Auxiliary driving switching method and device, equipment and storage medium

Abstract

The invention discloses an auxiliary driving switching method and device, equipment and a storage medium, and belongs to the technical field of vehicle auxiliary driving. The method is executed by a vehicle and comprises the steps that a memory navigation mode is started, and the vehicle is driven to run according to pre-stored path data; collecting the current state parameter of the vehicle, and judging whether the current state parameter meets the starting condition of the high-precision navigation mode or not; and under the condition that the starting condition of the high-precision navigation mode is met, the memory navigation mode is switched to the high-precision navigation mode, and the vehicle is driven to run according to pre-stored high-precision map data. According to the technical scheme, when the vehicle meets related conditions, the memory piloting mode can be switched to the high-precision piloting mode in time, and compared with the memory piloting mode, the vehicle can fully utilize map data and vehicle driving parameters in the high-precision piloting mode, and a higher-precision and safer auxiliary driving function is provided.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicle assisted driving, and particularly to an assisted driving switching method, device, equipment and storage medium. Background Art

[0002] With the continuous development of the vehicle field, assisted driving technology has been gradually applied to more and more vehicle models. The assisted driving modes include two types. One is the memory pilot mode based on route memory learning and personalized driving habit adaptation, and the other is the high-precision pilot mode based on high-precision maps and multi-sensor fusion.

[0003] In the related art, a vehicle is configured with both memory pilot and high-precision pilot assisted driving modes, and both assisted driving modes need to be manually selected by the driver.

[0004] It is difficult for the driver to accurately judge whether the current vehicle driving conditions meet the high-precision and safer high-precision pilot mode, which may lead to the vehicle not being able to switch to the high-precision pilot mode in time, and is not conducive to improving the driving safety of the vehicle. Summary of the Invention

[0005] Embodiments of the present disclosure provide an assisted driving switching method, device, equipment and storage medium, which can solve the above technical problems existing in the related art. The technical solutions are as follows:

[0006] On the one hand, an assisted driving switching method is provided. The method is executed by a vehicle, and the method includes:

[0007] Turn on the memory pilot mode and drive the vehicle to travel according to pre-stored path data;

[0008] Collect the current state parameters of the vehicle and judge whether the current state parameters meet the enabling conditions of the high-precision pilot mode;

[0009] When the enabling conditions of the high-precision pilot mode are met, switch from the memory pilot mode to the high-precision pilot mode and drive the vehicle to travel according to pre-stored high-precision map data.

[0010] In some possible implementation manners, before turning on the memory pilot mode, the method further includes:

[0011] When the vehicle is traveling on a target section, collect and store the path data of the target section.

[0012] In some possible implementation manners, the path data includes at least one of road markings, number of lanes, traffic signs, and signal light information.

[0013] In some possible implementation manners, collecting the current state parameters of the vehicle and determining whether the current state parameters meet the enabling conditions of the high-precision navigation mode includes:

[0014] Obtaining the longitude and latitude information of the vehicle and determining whether the vehicle is located within the target range of the high-precision map;

[0015] Obtaining the lane information of the vehicle and determining whether the vehicle is located on the target road type;

[0016] Calculating the current lane area confidence of the vehicle and determining whether the lane area confidence is greater than a preset confidence threshold.

[0017] In some possible implementation manners, calculating the current lane area confidence of the vehicle includes:

[0018] Based on the longitude and latitude information of the vehicle, calculating the positioning confidence of the vehicle;

[0019] Obtaining the lane line information of the current environment of the vehicle and calculating the lane position confidence of the vehicle based on the lane line information and the map lane line information in the high-precision map data;

[0020] Based on the lane line information and the map lane line information in the high-precision map data, calculating the map confidence of the high-precision map;

[0021] Based on the positioning confidence, the lane position confidence, and the map confidence, calculating the lane area confidence of the vehicle.

[0022] In some possible implementation manners, collecting the current state parameters of the vehicle and determining whether the current state parameters meet the enabling conditions of the high-precision navigation mode further includes:

[0023] Obtaining the driving curvature radius of the vehicle and determining whether the driving curvature radius is less than a preset curvature radius threshold.

