Vehicle intelligent driving control method, device and equipment and storage medium

Abstract

The invention discloses a vehicle intelligent driving control method, device and equipment and a storage medium, and relates to the technical field of vehicles, and the vehicle intelligent driving control method comprises the steps: determining a current road scene according to a vehicle road image, vehicle positioning information and navigation map information; when the current road scene is a target road combined scene, performing curve fitting according to lane line historical data and lane line sensing data to obtain virtual lane line data; and performing intelligent driving control of the vehicle according to the virtual lane line data. By intelligently recognizing the Y-shaped road merging scene and fitting the continuous and smooth virtual lane line, an accurate path planning basis is provided for an intelligent driving system, the driving risk caused by path planning errors is remarkably reduced, and a stable and reliable local path planning trajectory is provided in the complex Y-shaped road merging scene. The auxiliary intelligent driving system can easily deal with the scene road condition, and the passing rate and the driving experience of intelligent driving are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to vehicle intelligent driving control methods, devices, equipment and storage media. Background Art

[0002] In the intelligent driving system solution, local path planning needs to have coefficients that effectively characterize the lane lines on both sides of the lane in real time in order to generate the trajectory line of local path planning, and then further based on the linear speed and angular velocity of the vehicle's movement, make the trajectory of the vehicle at these speeds for a period of time in the future coincide with the trajectory line of local path planning. In actual roads, Y-shaped road merging scenes often occur, and the original lane line on one side disappears in the road merging area. At this time, the local path planning algorithm based on the lane lines determined on both sides will lose its effect, causing the intelligent driving vehicle to exit the function or jump to lateral control when passing through the Y-shaped road merging scene, affecting the safety and experience of the intelligent driving function. Summary of the invention

[0003] The main purpose of this application is to provide a vehicle intelligent driving control method, device, equipment and storage medium, aiming to solve the technical problem of how to improve the accuracy of path planning during vehicle intelligent driving.

[0004] To achieve the above objectives, the present application proposes a vehicle intelligent driving control method, the vehicle intelligent driving control method comprising: Determine the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; When the current road scene is a target road merging scene, curve fitting is performed according to lane line historical data and lane line perception data to obtain virtual lane line data; Intelligent driving control of the vehicle is performed according to the virtual lane line data.

[0005] In one embodiment, determining the current road scene according to the vehicle road image, the vehicle positioning information and the navigation map information includes: Performing image processing on the vehicle road image to obtain a lateral distance and an endpoint distance of a first lane line of a preset number of frames, and a lateral distance and an endpoint distance of a second lane line of the preset number of frames, where the second lane line is located at a different position from the first lane line; Performing scene recognition based on the lateral distance and endpoint distance of the first lane line of the preset number of frames and the lateral distance and endpoint distance of the second lane line of the preset number of frames to obtain an initial scene recognition result; The current road scene is determined according to the initial scene recognition result, the vehicle positioning information and the navigation map information.

[0006] In one embodiment, performing scene recognition according to the lateral distance and endpoint distance of the first lane line of the preset number of frames and the lateral distance and endpoint distance of the second lane line of the preset number of frames to obtain an initial scene recognition result includes: Comparing the lateral distance of the first lane line of the preset number of frames with the lateral distance of the second lane line of the preset number of frames to obtain a lateral distance comparison result; Comparing the endpoint distance of the first lane line of the preset number of frames with the endpoint distance of the second lane line of the preset number of frames to obtain an endpoint distance comparison result; When the lateral distance comparison result satisfies a preset lateral distance condition and the endpoint distance comparison result satisfies a preset endpoint distance condition, the initial scene recognition result is determined to be a target road merging scene.

[0007] In one embodiment, determining the current road scene according to the initial scene recognition result, vehicle positioning information, and navigation map information includes: When the initial scene recognition result is a target road merge scene, obtaining current navigation road information according to the vehicle positioning information and the navigation map information; Determining a corresponding navigation road scene according to the current navigation road information; When the navigation road scene is consistent with the target road merging scene, it is determined that the current road type is the target road merging scene.

[0008] In one embodiment, when the current road scene is a target road merging scene, curve fitting is performed according to lane line history data and lane line perception data to obtain virtual lane line data, including: When the current road scene is a target road merge scene, obtaining a lateral distance in lane line history data, and a lane line curvature and a curvature change rate in lane line perception data; Curve fitting is performed according to the lateral distance in the lane line historical data and the lane line curvature and curvature change rate in the lane line perception data to obtain virtual lane line data.

