An automatic driving planning and decision method and device

Abstract

The application provides an automatic driving planning decision method and device, which comprises the following steps: obtaining a self-vehicle trajectory point of a target vehicle in a preset time period based on a geodetic coordinate system; constructing a prediction line equation based on the self-vehicle trajectory point; obtaining a left and right lane line equation of a current driving lane of the target vehicle, a current self-vehicle speed and a current lane width in a self-vehicle coordinate system through a perception system of the target vehicle; determining a lane line confidence level according to the prediction line equation, the left and right lane line equation, the current self-vehicle speed and the current lane width; and determining a lane line following strategy according to the lane line confidence level. The method and device can determine a control strategy by comparing the self-lane left and right lane lines with the self-vehicle prediction trajectory, avoid problems such as a large steering wheel adjustment and a driving direction not meeting the expectation of a driver, and ensure that an automatic driving vehicle can still maintain a stable and safe driving state in a complex and changeable urban road environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to an automatic driving planning and decision method and device. BACKGROUND

[0002] Under the background of rapid development of automatic driving technology, vehicle path planning and tracking has become one of the core technologies to realize safe and efficient automatic driving. Traditional automatic driving systems often take the lane centerline as the reference path for accurate following driving based on the premise that the lane lines on both sides of the main lane are clear and regular. However, in practice, it is found that when the automatic driving vehicle enters the complex traffic environment of urban roads, there are often irregular lane lines such as lane branching and confluence. In this case, following the lane centerline is not ideal, and problems such as steering wheel hitting and driving direction not meeting the driver's expectation may occur, which not only affects the passenger's riding experience, but also may pose a potential threat to driving safety. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide an automatic driving planning and decision method and device, which can determine the control strategy by comparing the left and right lane lines of the self-lane with the self-vehicle predicted trajectory, avoid problems such as large steering wheel adjustment and driving direction not meeting the driver's expectation, and ensure that the automatic driving vehicle can still maintain a stable and safe driving state in the complex and variable urban road environment.

[0004] The first aspect of the present application provides an automatic driving planning and decision method, comprising:

[0005] obtaining a self-vehicle trajectory point of a target vehicle in a preset time period based on a geodetic coordinate system;

[0006] constructing a prediction line equation based on the self-vehicle trajectory point;

[0007] obtaining the left and right lane line equations of the current driving lane of the target vehicle, the current self-vehicle speed and the current lane width of the target vehicle in the self-vehicle coordinate system through the perception system of the target vehicle;

[0008] determining a lane line confidence level according to the prediction line equation, the left and right lane line equations, the current self-vehicle speed and the current lane width;

[0009] determining a lane line following strategy according to the lane line confidence level.

[0010] In the above implementation process, the method can determine the control strategy by comparing the left and right lane lines of the self-lane with the self-vehicle predicted trajectory, avoid problems such as large steering wheel adjustment and driving direction not meeting the driver's expectation, and ensure that the automatic driving vehicle can still maintain a stable and safe driving state in the complex and variable urban road environment.

[0011] Further, the constructing a prediction line equation based on the ego vehicle trajectory points comprises:

[0012] constructing an ego vehicle coordinate system based on the target vehicle;

[0013] performing coordinate system conversion on the ego vehicle trajectory points to obtain vehicle trajectory points in the ego vehicle coordinate system;

[0014] performing cubic polynomial fitting based on the vehicle trajectory points to obtain a prediction line equation.

[0015] Further, after the constructing a prediction line equation based on the ego vehicle trajectory points, the method further comprises:

[0016] determining whether the LCC centering function of the target vehicle is in an activated state within the preset time period;

[0017] if yes, determining that the ego vehicle trajectory points are valid trajectory points, and determining that the prediction line equation can be used to predict the future driving trajectory of the ego vehicle, and performing the obtaining, by the perception system of the target vehicle, of the left and right lane line equations of the current driving lane of the target vehicle in the ego vehicle coordinate system, the current ego vehicle speed, and the current lane width.

