Curve adaptive cruise control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a curve adaptive cruise control method and device, electronic equipment and a storage medium. The method comprises the steps that radar detection data, visual detection data and inertia detection data are acquired; determining relative information between the vehicle and each candidate vehicle in front according to the radar detection data and the visual detection data; determining lane line data according to the detection data of the inertial sensor and the visual detection data; according to the lane line data and the relative information, a target vehicle is determined from the multiple candidate vehicles, and the target vehicle is a candidate vehicle which is located in the lane range of the vehicle and is closest to the vehicle; according to the relative information between the vehicle and the target vehicle and the speed of the vehicle, the safe vehicle following distance is determined, and tracking control is carried out according to the safe vehicle following distance. According to the embodiment of the invention, the vehicle in the lane range of the vehicle is selected as the vehicle following target in the curve driving scene, so that accidents such as sideslip and collision of the vehicle are avoided.

Description

Technical Field

[0001] The present application belongs to the technical field of adaptive cruise control, and in particular, relates to a curve adaptive cruise control method, device, electronic device and computer-readable storage medium. Background Art

[0002] Adaptive Cruise Control (ACC) can automatically adjust the vehicle speed based on the distance and speed between the vehicle and the vehicle in front while maintaining the set speed, so as to maintain a safe distance from the vehicle in front.

[0003] At present, the ACC system selects the vehicle in front of the vehicle closest to the vehicle as the target to follow based on the distance between the vehicle and each vehicle in front; when it detects that the target vehicle is decelerating, the vehicle speed is automatically reduced.

[0004] In the case of driving on a curve, the vehicle in front of the adjacent lane may be closest to the vehicle. In this case, if the vehicle in front of the vehicle closest to the vehicle is selected as the target to follow, the vehicle in front of the adjacent lane will be selected as the target to follow. In order to maintain the following performance, the ACC system will enter the curve at a speed similar to that of the vehicle in front of the adjacent lane, which will cause the vehicle's road adhesion to reach its limit, leading to accidents such as rollover or skidding. In addition, if there is a vehicle in front of the lane where the vehicle is located, it may also cause the vehicle to collide with the vehicle in front of the lane. In other words, selecting the vehicle in front of the adjacent lane as the target to follow is prone to safety accidents such as skidding and collision. Summary of the invention

[0005] The embodiments of the present application provide a curve adaptive cruise control method, device, electronic device and computer-readable storage medium, which can solve the problem that in existing curve driving scenarios, the front vehicle in the adjacent lane is selected as the following target, which is prone to safety accidents such as skidding and collision.

[0006] In a first aspect, an embodiment of the present application provides a curve adaptive cruise control method, comprising:

[0007] Acquiring radar detection data collected by a radar, visual detection data collected by a camera, and inertial detection data collected by an inertial measurement unit, wherein the radar, the camera, and the inertial measurement unit are arranged on the vehicle;

[0008] Determine the relative information between the vehicle and each candidate vehicle ahead based on radar detection data and visual detection data;

[0009] Determine lane line data based on inertial detection data and visual detection data;

[0010] According to the lane line data and relative information, a target vehicle is determined from multiple candidate vehicles. The target vehicle is a candidate vehicle that is located within the lane range of the vehicle and is closest to the vehicle.

[0011] Determine the safe following distance based on the relative information between the vehicle and the target vehicle and the vehicle's speed;

[0012] Tracking control is performed based on the safe following distance.

[0013] As can be seen from the above, after the embodiment of the present application determines the lane line data and relative information through radar detection data, visual detection data and inertial detection data, it selects the candidate vehicle that is located within the lane range of the vehicle and is closest to the vehicle from multiple candidate vehicles in front as the following target (i.e., the target vehicle) based on the lane line data and relative information. Finally, based on the relevant information between the vehicle and the following target, it determines the safe following distance, thereby realizing the following control of the target vehicle by the vehicle. In this way, in the curved driving scenario, the vehicle in the adjacent lane will not be mistakenly selected as the following target, but the vehicle within the lane range of the vehicle will be selected as the following target, avoiding dangerous accidents such as skidding and collision, thereby improving the safety of vehicle driving.