[0009] In one embodiment, the intelligent driving control of the vehicle according to the virtual lane line data includes: When the vehicle is at a target road merging point, determining a corresponding lane line coefficient according to the virtual lane line data; Generate a lane centerline according to the lane line coefficient corresponding to the virtual lane line data and the preset lane line coefficient to obtain a path planning trajectory line; Determine the corresponding vehicle linear velocity and vehicle angular velocity according to the path planning trajectory line; Intelligent driving control of the vehicle is performed according to the vehicle linear velocity and the vehicle angular velocity.

[0010] In one embodiment, after performing intelligent driving control of the vehicle according to the vehicle linear velocity and the vehicle angular velocity, the method further includes: Perform image processing on the current road image to obtain the endpoint distance of the virtual lane line and the endpoint distance of the target lane line; When the endpoint distance of the virtual lane line is equal to the endpoint distance of the target lane line and the endpoint distance of the virtual lane line is a preset distance, the path planning of the virtual lane line is stopped, and the lane line is switched to obtain the lane lines on both sides of the vehicle; Obtaining switching path planning information according to lane lines on both sides of the vehicle; Intelligent driving control of the vehicle is performed according to the switching path planning information.

[0011] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle intelligent driving control device, the vehicle intelligent driving control device comprising: A recognition module, used to determine the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; A fitting module, used for performing curve fitting according to lane line historical data and lane line perception data to obtain virtual lane line data when the current road scene is a target road merge scene; A control module is used to perform intelligent driving control of the vehicle according to the virtual lane line data.

[0012] In addition, to achieve the above-mentioned objectives, the present application also proposes a vehicle intelligent driving control device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the vehicle intelligent driving control method as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the vehicle intelligent driving control method described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the vehicle intelligent driving control method described above.

[0015] This application determines the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; when the current road scene is a target road merging scene, curve fitting is performed based on lane line historical data and lane line perception data to obtain virtual lane line data; and intelligent driving control of the vehicle is performed based on the virtual lane line data. By intelligently identifying the Y-shaped road merging scene and fitting continuous and smooth virtual lane lines, an accurate path planning basis is provided for the intelligent driving system, significantly reducing the driving risk caused by path planning errors and providing a stable and reliable local path planning trajectory line in a complex Y-shaped road merging scene, so that the auxiliary intelligent driving system can easily cope with the road conditions in this scene, improving the pass rate and driving experience of intelligent driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the vehicle intelligent driving control method of the present application; Figure 2 A schematic diagram of Y-shaped road merging scene recognition provided in Example 1 of the vehicle intelligent driving control method of the present application; Figure 3 A schematic diagram of lane line coefficients provided in Example 1 of the vehicle intelligent driving control method of this application; Figure 4 A schematic diagram of virtual lane lines generated for driving toward a Y-shaped road intersection provided in Embodiment 1 of the vehicle intelligent driving control method of the present application; Figure 5 A schematic diagram of virtual lane lines and fitted local path planning provided in Example 1 of the vehicle intelligent driving control method of the present application; Figure 6 A flow chart of the second embodiment of the vehicle intelligent driving control method of the present application; Figure 7 A schematic diagram of driving toward a Y-shaped road intersection or merging point provided in Embodiment 2 of the vehicle intelligent driving control method of the present application; Figure 8 A schematic diagram of a Y-shaped road merging scene recognition process provided in Example 2 of the vehicle intelligent driving control method of the present application; Fig. 9This is a schematic diagram of the module structure of the vehicle intelligent driving control device according to an embodiment of the present application; Fig.10 This is a schematic diagram of the device structure of the hardware operating environment involved in the vehicle intelligent driving control method in the embodiment of the present application.

[0019] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of the embodiment of the present application is: determine the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; when the current road scene is a target road merge scene, perform curve fitting based on lane line historical data and lane line perception data to obtain virtual lane line data; and perform intelligent driving control of the vehicle based on the virtual lane line data.