[0018] Further, the determining a lane line confidence level according to the prediction line equation, the left and right lane line equations, the current ego vehicle speed, and the current lane width comprises:

[0019] calculating a current preview distance based on a preset preview time and the current ego vehicle speed;

[0020] calculating a distance threshold value according to the current lane width and a preset offset amount;

[0021] calculating a coordinate point proportion that a transverse distance between a prediction line and a lane line is less than the distance threshold value according to the left and right lane line equations, the prediction line equation, the current preview distance, and the distance threshold value;

[0022] determining a lane line confidence level according to the coordinate point proportion and a preset proportion threshold value.

[0023] Further, the determining a lane line following strategy according to the lane line confidence level comprises:

[0024] when the lane line confidence level only includes a left lane line confidence level, determining that the lane line following strategy is a left lane line following strategy;

[0025] when the lane line confidence level only includes a right lane line confidence level, determining that the lane line following strategy is a right lane line following strategy.

[0026] when the lane line confidence level comprises a left lane line confidence level and a right lane line confidence level, determining whether the left lane line confidence level and the right lane line confidence level are equal in level;

[0027] if yes, determining a lane line following strategy as a double lane following strategy.

[0028] Further, the method further comprises:

[0029] when the left lane line confidence level and the right lane line confidence level are not equal in level, and the left lane line confidence level is greater than the right lane line confidence level, determining a lane line following strategy as a left lane line following strategy;

[0030] when the left lane line confidence level and the right lane line confidence level are not equal in level, and the left lane line confidence level is not greater than the right lane line confidence level, determining a lane line following strategy as a right lane line following strategy.

[0031] Further, after the lane line following strategy is determined according to the lane line confidence level, the method further comprises:

[0032] calculating a planned target path equation based on the lane line following strategy and the left and right lane line equations;

[0033] controlling the target vehicle to perform automatic driving based on the planned target path.

[0034] The second aspect of the present application provides an automatic driving planning and decision device, which comprises:

[0035] a first acquisition unit configured to acquire, based on a geodetic coordinate system, a self-vehicle trajectory point of a target vehicle within a preset time period;

[0036] a construction unit configured to construct a prediction line equation based on the self-vehicle trajectory point;

[0037] a second acquisition unit configured to acquire, through a perception system of the target vehicle, a left and right lane line equation of a current driving lane of the target vehicle, a current self-vehicle speed, and a current lane width in the self-vehicle coordinate system;

[0038] a confidence determination unit configured to determine a lane line confidence level according to the prediction line equation, the left and right lane line equation, the current self-vehicle speed, and the current lane width;

[0039] a strategy determination unit configured to determine a lane line following strategy according to the lane line confidence level.

[0040] Further, the construction unit comprises:

[0041] a construction sub-unit configured to construct a self-vehicle coordinate system based on the target vehicle;

[0042] a conversion sub-unit configured to perform coordinate system conversion on the self-vehicle trajectory point to obtain a vehicle trajectory point in the self-vehicle coordinate system;

[0043] a fitting sub-unit configured to perform a cubic polynomial fitting based on the vehicle trajectory point to obtain a prediction line equation.

[0044] Further, the automatic driving planning and decision device further comprises:

[0045] a judgment unit configured to judge whether the LCC centering function of the target vehicle is in an activated state within the preset time period after the construction unit constructs the prediction line equation based on the self-vehicle trajectory point;

[0046] an effectiveness determination unit configured to determine that the self-vehicle trajectory point is an effective trajectory and that the prediction line equation can be used to predict the future driving trajectory of the self-vehicle when the LCC centering function of the target vehicle is in the activated state within the preset time period, and trigger the second acquisition unit to perform the operation of acquiring, by the perception system of the target vehicle, the left and right lane line equations of the current driving lane of the target vehicle in the self-vehicle coordinate system, the current self-vehicle speed, and the current lane width.

[0047] Further, the confidence determination unit comprises:

[0048] a calculation sub-unit configured to calculate a current preview distance based on a preset preview time and the current self-vehicle speed;

[0049] The calculation sub-unit is further configured to calculate a distance threshold based on the current lane width and a preset bias amount.