[0014] In some possible implementations of the first aspect, determining lane line data according to inertial detection data and visual detection data includes:

[0015] According to the visual detection data, the current lane line data and lane line quality are obtained;

[0016] According to the inertial detection data, the lane line data of the vehicle trajectory detected and calculated by the inertial sensor is obtained;

[0017] Determine a first weight coefficient of the current lane line data and a second weight coefficient of the vehicle trajectory lane line data according to the lane line quality;

[0018] According to the first weight coefficient and the second weight coefficient, the current lane line data and the lane line data of the vehicle trajectory are fused to obtain the lane line data.

[0019] In this implementation, the quality of lane line detection can be improved by fusing the inertial detection results and the visual detection results. Even when the lane line is not clear, a high-quality lane line detection result can still be obtained, further improving the accuracy of subsequent vehicle following target selection.

[0020] In some possible implementations of the first aspect, determining a safe following distance according to relative information between the host vehicle and the target vehicle and the speed of the host vehicle includes:

[0021] The vehicle speed is multiplied by a preset headway to obtain a product, wherein the preset headway is determined based on the relative information between the vehicle and the target vehicle and the vehicle speed;

[0022] Add the multiplied product and the preset minimum safety distance to obtain the safe following distance.

[0023] In this way, the safe following distance can be calculated dynamically, which improves the operating conditions of ACC in curved driving scenarios and improves the comfort and safety of vehicle driving.

[0024] In some possible implementations of the first aspect, determining a target vehicle from a plurality of candidate vehicles according to lane line data and relative information includes:

[0025] Determine the radius of the curve according to the curvature of the curve on which the vehicle is traveling;

[0026] For each candidate vehicle, determine the relative lateral distance between the vehicle and the candidate vehicle according to the radius of the curve and the relative radial distance between the vehicle and the candidate vehicle;

[0027] Determine the candidate vehicles within the lane range of the vehicle according to the relative lateral distances and lane widths of the candidate vehicles;

[0028] The candidate vehicle closest to the vehicle among the candidate vehicles within the lane of the vehicle is determined as the target vehicle;

[0029] Among them, the relative information includes the relative radial distance, and the lane line information includes the curvature and lane width.

[0030] In some possible implementations of the first aspect, determining the candidate vehicles located within the lane range of the host vehicle according to the relative lateral distances and lane widths of the candidate vehicles includes:

[0031] If the relative lateral distance of the candidate vehicle falls within the first preset interval, it is determined that the candidate vehicle is within the lane range of the host vehicle;

[0032] If the relative lateral distance of the candidate vehicle falls within the second preset interval or the third preset interval, it is determined that the candidate vehicle is located within the adjacent lane range;

[0033] The first preset interval, the second preset interval and the third preset interval are set according to the lane width.

[0034] In some possible implementations of the first aspect, determining the relative lateral distance between the host vehicle and the candidate vehicle according to the radius of the curve and the relative radial distance between the host vehicle and the candidate vehicle includes:

[0035] According to the radius of the curve and the relative radial distance between the vehicle and the candidate vehicle, the center angle of the road corresponding to the position point of the candidate vehicle is determined;

[0036] According to the radius of the curve and the center angle of the road, the lateral distance between the vehicle and the center position point is obtained;

[0037] The relative lateral distance between the vehicle and the candidate vehicle is determined based on the lateral distance, the road center angle and the relative radial distance.

[0038] In a second aspect, an embodiment of the present application provides a curve adaptive cruise control device, comprising:

[0039] An acquisition module, used to acquire radar detection data collected by the radar, visual detection data collected by the camera, and inertial detection data collected by the inertial measurement unit, wherein the radar, the camera, and the inertial measurement unit are arranged on the vehicle;

[0040] A relative information determination module is used to determine the relative information between the vehicle and each candidate vehicle ahead based on radar detection data and visual detection data;

[0041] A lane data determination module, used to determine lane line data based on inertial detection data and visual detection data;

[0042] The vehicle following target screening module is used to determine the target vehicle from multiple candidate vehicles based on the lane line data and relative information. The target vehicle is the candidate vehicle that is located within the lane range of the vehicle and is closest to the vehicle.