[0023] In the intelligent driving system solution, local path planning needs to have coefficients that effectively characterize the lane lines on both sides of the lane in real time in order to generate the trajectory line of local path planning, and then further based on the linear speed and angular velocity of the vehicle's movement, make the trajectory of the vehicle at these speeds for a period of time in the future coincide with the trajectory line of local path planning. In actual roads, Y-shaped road merging scenes often occur, and the original lane line on one side disappears in the road merging area. At this time, the local path planning algorithm based on the lane lines determined on both sides will lose its effect, causing the intelligent driving vehicle to exit the function or jump to lateral control when passing through the Y-shaped road merging scene, affecting the safety and experience of the intelligent driving function.

[0024] This application determines the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; when the current road scene is a target road merging scene, curve fitting is performed based on lane line historical data and lane line perception data to obtain virtual lane line data; and intelligent driving control of the vehicle is performed based on the virtual lane line data. By intelligently identifying the Y-shaped road merging scene and fitting continuous and smooth virtual lane lines, an accurate path planning basis is provided for the intelligent driving system, significantly reducing the driving risk caused by path planning errors and providing a stable and reliable local path planning trajectory line in a complex Y-shaped road merging scene, so that the auxiliary intelligent driving system can easily cope with the road conditions in this scene, improving the pass rate and driving experience of intelligent driving.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a vehicle intelligent driving control device that can realize the above functions, etc. The following takes the vehicle intelligent driving control device as the execution subject as an example to illustrate this embodiment and the following embodiments.

[0026] Based on this, the embodiment of the present application provides a vehicle intelligent driving control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle intelligent driving control method of the present application.

[0027] In this embodiment, the vehicle intelligent driving control method includes steps S10 to S40: Step S10, determining the current road scene according to the vehicle road image, vehicle positioning information and navigation map information; It is understandable that the Y-type road merging scenario intelligent driving system integration device is as follows Figure 2 As shown, in this embodiment, the lane line recognition of the visual perception module can reconstruct the equation coefficients of the lane line in the real environment, the scene recognition module combined with the navigation map module can determine the Y-type road merging scene, and the lane line fitting of the virtual lane line module can generate a virtual lane line with a continuous coefficient. The path planning module based on the lane line can generate a trajectory line for the virtual local path planning of the vehicle to smoothly pass through the merging area of ​​the Y-type merging scene through the input fitting lane line coefficient and the lane line coefficient of the other boundary. The virtual trajectory line coefficients (C0', C1', C2', C3') of the path planning module can be used by the control module to generate the vehicle's linear speed and angular velocity control instructions, and the vehicle's lateral and longitudinal control is achieved through the vehicle's execution device. The above modules can be integrated into a smart driving system integration device that can smoothly pass through the Y-type road merging scene.

[0028] It should be noted that the lane line coefficient diagram is as follows Figure 3As shown in the figure, lane line recognition and coefficient generation: the C0, C1, C2, and C3 coefficients of the lane line are extracted through image processing technology, where C0 represents the distance from the origin and the center of the rear axle of the vehicle to the left and right lane lines, C1 represents the angle between the direction of travel of the vehicle and the tangent of the left and right lane lines, C2 and C3 represent the curvature and curvature change rate of the left and right lane lines respectively, and the near end point determined by the field of view FOV of the vehicle camera is represented as the distance Near_End from the center of the rear axle of the vehicle to the nearest end point of the identifiable lane line, and the far end point is the distance Far_End from the farthest point of the identifiable left and right lane lines to the center of the rear axle of the vehicle determined by the camera recognition ability. During the vehicle driving process, the lane line of the lane is the key factor in forming the path planning of the vehicle and controlling the vehicle to drive in the center of the lane. Therefore, the lane line coefficient of the lane is the focus of attention, such as Figure 3 The figure shows the coefficients of the lane lines on the left and right sides of the lane where the vehicle is traveling, where the coefficients of the left and right lane lines are L0_C0, L0_C1, L0_C2, L0_C3, L0_Near_End, L0_Far_End, R0_C0, R0_C1, R0_C2, R0_C3, R0_Near_End, and R0_Far_End respectively.