[0050] The calculation sub-unit is further configured to calculate a coordinate point proportion that a lateral distance between the prediction line and the lane line is less than the distance threshold based on the left and right lane line equations, the prediction line equation, the current preview distance, and the distance threshold.

[0051] A first determination sub-unit is configured to determine a lane line confidence level based on the coordinate point proportion and a preset proportion threshold.

[0052] Further, the strategy determination unit comprises:

[0053] A second determination sub-unit is configured to determine that the lane line following strategy is a left lane line following strategy when the lane line confidence level only includes a left lane line confidence level.

[0054] The second determining subunit is further configured to determine the lane line following strategy as a right lane line following strategy when the lane line confidence levels only include a right lane line confidence level.

[0055] The determining subunit is configured to determine whether the left lane line confidence level and the right lane line confidence level are equal in level when the lane line confidence levels include a left lane line confidence level and a right lane line confidence level.

[0056] The second determining subunit is further configured to determine the lane line following strategy as a double-line following strategy when the left lane line confidence level and the right lane line confidence level are equal in level.

[0057] Further, the second determining subunit is further configured to determine the lane line following strategy as a left lane line following strategy when the left lane line confidence level and the right lane line confidence level are not equal in level, and the left lane line confidence level is greater than the right lane line confidence level.

[0058] The second determining subunit is further configured to determine the lane line following strategy as a right lane line following strategy when the left lane line confidence level and the right lane line confidence level are not equal in level, and the left lane line confidence level is not greater than the right lane line confidence level.

[0059] Further, the automatic driving planning and decision device further includes:

[0060] The calculating unit is configured to calculate a planning target path equation based on the lane line following strategy and the left and right lane line equations after the strategy determining unit determines the lane line following strategy according to the lane line confidence levels.

[0061] The control unit is configured to control the target vehicle to perform automatic driving based on the planning target path.

[0062] The third aspect of the present application provides an electronic device including a memory and a processor, the memory is configured to store a computer program, and the processor is configured to run the computer program to enable the electronic device to perform the automatic driving planning and decision method according to any one of the first aspect of the present application.

[0063] The fourth aspect of the present application provides a computer readable storage medium storing computer program instructions, the computer program instructions are read and run by a processor to perform the automatic driving planning and decision method according to any one of the first aspect of the present application.

[0064] The beneficial effects of the present application are that the method and device can combine the self-vehicle predicted trajectory information and lane line information, make lane selection decisions when the vehicle is performing L2 level self-lane automatic driving, thereby effectively dealing with lane line irregularities such as lane bifurcation and confluence, and further improving the stability of the function and reducing the number of driver takeovers. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments of the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0066] Figure 1 A flowchart of an automatic driving planning and decision method provided by an embodiment of the present application;

[0067] Figure 2 A flowchart of another automatic driving planning and decision method provided by an embodiment of the present application;

[0068] Figure 3 A structural diagram of an automatic driving planning and decision device provided by an embodiment of the present application;

[0069] Figure 4 A structural diagram of another automatic driving planning and decision device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present application will be described below in combination with the drawings in the embodiments of the present application.

[0071] It should be noted that similar reference numerals and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0072] Embodiment 1

[0073] Please refer to Figure 1 , Figure 1 A flowchart of an automatic driving planning and decision method provided by the present embodiment. The automatic driving planning and decision method comprises:

[0074] S101, obtaining a self-vehicle trajectory point of a target vehicle in a preset time period based on a geodetic coordinate system.

[0075] S102: Construct a prediction line equation based on the vehicle trajectory points.

[0076] S103. Obtain, through the perception system of the target vehicle, the left and right lane line equations of the target vehicle's current lane, the current speed of the target vehicle, and the current lane width in the target vehicle's own vehicle coordinate system.

[0077] S104: Determine a lane line confidence level based on the predicted line equation, the left and right lane line equations, the current vehicle speed, and the current lane width.

[0078] S105: Determine a lane line following strategy based on the lane line confidence level.

[0079] In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, and this is not limited in this embodiment.

[0080] In this embodiment, the execution subject of the method may also be a smart device such as a smart phone, a tablet computer, etc., which is not limited in this embodiment.