[0043] A determination module, used to determine a safe following distance based on the relative information between the vehicle and the target vehicle and the speed of the vehicle;

[0044] The following vehicle control module is used to perform tracking control based on the safe following distance.

[0045] In some possible implementations of the second aspect, the lane data determination module is specifically configured to:

[0046] According to the visual detection data, the current lane line data and lane line quality are obtained;

[0047] According to the inertial detection data, the lane line data of the vehicle trajectory detected and calculated by the inertial sensor is obtained;

[0048] According to the lane line quality, determine a first weight coefficient of the current lane line data and a second weight coefficient of the vehicle trajectory lane line data calculated by inertial detection;

[0049] According to the first weight coefficient and the second weight coefficient, the current lane line data and the lane line data of the vehicle trajectory are fused to obtain the lane line data.

[0050] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any method of the first aspect when executing the computer program.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method of any one of the above-mentioned first aspects is implemented.

[0052] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes any one of the methods described in the first aspect.

[0053] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A schematic diagram of a vehicle driving on a curve provided in an embodiment of the present application;

[0056] Figure 2 A schematic flow chart of a curve adaptive cruise control method provided in an embodiment of the present application;

[0057] Figure 3 A schematic diagram of a process flow of a target vehicle selection process provided in an embodiment of the present application;

[0058] Figure 4 A schematic diagram of a curved obstacle recognition solution provided in an embodiment of the present application;

[0059] Figure 5 A schematic block diagram of the structure of a curve adaptive cruise control device provided in an embodiment of the present application;

[0060] Figure 6 A schematic block diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0062] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0063] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0064] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0065] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0066] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0067] See also Figure 1A schematic diagram of a vehicle curve driving scenario provided by an embodiment of the present application is shown, and the scenario includes a vehicle, target 1, target 2, and target 3, where targets 1 to 3 are vehicles located in front of the vehicle.

[0068] The vehicle is traveling in the middle lane, and target 1 is also traveling in the middle lane. That is, target 1 is the vehicle ahead of the vehicle, and both are traveling in the same lane. Target 3 is traveling in the left lane, and target 2 is traveling in the right lane. That is, the lanes of targets 2 and 3 are different from the lane of the vehicle.

[0069] In the related art, the vehicle detects the relative distance and relative speed between the vehicle and target 1, target 2 and target 3 through sensors such as radar, and determines that target 3 is the closest to the vehicle, so target 3 is selected as the following target. After selecting target 3 as the following target, the vehicle enters the curve at a speed close to that of target 3.

[0070] At this time, target 3 is the vehicle ahead in the adjacent lane, that is, target 3 is in a different lane from the vehicle. That is, the vehicle in the adjacent lane is selected as the target to follow. In this case, when the vehicle enters the curve at a speed similar to that of target 3, the road adhesion of the vehicle may reach its limit, and then an instability accident such as rollover and skidding may occur. In addition, after selecting target 3 as the target to follow, the vehicle will automatically adjust its own speed according to the relative distance and relative speed between the vehicle and target 3 to maintain a safe distance from target 3. However, although the vehicle can maintain a safe distance from target 3 through ACC control, it may cause the vehicle to catch up with target 1 in the same lane, which may lead to a collision accident between the vehicle and target 1 in the same lane.

[0071] That is to say, in the scenario of driving on a curve, the relevant technology is likely to select the vehicle in front of the adjacent lane as the target to follow, which makes the vehicle prone to accidents such as rollover and collision, affecting the safety of vehicle driving.

[0072] In response to the above-mentioned related issues, the embodiment of the present application provides a curve adaptive cruise control solution, which selects the vehicle in front of the vehicle that is within the lane range of the vehicle and closest to the vehicle as the target to follow in the curve driving scenario. In this way, when the vehicle is tracked by ACC control, there will be no accidents such as skidding and collision, which improves the safety of vehicle driving.

[0073] See also Figure 2 , is a schematic flow chart of a curve adaptive cruise control method provided in an embodiment of the present application, and the method may include the following steps:

[0074] Step S201: acquiring radar detection data collected by a radar, visual detection data collected by a camera, and inertial detection data collected by an inertial measurement unit, wherein the radar, the camera, and the inertial measurement unit are arranged on the vehicle.

