system 1202, a sensor system 1203, a control system 1204, one or more peripheral devices 1205, a power supply 1206, a computer system 1207, and a user interface 1208. Optionally, the vehicle system architecture 1200 may include more or fewer systems, and each system may include multiple components. Furthermore, each system and component of the vehicle system architecture 1200 can be interconnected via wired or wireless means.

[0091] The vehicle system architecture 1200 includes an intelligent driving system 1201, which can be in a fully or partially automated driving mode. For example, the intelligent driving system 1201 can automatically control the vehicle's driving without human interaction; the intelligent driving system 1201 can also control the vehicle's automated driving while in automated driving mode, and can also adjust its automated driving behavior through human interaction. Specifically, the intelligent driving system 1201 can also determine that the vehicle is executing a partial automated driving program and the navigation-assisted driving system is not activated, obtain data corresponding to the current location and preset condition dimensions; call a recognition model to obtain a virtual destination based on the current location and the data; generate a virtual planned driving route based on the current location and the virtual destination, and control the vehicle's driving using the virtual planned driving route.

[0092] The powertrain 1202 may include components that provide power for the vehicle's motion. For example, the powertrain 1202 may include an engine, an energy source, a transmission, wheels, tires, etc. The engine may be an internal combustion engine, an electric motor, an air-compressed engine, or other combinations of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air-compressed engine. The engine converts the energy source into mechanical energy to supply the transmission. Examples of energy sources may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other electrical sources. The energy source may also provide energy to other systems in the vehicle. Furthermore, the transmission may include a gearbox, a differential, a drive shaft, and a clutch, etc.

[0093] Sensor system 1203 may include sensors for sensing the vehicle's surrounding environment. Examples include a positioning system (which may be a Global Positioning System (GPS), BeiDou Navigation Satellite System, or other positioning systems), radar, a laser rangefinder, an inertial measurement unit (IMU), and a camera. The positioning system can be used to determine the vehicle's geographical location. The IMU is used to sense changes in the vehicle's position and orientation based on inertial acceleration. In one embodiment, the IMU may be a combination of an accelerometer and a gyroscope. Radar can use radio signals to sense objects in the vehicle's surrounding environment. In some embodiments, in addition to sensing objects, radar can also be used to sense the speed and / or direction of travel of objects.

[0094] To monitor environmental information and objects located in front of, behind, or to the sides of the vehicle, radar, cameras, and other devices can be configured at appropriate locations on the exterior of the vehicle. For example, to acquire an image of the front of the vehicle, a camera can be configured inside the vehicle and close to the windshield. Alternatively, the camera can be configured around the front bumper or radiator grille. Similarly, to acquire an image of the rear of the vehicle, a camera can be configured inside the vehicle and close to the rear window. Alternatively, the camera can be configured around the rear bumper, trunk, or tailgate. To acquire images of the sides of the vehicle, a camera can be configured inside the vehicle and close to at least one of the side windows. Alternatively, the camera can be configured around the side mirrors, fenders, or doors.

[0095] Laser rangefinders use lasers to sense objects in the environment in which a vehicle is located.

[0096] A camera can be used to capture multiple images of the vehicle's surroundings. The camera can be a still or video camera.

[0097] The control system 1204 may include software systems for implementing autonomous driving, such as a route planning system, an obstacle avoidance system, and a vision system for image analysis. The control system 1204 may also include hardware systems such as an accelerator and steering wheel system. Furthermore, the control system 1204 may add or replace components other than those shown and described. Alternatively, some of the components shown above may be omitted.

[0098] The control system 1204 interacts with external sensors, other autonomous driving devices, other computer systems, or users via peripheral devices 1205. Peripheral devices 1205 may include wireless communication systems, on-board computers, microphones, and / or speakers.

[0099] In some embodiments, peripheral device 1205 provides a means for user interaction with the control system 1204 via a user interface. For example, an onboard computer may provide information to a user of the vehicle. The user interface may also operate the onboard computer to receive user input. The onboard computer may be operated via a touchscreen. In other cases, peripheral device may provide a means for communicating with other devices located within the vehicle. For example, a microphone may receive audio (e.g., voice commands or other audio input) from a user of the control system 1204. Similarly, a speaker may output audio to a user of the control system 1204.

[0100] Wireless communication systems can communicate wirelessly with one or more devices, either directly or via a communication network. For example, wireless communication systems can use networks such as cellular networks, WiFi, and wireless local area networks (WLANs), or they can use infrared links, Bluetooth, or ZigBee to communicate directly with devices. Other wireless protocols include those used in various autonomous driving communication systems.

[0101] The power source 1206 can provide power to various components of the vehicle. The power source 1206 can be a rechargeable lithium-ion or lead-acid battery.

[0102] The computer system 1207 controls some or all of the functions enabling autonomous driving. The computer system 1207 may include at least one processor that executes instructions stored in a non-transitory computer-readable medium such as memory. The computer system 1207 provides the execution code for the aforementioned intelligent driving system to enable autonomous driving.

[0103] The processor can be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor can be a dedicated device such as an application-specific integrated circuit (ASIC) or other hardware-based processor. Those skilled in the art will understand that the processor, computer, or memory can actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing. For example, memory can be a hard disk drive or other storage media located in a housing different from that of a computer. Therefore, references to processors or computers will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Unlike using a single processor to perform the steps described herein, some components, such as steering and deceleration components, may each have their own processor that performs calculations only related to the component's specific function.