[0081] It can be seen that the implementation of the autonomous driving planning and decision-making method described in this embodiment can determine the control strategy by comparing the left and right lane lines of the self-driving lane with the predicted trajectory of the self-vehicle, avoiding problems such as large steering wheel adjustments and driving directions that do not meet the driver's expectations, thereby ensuring that the autonomous driving vehicle can still maintain a stable and safe driving state in the complex and changeable urban road environment.

[0082] Example 2

[0083] Please see Figure 2 , Figure 2 This is a flow chart of an autonomous driving planning and decision-making method provided in this embodiment. The autonomous driving planning and decision-making method includes:

[0084] S201: Acquire vehicle trajectory points of a target vehicle within a preset time period based on a geodetic coordinate system.

[0085] In this embodiment, the method can obtain the position information of the vehicle in the geodetic coordinate system (horizontal coordinate x, vertical coordinate y) through the upstream positioning module, and continuously memorize the vehicle trajectory points within T time, where T is a preset constant value.

[0086] S202: Construct a vehicle coordinate system based on the target vehicle.

[0087] S203: Perform coordinate system conversion on the vehicle trajectory points to obtain the vehicle trajectory points in the vehicle coordinate system.

[0088] In this embodiment, the method can convert the set of vehicle trajectory points memorized within time T to the current vehicle coordinate system using the following coordinate conversion formula:

[0089] x' = (x - xh)cosAg + (y - yh)sinAg

[0090] y' = (y - yh)cosAg - (x - xh)sinAg

[0091] wherein x, y are the memorized trajectory points in the geodetic coordinate, x', y' are the converted trajectory points in the vehicle coordinate system, xh, yh, Ag are the current vehicle position and heading angle in the geodetic coordinate.

[0092] S204, performing a cubic polynomial fitting based on the vehicle trajectory points to obtain a prediction line equation.

[0093] In this embodiment, the method can utilize the converted memorized trajectory point set to perform a cubic polynomial fitting to obtain the polynomial coefficients P0, P1, P2, P3 of the prediction line. The prediction line polynomial coefficients P0, P1, P2, P3 can be used to predict the future driving trajectory of the ego vehicle.

[0094] In this embodiment, the method can predict the future driving trajectory of the ego vehicle based on the historical driving trajectory of the ego vehicle.

[0095] S205, determining whether the LCC centering function of the target vehicle is in an activated state within a preset time period, if yes, performing step S206; if no, ending the process.

[0096] In this embodiment, if the LCC centering function of the vehicle is in an activated state within the past T time, the memorized trajectory is considered valid.

[0097] S206, determining that the ego vehicle trajectory point is a valid trajectory, and determining that the prediction line equation can be used to predict the future driving trajectory of the ego vehicle, and performing step S207.

[0098] S207, obtaining the left and right lane line equations of the target vehicle currently driving lane, the current vehicle speed, and the current lane width in the ego vehicle coordinate system through the perception system of the target vehicle.

[0099] In this embodiment, the method can obtain the left and right lane line equations of the current vehicle driving lane through the perception system of the vehicle. The lane line equation is a cubic equation, and there are four coefficients (for example, L0, L1, L2, L3 for the left lane). The lane line equation is based on the ego vehicle coordinate system.

[0100] S208, calculating the current preview distance based on the preset preview time and the current vehicle speed.

[0101] In this embodiment, the method can calculate the current preview distance L by multiplying the preset preview time Tl and the current vehicle speed V (m / s); at the same time, the minimum value of L is limited by the preset minimum preview distance Lmin to ensure a certain minimum preview distance at low speed.

[0102] S209, calculate the distance threshold according to the current lane width and the preset offset.

[0103] S210, calculate the proportion of coordinate points with a lateral distance between the predicted line and the lane line less than the distance threshold according to the left and right lane line equations, the predicted line equation, the current preview distance, and the distance threshold.

[0104] In this embodiment, the method can take half of the current lane width plus a preset offset (such as 0.2m) as the threshold W, take 1m as the step, substitute the distances from 1 to L into the left and right lane line equations and the predicted line equation, and calculate the proportion of points with a lateral distance between the predicted line and the lane line less than W.