[0024] On the other hand, an assisted driving switching device is provided, and the device includes:

[0025] An enabling module, configured to drive the vehicle to travel according to pre-stored path data when the memory navigation mode is enabled;

[0026] A determination module, configured to collect the current state parameters of the vehicle and determine whether the current state parameters meet the enabling conditions of the high-precision navigation mode;

[0027] A switching module, configured to switch from the memory navigation mode to the high-precision navigation mode when the enabling conditions of the high-precision navigation mode are met, and drive the vehicle to travel according to pre-stored high-precision map data.

[0028] On the other hand, an assisted driving switching device is provided. The assisted driving switching device includes a processor and a memory. At least one program is stored in the memory, and the processor is configured to execute the at least one program in the memory to implement the assisted driving switching method described in the embodiments of the present disclosure.

[0029] On the other hand, a computer-readable storage medium is provided. The computer-readable storage medium is used to store at least one computer program, and the at least one computer program is loaded and executed by a processor to implement the assisted driving switching method described in the embodiments of the present disclosure.

[0030] On the other hand, a computer program product is provided. The computer program product includes a computer program. The computer program is stored in a computer-readable storage medium, and a processor reads and executes the computer program from the computer-readable storage medium to implement the assisted driving switching method described in the embodiments of the present disclosure.

[0031] The beneficial effects brought by the technical solution provided by the present disclosure at least include:

[0032] The present disclosure provides an assisted driving switching method for a vehicle. When the vehicle is in a driving state of the memory pilot mode, it can determine whether the vehicle meets the opening conditions of the high-precision pilot mode according to the current state parameters of the vehicle, and timely switch the vehicle from the memory pilot mode to the high-precision pilot mode. Compared with the memory pilot mode, in the high-precision pilot mode, the vehicle can make full use of map data and vehicle driving parameters to provide a higher-precision and safer assisted driving function.

[0033] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 It is a schematic diagram of the implementation environment of an assisted driving switching method provided by an embodiment of the present disclosure.

[0036] Figure 2 It is a schematic flowchart of an assisted driving switching method provided by an embodiment of the present disclosure.

[0037] Figure 3 It is a schematic flowchart of an assisted driving switching method provided by an embodiment of the present disclosure.

[0038] Figure 4 It is a schematic diagram of an assisted driving switching device provided by an embodiment of the present disclosure.

[0039] Figure 5 It is a structural block diagram of an assisted driving switching device provided by an embodiment of the present disclosure.

[0040] Through the above-mentioned drawings, specific embodiments of the present disclosure have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present disclosure in any way, but to illustrate the concept of the present disclosure to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0041] To make the objectives, technical solutions, and advantages of the present disclosure clearer, the following will further describe the embodiments of the present application in detail in conjunction with the drawings.

[0042] Figure 1 It is a schematic diagram of the implementation environment of an assisted driving switching method provided by an embodiment of the present disclosure. Referring to Figure 1 as shown, the implementation environment includes a millimeter-wave radar 101, a camera 102, an inertial measurement unit (IMU) 103, a positioning system 104, and a controller 105.

[0043] In some embodiments, the millimeter-wave radar 101 is a millimeter-wave sensor for detecting distance information. The millimeter-wave radar 101 can emit millimeter waves. When the millimeter waves encounter an obstacle (such as a vehicle or a pedestrian in front), they will be reflected back. The millimeter-wave radar 101 receives the reflected millimeter waves and calculates the distance between the vehicle 100 and the obstacle based on the time difference between transmission and reception and the propagation speed of millimeter waves in the air.

[0044] The camera 102 is used to capture images of the driving environment around the vehicle 100. The inertial measurement unit 103 is a sensor device for measuring the acceleration and angular velocity of an object, usually composed of an accelerometer and a gyroscope (sometimes also including a magnetometer). The inertial measurement unit 103 is used for attitude perception, motion tracking, and positioning assistance of the vehicle 100. The positioning system 104 is used to measure the longitude and latitude information of the vehicle, such as: Global Positioning System (GPS), BeiDou Navigation Satellite System, Galileo Satellite Navigation System, etc.

[0045] The controller 105 can obtain the environmental information around the vehicle 100 collected by the millimeter-wave radar 101 and the camera 102, and the position information of the vehicle 100 collected by the inertial measurement unit 103 and the positioning system 104, and determine whether the current state parameters of the vehicle 100 meet the enabling conditions for the high-precision navigation mode.