[0104] User interface 1208 is used to provide information to or receive information from users of the vehicle. Optionally, user interface 1208 may include one or more input / output devices within a set of peripheral devices 1205, such as wireless communication systems, on-board computers, microphones, and speakers.

[0105] It should be understood that the components described above are merely an example. In actual applications, components in the various modules or systems mentioned above may be added or removed as needed. Figure 12 This should not be construed as a limitation on the embodiments of this application.

[0106] The following is for reference. Figure 13 It shows a schematic diagram of the structure of a computer system 1300 suitable for implementing embodiments of the present invention. Figure 13 The computer system shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0107] like Figure 13 As shown, the computer system 1300 includes a central processing unit (CPU) 1301, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1302 or programs loaded from storage portion 1308 into random access memory (RAM) 1303. The RAM 1303 also stores various programs and data required for the operation of the system 1300. The CPU 1301, ROM 1302, and RAM 1303 are interconnected via a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0108] The following components are connected to I / O interface 1305: an input section 1306; an output section 1307 including devices such as cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; a storage section 1308 including devices such as hard disks; and a communication section 1309 including network interface cards such as LAN cards and modems. The communication section 1309 performs communication processing via a network such as the Internet. A drive 1310 is also connected to I / O interface 1305 as needed. Removable media 1311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 1310 as needed so that computer programs read from them can be installed into storage section 1308 as needed.

[0109] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1309, and / or installed from removable medium 1311. When the computer program is executed by central processing unit (CPU) 1301, it performs the functions defined above in the system of this invention.

[0110] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0111] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0112] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor can be described as including an acquisition unit, a processing unit, and a control unit. The names of these units do not necessarily limit the module itself; for example, the processing unit can also be described as a "module for determining a virtual destination."

[0113] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to: determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system is not activated; acquire data corresponding to the current location and preset condition dimensions; invoke a recognition model to obtain a virtual destination based on the current location and the data; generate a virtual planned driving route using the current location and the virtual destination; and control the vehicle's movement using the virtual planned driving route.

[0114] According to the technical solution of the present invention, the driving takeover rate can be reduced, and the user driving experience can be significantly improved.

[0115] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for assisted driving control planning, characterized in that, include: Determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system is not activated, and obtain data corresponding to the current location and preset condition dimensions; The recognition model is invoked to obtain the virtual destination based on the current location and the data; A virtual driving route is generated based on the current location and the virtual destination, and the vehicle is controlled using the virtual driving route.

2. The assisted driving control planning method according to claim 1, characterized in that, Also includes: In response to driving intervention commands, it identifies intervention signals to control vehicle movement; Reacquire the data corresponding to the current location and preset condition dimensions, and call the recognition model to determine the new virtual destination; By re-acquiring the current location and the new virtual destination, a new virtual driving route is generated, and the vehicle is controlled to drive.

3. The assisted driving control planning method according to claim 1, characterized in that, Also includes: In response to a driving takeover command, the system stops acquiring data corresponding to the current location and preset condition dimensions, or stops obtaining a virtual destination based on the current location and the data, or stops generating a virtual planned driving route based on the current location and the virtual destination, or stops controlling vehicle movement using the virtual planned driving route.

4. The assisted driving control planning method according to claim 1, characterized in that, include: The system acquires historical driving data of vehicles and trains a pre-defined recognition model using the dimensions of origin, destination, and preset conditions.

5. The assisted driving control planning method according to claim 4, characterized in that, The preset condition dimension includes condition attributes and corresponding condition attribute values; and The historical driving data stores a mapping relationship: origin, destination, and at least one condition dimension.

6. The assisted driving control planning method according to claim 4, characterized in that, Also includes: Monitor the incremental changes in the historical driving data of the vehicle; In response to the incremental situation meeting a preset quantity threshold, the current historical driving data is obtained, and a preset recognition model is trained using the origin, destination, and preset condition dimensions.

7. The assisted driving control planning method according to claim 4, characterized in that, Also includes: Count the number of driving intervention commands; In response to the number of times exceeding a preset threshold, the current historical driving data is obtained, and a preset recognition model is trained using the origin, destination, and preset condition dimensions.

8. The assisted driving control planning method according to claim 1, characterized in that, After calling the recognition model, it also includes: Based on the current location and the data, at least two virtual destinations are obtained, and the at least two virtual destinations are output. In response to the selection of a virtual destination, a virtual planned driving route is generated using the current location and the selected virtual destination.

9. The assisted driving control planning method according to any one of claims 1-8, characterized in that, After obtaining the data corresponding to the preset condition dimensions, it also includes: If the data corresponding to the preset condition dimension is determined to be empty, another preset condition dimension in the same cluster as the preset condition dimension is retrieved from the preset condition dimension pool, and then the data corresponding to the other preset condition dimension is obtained.

10. A driver assistance control planning device, characterized in that, include: The acquisition unit, processing unit, and control unit, wherein, The acquisition unit is used to determine that the vehicle is executing a partial autonomous driving program and the navigation-assisted driving system has not been activated, and to acquire data corresponding to the current location and preset condition dimensions; The processing unit is used to invoke the recognition model to obtain the virtual destination based on the current location and the data; The control unit is used to generate a virtual planned driving route based on the current location and the virtual destination, and to control the vehicle's movement using the virtual planned driving route.

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