[0105] For example, when calculating the proportion of the predicted line and the left lane line, it is assumed that 1m is taken as the step, and there are M points from 1 to L distance, of which N points satisfy:

[0106] |(P3*x^3+P2*x^2+P1*x+P0)-(L3*x^3+L2*x^2+L1*x+L0)|<W

[0107] At this time, x is the longitudinal distance corresponding to the N points;

[0108] The proportion of the current left lane line is PL=N / M.

[0109] In this embodiment, the calculation method of the right lane line proportion is the same as that of the left lane line proportion. The higher the proportion coefficient, the higher the similarity between the corresponding lane line and the predicted trajectory.

[0110] S211, determine the lane line confidence level according to the coordinate point proportion and the preset proportion threshold.

[0111] In this embodiment, the method can determine the confidence level of the lane line according to the interval composed of the preset threshold:

[0112] If the lane line proportion coefficient is greater than ThrHigh, the confidence level of the corresponding lane line is high;

[0113] If the lane line proportion coefficient is greater than ThrMid and less than or equal to ThrHigh, the confidence level of the corresponding lane line is medium;

[0114] If the lane line proportion coefficient is less than or equal to ThrMid, the confidence level of the corresponding lane line is low.

[0115] S212, determining a lane line following strategy according to the lane line confidence level.

[0116] As an optional implementation, determining a lane line following strategy according to the lane line confidence level comprises:

[0117] When the lane line confidence level only includes the left lane line confidence level, the lane line following strategy is determined as the left lane line following strategy;

[0118] When the lane line confidence level only includes the right lane line confidence level, the lane line following strategy is determined as the right lane line following strategy;

[0119] When the lane line confidence level includes the left lane line confidence level and the right lane line confidence level, it is determined whether the left lane line confidence level and the right lane line confidence level are equal in level;

[0120] If yes, the lane line following strategy is determined as the double line following strategy.

[0121] As a further optional implementation, the method further comprises:

[0122] When the left lane line confidence level and the right lane line confidence level are not equal in level, and the left lane line confidence level is greater than the right lane line confidence level, the lane line following strategy is determined as the left lane line following strategy;

[0123] When the left lane line confidence level and the right lane line confidence level are not equal in level, and the left lane line confidence level is not greater than the right lane line confidence level, the lane line following strategy is determined as the right lane line following strategy.

[0124] In this embodiment, the method can compare the confidence levels of the left and right lane lines to select a following strategy, wherein the strategy only takes effect when the lane lines on both sides of the vehicle lane are valid. For example, if only one side of the lane line is recognized by perception, the vehicle follows the recognized lane line on one side.

[0125] For example, when both sides of the lane line are recognized by perception:

[0126] If the confidence levels of the left and right lane lines are equal, the vehicle performs double line following, and the target path is planned as a double line equation with each coefficient averaged:

[0127] y = ((L3+R3) / 2)*x^3+((L2+R2) / 2)*x^2+((L1+R1) / 2)*x+((L0+R0) / 2).

[0128] If the confidence levels of the left and right lane lines are not equal, the vehicle follows the lane line with higher confidence. Taking the left lane line confidence as an example:

[0129] y = L3 * x^3 + L2 * x^2 + L1 * x + ((L0 + R0) / 2).

[0130] S213, based on the lane line following strategy and the left and right lane line equations, calculating a planning target path equation.

[0131] S214, based on the planning target path, controlling the target vehicle for automatic driving.

[0132] In this embodiment, the execution subject of the method can be a computer, a server, or other computing devices, which are not limited in this embodiment.

[0133] In this embodiment, the execution subject of the method can also be a smart phone, a tablet computer, or other smart devices, which are not limited in this embodiment.

[0134] It can be seen that by implementing the automatic driving planning and decision method described in this embodiment, the control strategy can be determined by comparing the left and right lane lines of the self-lane with the predicted trajectory of the self-vehicle, avoiding problems such as large steering wheel adjustment and driving direction not meeting the driver's expectations, and ensuring that the automatic driving vehicle can still maintain a stable and safe driving state in a complex and variable urban road environment.