[0075] Exemplarily, the radar is specifically a millimeter wave radar, which is installed in front of the vehicle. The camera is installed in front of the vehicle. The millimeter wave radar and the camera can collect data on the area in front of the vehicle to obtain radar detection data and visual detection data. The inertial measurement unit (IMU) is installed inside the vehicle. The IMU can continuously detect inertial detection data such as the vehicle's posture and the angular velocity of each axis.

[0076] Radar detection data may include information such as the relative distance and relative speed between the vehicle and each candidate vehicle in front. Visual detection data may include information such as the relative distance and relative speed between the vehicle and each candidate vehicle in front, and may also include lane line data. Lane line data may include but is not limited to curvature, the rate of change of curvature with distance, and deviation information between the boundary and the vehicle. Lane line data may be converted into corresponding lane centerline data.

[0077] Step S202: Determine the relative information between the vehicle and each candidate vehicle ahead based on the radar detection data and the visual detection data.

[0078] The relative information may include information such as relative distance and relative speed. In some embodiments, the radar detection data and the visual detection data may be fused to obtain more accurate relative information. For example, after the radar detection data and the visual detection data are processed separately, they are converted to the same coordinate system for data synchronization. For the synchronized radar detection data and the visual detection data, a cluster analysis method is used to fuse the radar detection data and the visual detection data into different categories for output. The category may refer to a candidate vehicle.

[0079] After the radar detection data and the visual detection data are integrated to identify the candidate vehicles in front of the vehicle and the status information (such as distance and speed, etc.) of each candidate vehicle, multiple target tracking can be performed on each candidate vehicle to obtain the trajectory and real-time status information of each candidate vehicle. For example, after determining the trajectory of each candidate vehicle, the Kalman filter algorithm is used to perform state estimation and filtering on the trajectory, and the new detection point is associated with the existing trajectory to achieve multi-target tracking.

[0080] S203: Determine lane line data according to the inertial detection data and the visual detection data.

[0081] It should be noted that lane line data can be obtained through visual detection data, and lane line data can also be obtained through inertial detection data. In some embodiments, the lane line data corresponding to the two detection data can be fused to obtain more accurate lane line data.

[0082] Exemplarily, according to the visual detection data, the current lane line data and the lane line quality can be obtained. Among them, the current lane line data may include curvature and the rate of change of curvature with distance. The lane line quality is used to characterize the quality of visual data. When the lane line quality is high, it indicates that the quality of the visual detection data is high, the confidence of the lane line data obtained through the visual detection data is high, and the weight coefficient assigned during fusion is correspondingly high; conversely, when the lane line quality is low, it indicates that the quality of the visual detection data is low, the confidence of the lane line data obtained through the visual detection data is low, and the weight coefficient assigned during fusion is correspondingly low.

[0083] Based on the inertial detection data, the lane line data of the vehicle trajectory calculated by inertial detection can also be obtained. Specifically, the inertial detection data includes data such as angular velocity and vehicle posture, through which the vehicle's own trajectory can be estimated. The lane line data of the vehicle trajectory can include relevant lane line data such as curvature, the rate of change of curvature with distance, and the deviation information between the boundary and the vehicle. For example, the current curve curvature C can be estimated by the vehicle yaw angular velocity w and the vehicle speed v detected by the IMU. Among them,

[0084] After determining the current lane line data and the vehicle trajectory lane line data, the respective fusion weight coefficients can be determined. In specific applications, the first weight coefficient of the current lane line data and the second weight coefficient of the vehicle trajectory lane line data can be determined according to the lane line quality. The sum of the first weight coefficient and the second weight coefficient is equal to 1. According to the lane line quality, the first weight coefficient of the current lane line data can be determined, and the second weight coefficient is equal to 1 minus the first weight coefficient.

[0085] For example, according to the quality of the lane line, the confidence of the visual detection data is very high, and the first weight coefficient of the current lane line data is 1 accordingly, while the second weight coefficient is 0 accordingly.