[0046] In some embodiments, classified by the use of the vehicle 100, the vehicle 100 can be a sedan, a bus, a truck, a taxi, a sport utility vehicle (SUV), etc. Classified by the power source of the vehicle 100, the vehicle 100 can be a fuel vehicle, or a battery electric vehicle (BEV, or EV for short), or a hybrid electric vehicle (HEV for short), such as: range-extended, plug-in, etc.

[0047] Figure 2 It is a schematic flowchart of an assisted driving switching method provided by an embodiment of the present disclosure. This method is executed by an assisted driving switching device. Refer to Figure 2 As shown, the assisted driving switching method includes the following steps:

[0048] 201. Activate the memory navigation mode and drive the vehicle to travel according to the pre-stored path data.

[0049] Memory navigation assisted driving is an advanced assisted driving technology that combines local high-precision maps, real-time perception, memory learning, and intelligent decision-making. Its core is to enable the vehicle to learn and understand the routes (e.g., commuting roads) and driving habits that the driver often travels, so as to achieve more accurate and smooth assisted driving functions in specific scenarios (e.g., home-company commuting, home-fixed business district, etc.).

[0050] On familiar routes, the vehicle can complete operations such as lane keeping, acceleration and deceleration, and intersection turning according to the pre-stored path data, and adjust parameters such as following distance, acceleration / braking sensitivity, etc. according to the pre-stored user habits.

[0051] 202. Collect the current state parameters of the vehicle and determine whether the current state parameters meet the enabling conditions for the high-precision navigation mode.

[0052] The collected current state parameters of the vehicle include: lane line information of the vehicle collected by the millimeter-wave radar 101 and the camera 102, and geographical location information of the vehicle collected by the inertial measurement unit 103 and the positioning system 104. Determine whether the current state parameters of the vehicle meet the enabling conditions for the high-precision navigation mode, such as: whether the lane lines are clearly recognizable, and whether the vehicle is located in a section covered by the high-precision map.

[0053] 203. When the enabling conditions for the high-precision navigation mode are met, switch from the memory navigation mode to the high-precision navigation mode, and drive the vehicle to travel according to the pre-stored high-precision map data.

[0054] The high-precision navigation mode (also known as high-precision map navigation assisted driving or high-order intelligent driving) is an assisted driving technology based on high-precision maps (HD Maps) and multi-sensor fusion, which can achieve automatic navigation, lane-level path planning and precise control of vehicles on structured roads such as highways and urban expressways. For example: Highway NOA (Navigate on Autopilot), Urban NOA, etc.

[0055] The embodiments of the present disclosure provide an assisted driving switching method. When the vehicle is in the driving state of the memory navigation mode, it can judge whether the vehicle meets the enabling conditions for the high-precision navigation mode according to the current state parameters of the vehicle, and timely switch the vehicle from the memory navigation mode to the high-precision navigation mode. Compared with the memory navigation mode, in the high-precision navigation mode, the vehicle can make full use of map data and vehicle driving parameters to provide higher-precision and safer assisted driving functions.

[0056] Figure 3 is a schematic flowchart of an assisted driving switching method provided by the embodiments of the present disclosure. This method is executed by an assisted driving switching device. Referring to Figure 3 as shown, the assisted driving switching method includes the following steps:

[0057] 301. When the vehicle is driving on the target section, collect and store the path data of the target section.

[0058] The driver turns on the route learning function of the vehicle through the in-vehicle display screen or mobile phone application, and drives the vehicle to complete the route to be learned. During the driving process, static objects such as lane lines, walls, and columns are recorded by the camera, the environmental three-dimensional map is constructed by lidar / ultrasonic radar, and dynamic parameters such as steering wheel angle, vehicle speed, and acceleration are recorded by the inertial measurement unit and positioning system, and the path data of these target sections are collected and stored in the memory.

[0059] 302. Turn on the memory navigation mode and drive the vehicle to travel according to the pre-stored path data.

[0060] When the enabling conditions for the memory navigation mode are met, turn on the memory navigation mode. The enabling conditions for the memory navigation mode include: the feature matching degree between the current scene parameters and the pre-stored path data is greater than the preset threshold (for example: 90%), no new obstacles are detected in real time (for example: temporarily parked vehicles), and the driver manually confirms (for example: click the central control screen or trigger by the application).

[0061] In some embodiments, the path data includes at least one of road markings, number of lanes, traffic signs, and signal light information.

[0062] Road markings are the basis for vehicle positioning and lane keeping. Road markings include lane line types (solid / dashed lines, colors, widths), ground arrows (straight, turning, U-turn markings), speed bumps, zebra crossings, and other special markings.