[0135] Embodiment 3

[0136] Please refer to Figure 3 , Figure 3 A structural schematic diagram of an automatic driving planning and decision device provided in this embodiment. As Figure 3 shown, the automatic driving planning and decision device comprises:

[0137] The first acquisition unit 310 is configured to acquire a self-vehicle trajectory point of a target vehicle in a preset time period based on a geodetic coordinate system.

[0138] The construction unit 320 is configured to construct a prediction line equation based on the self-vehicle trajectory point.

[0139] The second acquisition unit 330 is configured to acquire, through a perception system of the target vehicle, a left and right lane line equation of a current driving lane of the target vehicle, a current self-vehicle speed, and a current lane width in a self-vehicle coordinate system.

[0140] The confidence determination unit 340 is configured to determine a lane line confidence level according to the prediction line equation, the left and right lane line equation, the current self-vehicle speed, and the current lane width.

[0141] The strategy determination unit 350 is configured to determine a lane line following strategy according to the lane line confidence level.

[0142] In this embodiment, the explanation of the autonomous driving planning and decision-making device can refer to the description in Example 1 or Example 2, and will not be further elaborated in this embodiment.

[0143] It can be seen that the implementation of the autonomous driving planning and decision-making device described in this embodiment can determine the control strategy by comparing the left and right lane lines of the self-lane with the predicted trajectory of the self-vehicle, avoiding problems such as large steering wheel adjustments and driving directions that do not meet the driver's expectations, thereby ensuring that the autonomous driving vehicle can still maintain a stable and safe driving state in the complex and changeable urban road environment.

[0144] Example 4

[0145] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of an autonomous driving planning and decision-making device provided in this embodiment. Figure 4 As shown, the autonomous driving planning and decision-making device includes:

[0146] The first acquisition unit 310 is configured to acquire the target vehicle's trajectory points within a preset time period based on the earth coordinate system;

[0147] A construction unit 320 is used to construct a prediction line equation based on the vehicle trajectory points;

[0148] The second acquisition unit 330 is configured to acquire, through the perception system of the target vehicle, the left and right lane line equations of the target vehicle's current lane, the current speed of the target vehicle, and the current lane width in the target vehicle's own vehicle coordinate system;

[0149] A confidence determination unit 340 is configured to determine a lane line confidence level based on the predicted line equation, the left and right lane line equations, the current vehicle speed, and the current lane width;

[0150] The strategy determination unit 350 is configured to determine a lane following strategy based on the lane confidence level.

[0151] As an optional implementation, the construction unit 320 includes:

[0152] A construction subunit 321 is used to construct a vehicle coordinate system based on the target vehicle;

[0153] The conversion subunit 322 is used to perform coordinate system conversion on the ego vehicle trajectory point to obtain the vehicle trajectory point in the ego vehicle coordinate system;

[0154] The fitting subunit 323 is used to perform cubic polynomial fitting based on the vehicle trajectory points to obtain the prediction line equation.

[0155] As an optional implementation, the autonomous driving planning and decision-making device further includes:

[0156] a determination unit 360 for determining whether the LCC centering function of the target vehicle is activated within a preset time period after the construction unit 320 constructs the prediction line equation based on the ego vehicle trajectory points;

[0157] The validity determination unit 370 is used to determine that the ego vehicle trajectory point is a valid trajectory when the LCC centering function of the target vehicle is activated within a preset time period, and to determine that the predicted line equation can be used to predict the future driving trajectory of the ego vehicle, and to trigger the second acquisition unit 330 to execute the operation of obtaining the left and right lane line equations of the target vehicle's current driving lane, the current ego vehicle speed, and the current lane width in the ego vehicle coordinate system through the target vehicle's perception system.

[0158] As an optional implementation, the confidence determination unit 340 includes:

[0159] The calculation subunit 341 is used to calculate the current preview distance based on the preset preview time and the current vehicle speed;

[0160] The calculation subunit 341 is further configured to calculate a distance threshold based on the current lane width and a preset offset;

[0161] The calculation subunit 341 is further configured to calculate the percentage of coordinate points where the lateral distance between the predicted line and the lane line is less than the distance threshold based on the left and right lane line equations, the predicted line equation, the current preview distance, and the distance threshold;

[0162] The first determining subunit 342 is configured to determine a lane line confidence level based on a coordinate point ratio and a preset ratio threshold.