[0086] According to the first weight coefficient and the second weight coefficient, the current lane line data and the vehicle trajectory lane line data can be fused to obtain fused lane line data.

[0087] It is worth pointing out that the fused lane line data is more accurate. In addition, the embodiments of the present application can also obtain accurate lane line data when the lane line is not clear. Specifically, when the lane line is clear, the confidence of the current lane line data obtained through visual detection data is higher, so the first weight coefficient is usually higher and the second weight coefficient is lower. When the lane line is not clear, such as in a night environment or the lane line itself is not clear, the confidence of the current lane line data obtained through visual detection data is lower, so the first weight coefficient is usually lower and the second weight coefficient is higher, and accurate lane line data can still be obtained after fusion. Among them, when the lane line is not clear, the lane line quality is low.

[0088] In the related art, lane line data is usually obtained by using visual detection data. However, when the lane line is not clear, the lane line data cannot be obtained or the obtained lane line data is inaccurate. However, by fusing the current lane line data and the lane line data of the vehicle trajectory, the embodiment of the present application can still obtain accurate lane line data when the lane line is not clear, further improving the accuracy of subsequent following vehicle target selection.

[0089] Step S204: determining a target vehicle from multiple candidate vehicles based on the lane line data and the relative information. The target vehicle is a candidate vehicle that is within the lane range of the vehicle and is closest to the vehicle.

[0090] For example, in Figure 1 In the curved driving scenario shown, through the solution provided by the embodiment of the present application, target 1 will be selected as the following target (i.e., the target vehicle). Target 1 is in the same lane as the vehicle. In this way, during the ACC control process, there will be no collision between the vehicle and target 1, and the vehicle will not have a rollover accident due to the adhesion reaching the limit.

[0091] Step S205: Determine a safe following distance based on the relative information between the vehicle and the target vehicle and the speed of the vehicle.

[0092] In some embodiments, the vehicle speed and the preset headway time can be multiplied to obtain the product, where the preset headway time is determined based on the relative information between the vehicle and the target vehicle and the vehicle speed; the product and the preset minimum safety distance are then added to obtain the safe following distance.

[0093] For example, by the formula Δd des =t h v+Δx0, calculate the safe following distance. Among them, Δd des is the safe following distance. v is the speed of the vehicle, t h is the preset headway distance. Δx0 is the preset minimum safety distance. h= is equal to the relative distance between the vehicle and the target vehicle / vehicle speed v. The relative information between the vehicle and the target vehicle includes the relative distance between the vehicle and the target vehicle. h , for example, t h It can be 1s, 1.9s or 2.5s, etc.

[0094] It is worth pointing out that the embodiment of the present application can dynamically calculate the safe following distance, improve the use conditions of ACC in the curve driving scenario, and improve the comfort and safety of vehicle driving.

[0095] Step S206: Perform tracking control according to the safe following distance.

[0096] In specific applications, the vehicle can track and control itself according to the safe following distance through the control module to achieve the following of the target vehicle. For example, according to the speed and relative distance of the target vehicle, the vehicle's own speed is adjusted to maintain a safe distance from the target vehicle.

[0097] After the lane line data and relative information are determined by radar detection data, visual detection data and inertial detection data, the embodiment of the present application selects the candidate vehicle that is located within the lane range of the vehicle and is closest to the vehicle from multiple candidate vehicles in front as the following target (i.e., the target vehicle) based on the lane line data and relative information. Finally, based on the relevant information between the vehicle and the following target, the safe following distance is determined, thereby realizing the following control of the target vehicle by the vehicle. In this way, in the curved driving scenario, the vehicle in the adjacent lane will not be mistakenly selected as the following target, but the vehicle within the lane range of the vehicle will be selected as the following target, thereby avoiding dangerous accidents such as skidding and collision, and improving the safety of vehicle driving.

[0098] Based on the above embodiments, see Figure 3 A flow chart of a target vehicle selection process provided by an embodiment of the present application is shown. The process of determining a target vehicle from a plurality of candidate vehicles based on lane line data and relative information may specifically include the following steps:

[0099] Step S301: Determine the radius of the curve according to the curvature of the curve on which the vehicle is traveling.