[0063] The number of lanes is the lateral space basis for vehicle path planning. The number of lanes includes the total number of lanes on the current road and lane width changes (such as ramp narrowing).

[0064] Traffic signs are the basis for vehicle speed control and behavior decision-making. Traffic signs include static signs such as speed limit signs and no-overtaking signs, and variable message signs (such as electronic speed limit signs, road information LED screens).

[0065] Signal light information is used for vehicle behavior planning at intersections. Signal light information includes the position, height, phase timing (which needs to be combined with real-time detection), and special signal lights (such as bus-only lights) of traffic lights.

[0066] 303. Obtain the longitude and latitude information of the vehicle and determine whether the vehicle is within the target range of the high-precision map.

[0067] First, obtain the longitude and latitude information (longitude, latitude) of the vehicle through an inertial measurement unit and a positioning system.

[0068] Second, load the high-precision map data and obtain the set of boundary coordinates of the target area, which is usually a list of polygon vertex coordinates.

[0069] Then, uniformly convert the longitude and latitude information and the high-precision map coordinates to the same coordinate system.

[0070] Then, use the ray casting algorithm or the point-in-polygon algorithm to determine whether the vehicle coordinates are within the target polygon range.

[0071] Finally, output a boolean value (True / False) indicating whether the vehicle is within the target range. If the vehicle is within the target range, proceed to step 304. If the vehicle is within the target range, wait for the next judgment cycle and continue to execute step 303.

[0072] 304. Obtain the lane information of the vehicle and determine whether the vehicle is on the target road type.

[0073] The methods for obtaining the lane information of the vehicle include at least one of the following:

[0074] (1) Obtain the positioning data of the vehicle through the inertial measurement unit and the positioning system, and match it with the high-precision map data to further determine the current lane attributes.

[0075] (2) Determine the current lane attributes by using cameras, lidar, etc. to identify the lane line types and road features.

[0076] The target road type can be a highway or an urban expressway, and the high-precision navigation mode is Highway NOA; the target road type can also be an urban internal road, and the high-precision navigation mode is Urban NOA.

[0077] If the current lane attributes match the target road type, then proceed to step 305; if the current lane attributes do not match the target road type, then wait until the start of the next judgment cycle to execute step 303.

[0078] Through the preliminary judgments in steps 303 and 304, it can be judged whether the vehicle meets the preliminary opening conditions of the high-precision navigation mode. Since the judgment mechanisms of steps 303 and 304 are relatively simple, the processing burden on the processor is small, which is conducive to quickly making judgments on the current state parameters of the vehicle.

[0079] 305. Calculate the current lane area confidence of the vehicle, and judge whether the lane area confidence is greater than the preset confidence threshold.

[0080] The lane area confidence adopts a multi-level confidence verification scheme. If the lane area confidence is greater than the preset confidence threshold, then proceed to step 306; if the lane area confidence is less than or equal to the preset confidence threshold, then wait until the start of the next judgment cycle to execute step 303.

[0081] In some embodiments, calculating the current lane area confidence of the vehicle includes:

[0082] First, calculate the positioning confidence of the vehicle based on the longitude and latitude information of the vehicle.

[0083] Read the estimated longitude and latitude values and their standard deviations collected by the inertial measurement unit and the positioning system, and obtain the positioning confidence Conf through a confidence calculation method (for example: elliptical confidence region method, simple standard deviation weighting method). loc .

[0084] Then, obtain the lane line information of the current environment of the vehicle, and calculate the lane position confidence of the vehicle based on the lane line information and the map lane line information in the high-precision map data.

[0085] Obtain the current lane line data output by the sensor (for example: camera, millimeter wave radar) and the lane line data in the high-precision map (polynomial representation or discrete point sequence);

[0086] Perform geometric matching on each perceived lane line and the most similar corresponding lane line in the high-precision map;

[0087] Calculate the distance between the map lane line and the perceived lane line for all matched lane lines, and statistically obtain the average distance to get the matching degree of perception and positioning, and obtain the position confidence Conf of the current lane lane 。

[0088] Then, based on the lane line information and the map lane line information in the high-precision map data, calculate the map confidence of the high-precision map.