[0163] As an optional implementation, the policy determination unit 350 includes:

[0164] The second determining subunit 351 is configured to determine that the lane line following strategy is a left lane line following strategy when the lane line confidence level includes only the left lane line confidence level;

[0165] The second determining subunit 351 is further configured to determine that the lane line following strategy is the right lane line following strategy when the lane line confidence level only includes the right lane line confidence level;

[0166] a judgment subunit 352 for judging whether the left lane line confidence level and the right lane line confidence level are equal when the lane line confidence level includes the left lane line confidence level and the right lane line confidence level;

[0167] The second determining subunit 351 is further configured to determine that the lane line following strategy is a dual-line following strategy when the left lane line confidence level and the right lane line confidence level are equal.

[0168] As an optional implementation, the second determining sub-unit 351 is further configured to determine the lane line following strategy as the left lane line following strategy when the level of the left lane line confidence is not equal to the level of the right lane line confidence, and the level of the left lane line confidence is greater than the level of the right lane line confidence.

[0169] The second determining sub-unit 351 is further configured to determine the lane line following strategy as the right lane line following strategy when the level of the left lane line confidence is not equal to the level of the right lane line confidence, and the level of the left lane line confidence is not greater than the level of the right lane line confidence.

[0170] As an optional implementation, the automatic driving planning and decision device further comprises:

[0171] The computing unit 380 is configured to, after the strategy determining unit 350 determines the lane line following strategy according to the lane line confidence levels, calculate a planning target path equation based on the lane line following strategy and the left and right lane line equations.

[0172] The control unit 390 is configured to control the target vehicle to perform automatic driving based on the planning target path.

[0173] In this embodiment, the description of the automatic driving planning and decision device can refer to the description in Embodiment 1 or Embodiment 2, and the description will not be repeated here.

[0174] It can be seen that, by implementing the automatic driving planning and decision device described in this embodiment, the control strategy can be determined by comparing the left and right lane lines of the self-lane with the predicted trajectory of the self-vehicle, so as to avoid problems such as large steering wheel adjustment and driving direction not meeting the expectations of the driver, and ensure that the automatic driving vehicle can still maintain a stable and safe driving state in a complex and variable urban road environment.

[0175] The electronic device provided in the embodiment of the present application comprises a memory and a processor, the memory is configured to store a computer program, and the processor is configured to run the computer program to enable the electronic device to perform the automatic driving planning and decision method in Embodiment 1 or Embodiment 2 of the present application.

[0176] The computer readable storage medium provided in the embodiment of the present application stores computer program instructions, and the computer program instructions are read and run by a processor to perform the automatic driving planning and decision method in Embodiment 1 or Embodiment 2 of the present application.

[0177] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can also be implemented by other means. The apparatus embodiments described above are only illustrative, for example, the flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the apparatus, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logic function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different order from that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0178] In addition, the functional modules in the embodiments of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0179] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0180] The above merely provides an example of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and thus, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0181] The above merely provides an example of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, and thus, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings.

[0182] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

Claims

1. An autonomous driving planning and decision-making method, characterized in that: include: Obtain the target vehicle's trajectory points within a preset time period based on the geodetic coordinate system; Constructing a prediction line equation based on the ego vehicle trajectory points; Obtaining, through the perception system of the target vehicle, the left and right lane line equations of the target vehicle's current lane, the current speed of the target vehicle, and the current lane width in the target vehicle's own vehicle coordinate system; Determining a lane line confidence level based on the predicted line equation, the left and right lane line equations, the current vehicle speed, and the current lane width; Determining a lane following strategy based on the lane confidence level; The determining of the lane line confidence level based on the predicted line equation, the left and right lane line equations, the current vehicle speed, and the current lane width includes: Calculating a current preview distance based on a preset preview time and the current vehicle speed; Calculating a distance threshold according to the current lane width and a preset offset; Calculate the proportion of coordinate points whose lateral distance between the predicted line and the lane line is less than the distance threshold based on the left and right lane line equations, the predicted line equation, the current preview distance, and the distance threshold; The lane line confidence level is determined based on the coordinate point ratio and a preset ratio threshold.