[0029] It should be noted that, among them, C0 (lateral distance) refers to the lateral distance from the nearest endpoint of the lane line fitting curve to the origin of the vehicle coordinate system; C1 (curve slope) refers to the slope of the lane line curve (the angle between the vehicle speed direction and the curve tangent direction); C2 (curve curvature) refers to the curvature of the lane line curve; C3 (curve curvature change rate) refers to the curvature change rate of the lane line curve; Near_End (near end point of intelligent driving lane line recognition) refers to the near end point of the intelligent driving lane line recognition, which is the lane line endpoint closest to the current position of the vehicle that can be effectively detected by the lane line recognition algorithm, which is limited by the layout of the camera and the field of view; Far_End (far end point of intelligent driving lane line recognition) refers to the near end point of the intelligent driving lane line recognition, which is the lane line endpoint farthest from the current position of the vehicle that can be effectively detected by the lane line recognition algorithm, which is limited by the layout of the camera and the algorithm ability to effectively detect the lane line distance; GPS (Global Positioning System) refers to a high-precision radio navigation positioning system based on artificial earth satellites, which can provide accurate geographic location, vehicle speed and precise time information anywhere in the world and in near-Earth space.

[0030] It can be understood that the vehicle road image refers to the image of the road outside the vehicle collected by the camera, the vehicle positioning information refers to the exact location data of the vehicle's current location, the navigation map information refers to the map information in the vehicle navigation, and the current road scene refers to the road scene where the vehicle is currently located. For example, the Y-type road merging scene refers to the road type - Y-type intersection. Specifically: Y-type road refers to the road type with a Y-shaped intersection, which is formed by two or more roads intersecting at a certain point to form a shape similar to the letter "Y". The merging scene refers to the situation where two or more roads intersect at a certain point and merge into one road.

[0031] In a specific implementation, the scene where the vehicle is located is identified based on the road image outside the vehicle collected by the camera, the vehicle's current exact location data and the map information in the vehicle navigation, and then the road scene where the vehicle is currently located is obtained based on the identification result.

[0032] Step S20, when the current road scene is a target road merge scene, curve fitting is performed according to lane line historical data and lane line perception data to obtain virtual lane line data; It can be understood that the target road merging scenario refers to the Y-shaped road merging scenario, the lane line historical data refers to the lane line information received by the vehicle in the past period of time, the lane line perception data refers to the lane line information obtained by the vehicle through the sensor at the current moment, and the virtual lane line data refers to the virtual lane line data generated by the intelligent driving algorithm fitting. Specifically: when the lane line is missing, blurred, exceeds the width recognized by the algorithm and other limits, you can adopt appropriate fitting methods and response strategies. When the area can be determined to be within the road boundary or the valid drivable area, you can fit a virtual lane line in the corresponding area, thereby improving the accuracy and robustness of lane line detection and providing vehicles with smoother and more continuous road information.

[0033] In the specific implementation, when the current road scene is a Y-shaped road merging scene, the lane line on the side of the vehicle is about to disappear, and then curve fitting is performed based on the lane line information received by the vehicle in the past period of time and the lane line information obtained by the vehicle through sensors at the current moment, and finally a continuous and smooth virtual lane line is obtained.

[0034] In a feasible implementation, step S20 may include steps A21-A22: Step A21, when the current road scene is a target road merge scene, obtaining the lateral distance in the lane line history data, and the lane line curvature and curvature change rate in the lane line perception data; It can be understood that when the current road scene is a Y-shaped road merging scene, in order to accurately fit the virtual lane line, the lateral distance from the nearest endpoint of the corresponding lane line fitting curve to the origin of the vehicle coordinate system is determined based on the lane line information received by the vehicle in the past period of time, as well as the lane line curve curvature and lane line curve curvature change rate in the lane line information obtained by the vehicle through the sensor at the current moment.

[0035] Step A22, performing curve fitting based on the lateral distance in the lane line historical data and the lane line curvature and curvature change rate in the lane line perception data to obtain virtual lane line data.

[0036] It can be understood that curve fitting is performed based on the lateral distance from the nearest endpoint of the lane line fitting curve in the lane line historical data to the origin of the vehicle coordinate system, and the lane line curve curvature and the lane line curve curvature change rate in the lane line perception data. The lateral distance of the virtual lane line obtained is consistent with the lateral distance in the lane line historical data, and the lane line curve curvature and the lane line curve curvature change rate of the virtual lane line are consistent with the lane line curvature and the curvature change rate in the lane line perception data, thereby obtaining the virtual lane line data.