[0089] Perform position, quantity, line type, and shape verification on all matched lane lines and map lane lines to obtain the map confidence Conf map , where the position verification (Position Verification) includes the mean lateral offset (lane keeping), longitudinal continuity (lane line trend consistency), and special point matching (such as dashed line endpoints), the line type verification (Line Type Verification) includes solid / dashed line type matching, color consistency (e.g., yellow / white), and special markings (such as tidal lane lines), and the shape verification (Geometry Verification) includes curvature consistency, tangent angle deviation, and lane width stability.

[0090] Then, based on the positioning confidence, lane position confidence, and map confidence, calculate the lane area confidence of the vehicle.

[0091] Calculate the lane area confidence through the following weighted average formula:

[0092] Conf 车道 =w loc ·Conf loc +w lane ·Conf lane +w map ·Conf map

[0093] where w is the weight value, w loc +w lane +w map =1, and the specific values of each weight value can be dynamically adjusted according to parameters such as the environmental scenario.

[0094] Finally, compare the lane area confidence Conf 车道 with a preset confidence threshold (e.g., 0.95).

[0095] In some embodiments, collecting the current state parameters of the vehicle and determining whether the current state parameters meet the enabling conditions of the high-precision pilot mode further includes:

[0096] Obtain the driving curvature radius of the vehicle and determine whether the driving curvature radius is less than a preset curvature radius threshold.

[0097] The preset curvature radius threshold should be determined according to vehicle performance, road conditions, and safety requirements. If the driving curvature radius is less than the preset curvature radius threshold, continue to execute the subsequent steps. If the driving curvature radius is greater than or equal to the preset curvature radius threshold, wait until the start of the next judgment cycle to execute step 303.

[0098] 306. Under the condition that the opening condition of the high-precision pilot mode is satisfied, switch from the memory pilot mode to the high-precision pilot mode, and drive the vehicle to travel according to the pre-stored high-precision map data.

[0099] In the embodiment of the present disclosure, step 306 is the same as step 203 above and will not be elaborated here.

[0100] In some embodiments, after switching from the memory pilot mode to the high-precision pilot mode and driving the vehicle to travel according to the pre-stored high-precision map data under the condition that the opening condition of the high-precision pilot mode is satisfied, the method further includes:

[0101] Obtain the longitude and latitude information of the vehicle and determine whether the vehicle has driven out of the target range of the high-precision map. If the vehicle has not driven out of the target range of the high-precision map, the vehicle continues to maintain the high-precision pilot mode. If the vehicle has driven out of the target range of the high-precision map, the vehicle switches from the high-precision pilot mode to the memory pilot mode.

[0102] The memory pilot mode depends on the learning of the user's driving path and the environmental conditions during learning. Due to factors such as environmental occlusion and driver behavior during the memory process, the lane environment construction may be incomplete or inaccurate. The high-precision pilot mode depends on high-precision maps and has a complete lane environment expression and a high-precision lane model, which is more conducive to the vehicle in the assisted driving state to realize functions such as entering and leaving ramps, efficient lane changes, and passing through complex roads.

[0103] The embodiment of the present disclosure provides an assisted driving switching method. When the vehicle is in the driving state of the memory pilot mode, it can judge whether the vehicle meets the opening condition of the high-precision pilot mode according to the current state parameters of the vehicle, and timely switch the vehicle from the memory pilot mode to the high-precision pilot mode. Compared with the memory pilot mode, in the high-precision pilot mode, the vehicle can make full use of map data and vehicle driving parameters to provide higher-precision and safer assisted driving functions.

[0104] At the same time, the switching between the two assisted driving modes does not require manual operation by the driver, improving the switching efficiency and smoothness of the assisted driving mode, which is beneficial to the driver's driving experience.

[0105] In the high-precision map area, due to the integrity of the road structure, it is possible to enter and exit ramps better, improving the comfort of entering and exiting ramps; with the support of high-precision map data, the high-precision pilot mode can drive the vehicle to change lanes or routes in a timely manner, shortening the driver's commuting time.

[0106] Figure 4 It is a schematic diagram of an assisted driving switching device provided by an embodiment of the present disclosure. The device is used to execute the steps in the above-mentioned assisted driving switching method. Referring to Figure 4 As shown, the device includes:

[0107] An enabling module 401, configured to drive the vehicle to travel according to pre-stored path data when the memory pilot mode is enabled;

[0108] A judgment module 402, configured to collect the current state parameters of the vehicle and judge whether the current state parameters meet the enabling conditions of the high-precision pilot mode;

[0109] A switching module 403, configured to switch from the memory pilot mode to the high-precision pilot mode when the enabling conditions of the high-precision pilot mode are met, and drive the vehicle to travel according to pre-stored high-precision map data.