[0100] For example, The radius R of the curve on which the vehicle is traveling can be determined by the curvature of the curve.

[0101] Step S302: for each candidate vehicle, determine the relative lateral distance between the host vehicle and the candidate vehicle according to the radius of the curve and the relative radial distance between the host vehicle and the candidate vehicle.

[0102] For example, see Figure 4FIG. 1 is a schematic diagram of a curved obstacle recognition scheme provided by an embodiment of the present application, wherein SV represents the vehicle, TV represents the vehicle in front (i.e., the candidate vehicle), v represents the speed of the vehicle, PQ represents the predicted relative lateral distance between the vehicle and the vehicle in front, denoted as D, and the predicted lateral distance is D r ; is the road center angle corresponding to the candidate vehicle position point P, and L is the longitudinal horizontal distance from the rear axle of the vehicle to the radar installation point.

[0103] In some embodiments, the center angle of the road corresponding to the position point of the candidate vehicle can be determined based on the radius of the curve and the relative radial distance between the host vehicle and the candidate vehicle.

[0104] Specifically, the road center angle Among them, K is the correction coefficient, which corresponds to different values ​​at different vehicle speeds, and ρ is the relative radial distance between the vehicle and the candidate vehicle.

[0105] Then, according to the radius of the curve and the center angle of the road, the lateral distance between the vehicle and the center position point Q is obtained. Finally, according to the lateral distance, the center angle of the road and the relative radial distance, the relative lateral distance between the vehicle and the candidate vehicle is determined.

[0106] For example, the lateral distance Relative lateral distance

[0107] Step S303: Determine the candidate vehicles within the lane range of the vehicle according to the relative lateral distances and lane widths of the candidate vehicles.

[0108] In some embodiments, if the relative lateral distance of the candidate vehicle falls within a first preset interval, it is determined that the candidate vehicle is within the lane range of the vehicle; if the relative lateral distance of the candidate vehicle falls within a second preset interval or a third preset interval, it is determined that the candidate vehicle is within the adjacent lane range; the first preset interval, the second preset interval and the third preset interval are set according to the lane width W.

[0109] Exemplarily, the first preset interval is The second preset interval is The third preset interval is

[0110] by Figure 1 Taking the curved driving scenario shown as an example, if the relative lateral distance of target 1 falls within the first preset interval, it is determined that target 1 is the vehicle ahead within the lane range of the vehicle; if the relative lateral distance of target 2 falls within the second preset interval, it is determined that target 2 is the vehicle ahead traveling in the adjacent right lane; if the relative lateral distance of target 3 falls within the third preset interval, it is determined that target 3 is the vehicle ahead traveling in the adjacent left lane.

[0111] After determining the lanes where target 1, target 2, and target 3 are located, the vehicle that is within the lane of the vehicle and is closest to the vehicle can be selected as the target vehicle based on the detected relative distance and other information. At this time, the vehicle that is within the lane of the vehicle and is closest to the vehicle is target 1, that is, target 1 is selected as the following target.

[0112] Step S304: determine the candidate vehicle closest to the vehicle among the candidate vehicles within the lane range of the vehicle as the target vehicle; wherein the relative information includes the relative radial distance, and the lane line information includes the curvature and lane width.

[0113] Through the curve obstacle recognition solution provided in the embodiment of the present application, the lanes of the vehicles in front of the vehicle can be accurately identified, and then the vehicle that is within the lane range of the vehicle and the closest to it can be accurately selected as the following target.

[0114] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0115] Corresponding to the curve adaptive cruise control method described in the above embodiment, Figure 5 A schematic block diagram of the structure of a curve adaptive cruise control device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0116] Reference Figure 5 , the device comprises:

[0117] An acquisition module 51 is used to acquire radar detection data collected by a radar, visual detection data collected by a camera, and inertial detection data collected by an inertial measurement unit, wherein the radar, the camera, and the inertial measurement unit are arranged on the vehicle;

[0118] A relative information determination module 52 is used to determine the relative information between the vehicle and each candidate vehicle ahead according to the radar detection data and the visual detection data;

[0119] A lane data determination module 53, used to determine lane line data according to inertial detection data and visual detection data;

[0120] The vehicle following target screening module 54 is used to determine a target vehicle from multiple candidate vehicles based on the lane line data and relative information. The target vehicle is a candidate vehicle that is located within the lane range of the vehicle and is closest to the vehicle.