[0037] It should be noted that the virtual lane line diagram generated when driving towards the Y-shaped road intersection is as follows Figure 4 As shown in the figure, virtual lane line fitting: when the vehicle drives near the Y-shaped road merging scene and the value of L0_Far_End starts to decrease, an advanced curve fitting algorithm (such as Bezier curve, spline curve, etc.) is immediately used to combine the lane line historical data (original L0_C0, L0_C1, L0_C2, L0_C3) and real-time perception data (R0_C0, R0_C1, R0_C2, R0_C3) to fit a continuous and smooth virtual lane line L0' (L0' and the original L0 coefficient maintain curvature continuity, and the curvature C2 and curvature change rate C3 of L0' and R0 remain basically consistent). This virtual lane line can accurately reflect the lane direction in the Y-shaped road merging scene area, and provide a reliable path planning reference for the intelligent driving system. At this time, the virtual lane line L0' is the fitted lane line in this direction. When L0_Far_End / L1_Far_End of the two real lane lines in the same direction is equal to 0, it means that the vehicle has traveled to the intersection and vanishing point of the two lane lines. It can be judged that the vehicle has reached the point where the Y-shaped road merges. At this time, the ID of the lane line that was originally farther away in the same direction jumps from L2 to the adjacent lane line L1 in this direction.

[0038] Step S30: performing intelligent driving control of the vehicle according to the virtual lane line data.

[0039] It can be understood that when the vehicle reaches the merging point of the Y-shaped road, based on the fitted virtual lane line coefficient and the real lane line coefficient on the other side, the intelligent driving system uses the dynamic window method, etc. to fit the center line of the virtual lane line and the real lane line as the local path planning trajectory line, and controls the linear speed and angular velocity of the vehicle, and drives according to the planned trajectory path, thereby realizing intelligent driving control of the vehicle.

[0040] In a feasible implementation, step S30 may include steps A31 to A34: Step A31, when the vehicle is at a target road merging point, determining a corresponding lane line coefficient according to the virtual lane line data; It can be understood that the target road merging point refers to the target road merging point, and the lane line coefficient refers to the virtual lane line coefficient.

[0041] In a specific implementation, when the vehicle reaches a merging point of a Y-shaped road, a lane centerline is generated for fitting and a fitted virtual lane line coefficient is obtained.

[0042] Step A32, generating a lane centerline according to the lane line coefficient corresponding to the virtual lane line data and the preset lane line coefficient, and obtaining a path planning trajectory line; It can be understood that the preset lane line coefficient refers to the real lane line coefficient on the other side, the lane centerline refers to the center line between the virtual lane line and the real lane line on the other side, and the path planning trajectory line refers to the vehicle driving trajectory obtained through local path planning, where local path planning refers to intelligent driving path planning, specifically: path planning is how to plan a safe, efficient, and collision-free driving path for an autonomous driving vehicle in a complex environment. Local path planning is to plan the optimal path from the current position to the local target point for the autonomous driving vehicle based on real-time sensor data. This planning is dynamic and needs to be adjusted in real time according to environmental changes.

[0043] In the specific implementation, based on the fitted virtual lane line coefficient and the real lane line coefficient on the other side, the intelligent driving system uses the dynamic window method to fit and generate the center line of the virtual lane line and the real lane line as the local path planning trajectory line, thereby obtaining the path planning trajectory line.

[0044] Step A33, determining the corresponding vehicle linear velocity and vehicle angular velocity according to the path planning trajectory line; It can be understood that the vehicle linear velocity refers to the linear velocity of the vehicle controlled to travel along the path planning trajectory, and the vehicle angular velocity refers to the angular velocity of the vehicle controlled to travel along the path planning trajectory.

[0045] In a specific implementation, the vehicle speed is calculated based on the path planning trajectory line generated by fitting, and then the linear speed and angular speed of the vehicle controlled to travel along the path planning trajectory line are obtained.

[0046] Step A34, performing intelligent driving control of the vehicle according to the vehicle linear velocity and the vehicle angular velocity.

[0047] It can be understood that the linear speed and angular speed of the vehicle are controlled based on the local path planning trajectory line, and the vehicle travels according to the planned trajectory path.

[0048] In a feasible implementation manner, step A34 may include steps B341 to B344: Step B341, performing image processing on the current road image to obtain the endpoint distance of the virtual lane line and the endpoint distance of the target lane line; It can be understood that the current road image refers to the road image when the vehicle is controlled to travel according to the planned trajectory path, and the target lane line refers to the lane line adjacent to the virtual lane line.