2. The autonomous driving planning and decision-making method according to claim 1, characterized in that: The constructing of a prediction line equation based on the vehicle trajectory points includes: Constructing a vehicle coordinate system based on the target vehicle; Performing coordinate system conversion on the vehicle trajectory points to obtain vehicle trajectory points in the vehicle coordinate system; A cubic polynomial fitting is performed based on the vehicle trajectory points to obtain a prediction line equation.

3. The autonomous driving planning and decision-making method according to claim 1, characterized in that: After constructing the prediction line equation based on the ego vehicle trajectory points, the method further includes: Determining whether the LCC centering function of the target vehicle is in an activated state within the preset time period; If so, the vehicle trajectory point is determined to be a valid trajectory, and the predicted line equation is determined to be able to be used to predict the future driving trajectory of the vehicle, and the perception system of the target vehicle is executed to obtain the left and right lane line equations of the current lane of the target vehicle in the vehicle coordinate system, the current vehicle speed and the current lane width.

4. The autonomous driving planning and decision-making method according to claim 1, characterized in that: The determining of a lane line following strategy according to the lane line confidence level includes: When the lane line confidence level only includes the left lane line confidence level, determining that the lane line following strategy is the left lane line following strategy; When the lane line confidence level only includes the right lane line confidence level, determining that the lane line following strategy is the right lane line following strategy; When the lane line confidence level includes a left lane line confidence level and a right lane line confidence level, determining whether the left lane line confidence level and the right lane line confidence level are equal; If yes, the lane line following strategy is determined to be a double-line following strategy.

5. The autonomous driving planning and decision-making method according to claim 4, characterized in that: The method further comprises: When the left lane line confidence level and the right lane line confidence level are not equal, and the left lane line confidence level is greater than the right lane line confidence level, determining that the lane line following strategy is the left lane line following strategy; When the left lane line confidence level is not equal to the right lane line confidence level, and the left lane line confidence level is not greater than the right lane line confidence level, the lane line following strategy is determined to be the right lane line following strategy.

6. The autonomous driving planning and decision-making method according to claim 1, characterized in that: After determining the lane line following strategy according to the lane line confidence level, the method further includes: Calculating a planned target path equation based on the lane line following strategy and the left and right lane line equations; The target vehicle is controlled to perform automatic driving based on the planned target path.

7. An autonomous driving planning and decision-making device, characterized in that: The autonomous driving planning and decision-making device includes: A first acquisition unit is configured to acquire a target vehicle's trajectory points within a preset time period based on a geodetic coordinate system; A construction unit, configured to construct a prediction line equation based on the vehicle trajectory points; A second acquisition unit is configured to acquire, through the perception system of the target vehicle, left and right lane line equations of the current lane of the target vehicle, the current speed of the target vehicle, and the current lane width in the vehicle coordinate system; a confidence determination unit, configured to determine a lane line confidence level based on the predicted line equation, the left and right lane line equations, the current vehicle speed, and the current lane width; a strategy determination unit, configured to determine a lane following strategy based on the lane confidence level; Wherein, the confidence determination unit includes: A calculation subunit, configured to calculate a current preview distance based on a preset preview time and the current vehicle speed; The calculation subunit is further configured to calculate a distance threshold based on the current lane width and a preset offset; The calculation subunit is further configured to calculate, based on the left and right lane line equations, the predicted line equation, the current preview distance, and the distance threshold, a proportion of coordinate points whose lateral distance between the predicted line and the lane line is less than the distance threshold; The first determination subunit is used to determine the lane line confidence level according to the coordinate point ratio and a preset ratio threshold.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the autonomous driving planning and decision-making method according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that The readable storage medium stores computer program instructions, and when the computer program instructions are read and executed by a processor, the automatic driving planning and decision-making method according to any one of claims 1 to 6 is executed.

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