[0121] A determination module 55, for determining a safe following distance based on the relative information between the vehicle and the target vehicle and the speed of the vehicle;

[0122] The following vehicle control module 56 is used to perform tracking control according to the safe following vehicle distance.

[0123] In some possible implementations, the lane data determination module is specifically used to: obtain current lane line data and lane line quality based on visual detection data; obtain self-vehicle trajectory lane line data detected and calculated by inertial sensors based on inertial detection data; determine a first weight coefficient of current lane line data and a second weight coefficient of self-vehicle trajectory lane line data based on lane line quality; and fuse the current lane line data with the self-vehicle trajectory lane line data based on the first weight coefficient and the second weight coefficient to obtain lane line data.

[0124] In some possible implementations, the determination module is specifically used to: multiply the vehicle speed and the preset headway to obtain the product, where the preset headway is determined based on the relative information between the vehicle and the target vehicle and the vehicle speed; add the product and the preset minimum safety distance to obtain the safe following distance.

[0125] In some possible implementations, the vehicle following target screening module is specifically used to: determine the radius of the curve according to the curvature of the curve on which the vehicle is traveling; for each candidate vehicle, determine the relative lateral distance between the vehicle and the candidate vehicle according to the radius of the curve and the relative radial distance between the vehicle and the candidate vehicle; determine the candidate vehicles within the lane range of the vehicle according to the relative lateral distances and lane widths of the candidate vehicles; determine the candidate vehicle closest to the vehicle among the candidate vehicles within the lane range of the vehicle as the target vehicle; wherein the relative information includes the relative radial distance, and the lane line information includes the curvature and the lane width.

[0126] In some possible implementations, the vehicle following target screening module is specifically used to: if the relative lateral distance of the candidate vehicle falls within the first preset interval, determine that the candidate vehicle is located within the lane range of the vehicle; if the relative lateral distance of the candidate vehicle falls within the second preset interval or the third preset interval, determine that the candidate vehicle is located within the adjacent lane range; the first preset interval, the second preset interval and the third preset interval are set according to the lane width.

[0127] In some possible implementations, the vehicle following target screening module is specifically used to: determine the road center angle corresponding to the position point of the candidate vehicle based on the radius of the curve and the relative radial distance between the vehicle and the candidate vehicle; obtain the lateral distance between the vehicle and the center position point based on the radius of the curve and the road center angle; determine the relative lateral distance between the vehicle and the candidate vehicle based on the lateral distance, the road center angle and the relative radial distance.

[0128] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0129] Figure 6 This is a schematic block diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 6 Only one is shown in the figure), a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, wherein the processor 60 implements the steps of any of the above-mentioned method embodiments when executing the computer program 62.

[0130] The electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 It is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0131] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0132] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 6. Further, the memory 61 may also include both an internal storage unit of the electronic device 6 and an external storage device. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0133] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0134] In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and are not used to limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the above method embodiment, which will not be repeated here.

[0135] An embodiment of the present application also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0136] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0137] An embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0139] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] In the embodiments provided in the present application, it should be understood that the disclosed devices, electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0142] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A curve adaptive cruise control method, characterized in that: include: Acquiring radar detection data collected by a radar, visual detection data collected by a camera, and inertial detection data collected by an inertial measurement unit, wherein the radar, the camera, and the inertial measurement unit are arranged on the vehicle; Determining relative information between the host vehicle and each candidate vehicle ahead according to the radar detection data and the visual detection data; Determining lane line data according to the inertial detection data and the visual detection data; Determine a target vehicle from the plurality of candidate vehicles according to the lane line data and the relative information, wherein the target vehicle is a candidate vehicle that is located within the lane range of the host vehicle and is closest to the host vehicle; Determining a safe following distance based on the relative information between the host vehicle and the target vehicle and the host vehicle's speed; Tracking control is performed according to the safe following distance.