[0110] In some embodiments, before enabling the memory pilot mode, the enabling module 401 is configured to collect and store the path data of the target section when the vehicle is traveling on the target section.

[0111] In some embodiments, the path data includes at least one of road markings, number of lanes, traffic signs, and signal light information.

[0112] In some embodiments, the judgment module 402 is further configured to:

[0113] Obtain the longitude and latitude information of the vehicle and judge whether the vehicle is located within the target range of the high-precision map;

[0114] Obtain the lane information of the vehicle and judge whether the vehicle is located on the target road type;

[0115] Calculate the lane area confidence of the vehicle at present and judge whether the lane area confidence is greater than a preset confidence threshold.

[0116] In some embodiments, the judgment module 402 is further configured to:

[0117] Calculate the positioning confidence of the vehicle based on the longitude and latitude information of the vehicle;

[0118] Obtain the lane line information of the current environment of the vehicle, and calculate the lane position confidence of the vehicle based on the lane line information and the map lane line information in the high-precision map data;

[0119] Calculate the map confidence of the high-precision map based on the lane line information and the map lane line information in the high-precision map data;

[0120] Calculate the lane area confidence of the vehicle based on the positioning confidence, the lane position confidence, and the map confidence.

[0121] In some embodiments, the determination module 402 is further configured to:

[0122] Obtain the driving curvature radius of the vehicle, and determine whether the driving curvature radius is less than a preset curvature radius threshold.

[0123] The embodiments of the present disclosure provide an assisted driving switching device, which can determine whether the vehicle meets the opening conditions of the high-precision piloting mode according to the current state parameters of the vehicle and timely switch the vehicle from the memory piloting mode to the high-precision piloting mode when the vehicle is in the driving state of the memory piloting mode. Compared with the memory piloting mode, in the high-precision piloting mode, the vehicle can make full use of the map data and the vehicle driving parameters to provide a higher-precision and safer assisted driving function.

[0124] At the same time, the switching between the two assisted driving modes does not require manual operation by the driver, which improves the switching efficiency and smoothness of the assisted driving mode and is beneficial to the driving experience of the driver.

[0125] In the high-precision map area, due to the integrity of the road structure, it is possible to enter and exit the ramp better, improving the comfort of entering and exiting the ramp; with the support of the high-precision map data, the high-precision piloting mode can timely drive the vehicle to change lanes or change the driving route, shortening the driver's commuting time.

[0126] It can be understood that, in order to implement the above functions, the assisted driving switching device provided by the embodiments of the present disclosure includes the corresponding hardware structures and / or software modules for executing each function. Combining the units and algorithm steps of the various examples disclosed in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraint conditions of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present disclosure.

[0127] Figure 5 It is a structural block diagram of an assisted driving switching device provided by the embodiments of the present disclosure. The assisted driving switching device 500 can be any electronic device with data calculation, processing, and storage capabilities. The assisted driving switching device 500 can be used to implement the assisted driving switching method provided in the above embodiments.

[0128] The assisted driving switching device 500 includes: a processor 501 and a memory 502.

[0129] The processor 501 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor 501 may be implemented in at least one of the following hardware forms: one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and generic array logics (GALs).

[0130] The processor 801 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 801 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 801 may further include an artificial intelligence (AI) processor, and the AI processor is used to process computational operations related to machine learning.

[0131] The memory 502 may include one or more computer-readable storage media, and the computer-readable storage media may be non-transitory. The memory 502 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 502 is used to store a computer program, and the computer program is configured to be executed by one or more processors to implement the above-mentioned assisted driving switching method.

[0132] Those skilled in the art can understand,Figure 5 The structure shown does not constitute a limitation on the assisted driving switching device 500, and may include more or fewer components than shown, or combine certain components, or adopt different component arrangements.

[0133] The present disclosure also provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-mentioned assisted driving switching method. Optionally, the computer-readable storage medium may include: ROM (Read-Only Memory), RAM (Random Access Memory), SSD (Solid State Drives), or optical discs, etc. Among them, the random access memory may include ReRAM (Resistance Random Access Memory) and DRAM (Dynamic Random Access Memory).