2. The method according to claim 1, characterized in that Determining lane line data according to the inertial detection data and the visual detection data includes: Obtaining current lane line data and lane line quality according to the visual detection data; According to the inertial detection data, obtaining the lane line data of the vehicle trajectory detected and solved by the inertial sensor; Determining a first weight coefficient of the current lane line data and a second weight coefficient of the vehicle trajectory lane line data according to the lane line quality; According to the first weight coefficient and the second weight coefficient, the current lane line data and the vehicle trajectory lane line data are fused to obtain the lane line data.

3. The method according to claim 1, characterized in that Determining a safe following distance according to the relative information between the host vehicle and the target vehicle and the host vehicle speed includes: Multiplying the vehicle speed and a preset headway to obtain a product, wherein the preset headway is determined based on relative information between the vehicle and the target vehicle and the vehicle speed; The product of the multiplication and the preset minimum safety distance are added to obtain the safe following distance.

4. The method according to any one of claims 1 to 3, characterized in that Determining a target vehicle from a plurality of candidate vehicles according to the lane line data and the relative information includes: Determining the radius of the curve according to the curvature of the curve on which the vehicle is traveling; For each of the candidate vehicles, determining a relative lateral distance between the host vehicle and the candidate vehicle according to the radius of the curve and the relative radial distance between the host vehicle and the candidate vehicle; Determine the candidate vehicles within the lane range of the vehicle according to the relative lateral distance and lane width of each candidate vehicle; Determine the candidate vehicle closest to the vehicle among the candidate vehicles within the lane range of the vehicle as the target vehicle; The relative information includes the relative radial distance, and the lane line information includes the curvature and the lane width.

5. The method according to claim 4, characterized in that Determining candidate vehicles located within the lane range of the vehicle according to the relative lateral distances and lane widths of the candidate vehicles includes: If the relative lateral distance of the candidate vehicle falls within a first preset interval, it is determined that the candidate vehicle is located within the lane range of the host vehicle; If the relative lateral distance of the candidate vehicle falls within the second preset interval or the third preset interval, determining that the candidate vehicle is located within the adjacent lane range; The first preset interval, the second preset interval and the third preset interval are set according to the lane width.

6. The method according to claim 4, characterized in that Determining the relative lateral distance between the host vehicle and the candidate vehicle according to the radius of the curve and the relative radial distance between the host vehicle and the candidate vehicle includes: Determine the center angle of the road corresponding to the position point of the candidate vehicle according to the radius of the curve and the relative radial distance between the host vehicle and the candidate vehicle; Obtaining a lateral distance between the vehicle and a center position point according to the radius of the curve and the center angle of the road; The relative lateral distance between the host vehicle and the candidate vehicle is determined according to the lateral distance, the road center angle and the relative radial distance.

7. A curve adaptive cruise control device, characterized in that: include: An acquisition module, used to acquire radar detection data collected by a radar, visual detection data collected by a camera, and inertial detection data collected by an inertial measurement unit, wherein the radar, the camera, and the inertial measurement unit are arranged on the vehicle; A relative information determination module, used to determine the relative information between the vehicle and each candidate vehicle ahead according to the radar detection data and the visual detection data; A lane data determination module, used to determine lane line data according to the inertial detection data and the visual detection data; A vehicle following target screening module, used to determine a target vehicle from the plurality of candidate vehicles according to the lane line data and the relative information, wherein the target vehicle is a candidate vehicle that is located within the lane range of the vehicle and is closest to the vehicle; A determination module, used to determine a safe following distance based on the relative information between the host vehicle and the target vehicle and the host vehicle speed; The vehicle following control module is used to perform tracking control according to the safe vehicle following distance.

8. The device according to claim 7, characterized in that The lane data determination module is specifically used for: Obtaining current lane line data and lane line quality according to the visual detection data; According to the inertial detection data, the lane line data of the vehicle trajectory detected and calculated by the inertial sensor is obtained; Determining a first weight coefficient of the current lane line data and a second weight coefficient of the vehicle trajectory lane line data according to the lane line quality; According to the first weight coefficient and the second weight coefficient, the current lane line data and the vehicle trajectory lane line data are fused to obtain the lane line data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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