[0134] The present disclosure also provides a computer program product including a computer program stored in a computer-readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program to enable the computer device to execute the above-mentioned assisted driving switching method.

[0135] In the description of this specification, the description with reference to the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of the present disclosure. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0136] It can be understood that in the present disclosure, "a plurality of" means two or more, and other quantifiers are similar thereto. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The singular forms of "a", "the", and "said" are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0137] It can be further understood that the terms "first", "second", etc. are used to describe various information, but this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other, and do not represent a specific order or degree of importance. In fact, the expressions such as "first" and "second" can be used interchangeably. For example, without departing from the scope of the present disclosure, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

[0138] It can be further understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "front", "rear", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present embodiment and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation.

[0139] It can be further understood that unless otherwise clearly specified and limited, the terms "installed", "connected", "coupled", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integrally formed one; it can be a mechanical connection, an electrical connection, or a connection that can communicate with each other; it can be a direct connection without other components between the two, or an indirect connection through an intermediate medium. It can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.

[0140] It can be further understood that although the operations are described in a specific order in the drawings in the embodiments of the present disclosure, it should not be understood as requiring these operations to be performed in the specific order or serial order shown, or requiring all the operations shown to obtain the desired result. In a specific environment, multitasking and parallel processing may be advantageous.

[0141] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed herein. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present disclosure are pointed out by the scope of the claims.

[0142] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. An assisted driving switching method, characterized in that, The method is executed by a vehicle, and the method includes: Turn on the memory pilot mode and drive the vehicle to travel according to pre-stored path data; Collect the current state parameters of the vehicle and determine whether the current state parameters meet the enabling conditions of the high-precision pilot mode; When the enabling conditions of the high-precision pilot mode are met, switch from the memory pilot mode to the high-precision pilot mode and drive the vehicle to travel according to pre-stored high-precision map data.

2. The method according to claim 1, characterized in that, Before turning on the memory pilot mode, the method further includes: When the vehicle is traveling on a target section, collect and store the path data of the target section.

3. The method according to claim 1 or 2, characterized in that, The path data includes at least one of road markings, the number of lanes, traffic signs, and signal light information.

4. The method according to claim 1, wherein The collecting the current state parameters of the vehicle and determining whether the current state parameters meet the enabling conditions of the high-precision pilot mode includes: Obtain the longitude and latitude information of the vehicle and determine whether the vehicle is within the target range of the high-precision map; Obtain the lane information of the vehicle and determine whether the vehicle is on the target road type; Calculate the current lane area confidence of the vehicle and determine whether the lane area confidence is greater than a preset confidence threshold.

5. The method according to claim 4, characterized in that, The calculating the current lane area confidence of the vehicle includes: Based on the longitude and latitude information of the vehicle, calculate the positioning confidence of the vehicle; Obtain the lane line information of the current environment of the vehicle, and based on the lane line information and the map lane line information in the high-precision map data, calculate the lane position confidence of the vehicle; Based on the lane line information and the map lane line information in the high-precision map data, calculate the map confidence of the high-precision map; Based on the positioning confidence, the lane position confidence, and the map confidence, calculate the lane area confidence of the vehicle.

6. The method according to claim 4, wherein The collecting the current state parameters of the vehicle and determining whether the current state parameters meet the enabling conditions of the high-precision pilot mode further includes: Obtain the driving curvature radius of the vehicle and determine whether the driving curvature radius is less than a preset curvature radius threshold.

7. An assisted driving switching device, characterized in that, The device includes: An enabling module, configured to turn on the memory pilot mode and drive the vehicle to travel according to pre-stored path data; A judgment module, configured to collect the current state parameters of the vehicle and determine whether the current state parameters meet the enabling conditions of the high-precision pilot mode; A switching module, configured to switch from the memory pilot mode to the high-precision pilot mode when the enabling conditions of the high-precision pilot mode are met, and drive the vehicle to travel according to pre-stored high-precision map data.

8. An assisted driving switching device, characterized in that, The assisted driving switching device includes a processor and a memory, and at least one program is stored in the memory. The processor is configured to execute the at least one program in the memory to implement the assisted driving switching method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by a processor to implement the assisted driving switching method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program, the computer program is stored in a computer-readable storage medium, and the processor reads and executes the computer program to implement the assisted driving switching method according to any one of claims 1 to 6.

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