[0049] In a specific implementation, when controlling the vehicle to travel along the planned trajectory path, the surrounding environment changes are continuously monitored and the current road image is acquired, thereby obtaining the distance between the vehicle and the far end point of the virtual lane line, and the distance between the vehicle and the far end point of the lane line adjacent to the virtual lane line.

[0050] Step B342, when the endpoint distance of the virtual lane line is equal to the endpoint distance of the target lane line and the endpoint distance of the virtual lane line is a preset distance, stop the path planning of the virtual lane line, and switch the lane line to obtain the lane lines on both sides of the vehicle; It is understandable that the preset distance refers to the originally set zero distance.

[0051] In a specific implementation, when the distance between the vehicle and the far end point of the virtual lane line is equal to the distance between the vehicle and the far end point of the virtual lane line, and the distance between the vehicle and the far end point of the virtual lane line is zero, it indicates that the vehicle has passed through the intersection of the virtual lane line and the adjacent lane line of the real Y-shaped road merging area, and then the vehicle stops traveling according to the path planning trajectory line corresponding to the virtual lane line, and switches the virtual lane line to the real lane line adjacent to the virtual lane line.

[0052] Step B343, obtaining switching path planning information according to lane lines on both sides of the vehicle; It can be understood that the switching path planning information refers to the path planning trajectory after the lane line is switched.

[0053] In a specific implementation, when the switching lane line is a real lane line adjacent to the virtual lane line, the lane center line is fitted according to the real lane lines on both sides of the vehicle, and then the corresponding path planning trajectory, that is, the path planning trajectory, is obtained.

[0054] Step B344, performing intelligent driving control of the vehicle according to the switching path planning information.

[0055] It can be understood that the corresponding linear speed and angular velocity are obtained according to the path planning trajectory after the lane line switches, and then the vehicle is controlled to travel according to the path planning trajectory after the lane line switches according to the calculated linear speed and angular velocity, so as to realize intelligent driving control of the vehicle.

[0056] It should be noted that the virtual lane line and the fitted local path planning diagram are shown in Figure 5 As shown in the figure, path planning and control: when the vehicle reaches the merging point of the Y-shaped road, based on the fitted virtual lane line L0' coefficient and the real lane line R0 coefficient on the other side, the intelligent driving system uses the dynamic window method to fit the center line of the virtual lane line L0' and the real lane line R0 as the local path planning trajectory line, and controls the linear speed and angular velocity of the vehicle to drive according to the planned trajectory path. During driving, the surrounding environment changes are continuously monitored, and the path planning and control strategies are adjusted in time. The vehicle continues to move forward. During the process, if the virtual lane line L0' and the far end point Far_End of the adjacent lane line L1 intersect, that is, the distance value L0'_Far_End / L1_Far_End of the virtual lane line and the adjacent lane line are equal, the virtual lane line is stopped from fitting to the farther end until the far end point L0'_Far_End of the vehicle's virtual lane line is equal to 0, indicating that the vehicle has passed the intersection of the virtual lane line L0' and the real adjacent lane line L1 in the Y-shaped road merging area. At this time, the real adjacent lane line L1 jumps to the lane line L0 of the lane. The subsequent vehicles drive normally according to the path planning generated by the real lane lines L0 and R0 on both sides of the lane as the boundaries, which proves that the vehicle has safely passed the Y-shaped road merging scene area.

[0057] This embodiment determines the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; when the current road scene is a target road merging scene, curve fitting is performed based on lane line historical data and lane line perception data to obtain virtual lane line data; and intelligent driving control of the vehicle is performed based on the virtual lane line data. By intelligently identifying the Y-shaped road merging scene and fitting a continuous and smooth virtual lane line, an accurate path planning basis is provided for the intelligent driving system, significantly reducing the driving risk caused by path planning errors and providing a stable and reliable local path planning trajectory line in a complex Y-shaped road merging scene, so that the auxiliary intelligent driving system can easily cope with the road conditions in this scene, improving the pass rate and driving experience of intelligent driving.

[0058] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 6 , step S10 in the vehicle intelligent driving control method further includes steps S11 to S13: Step S11, performing image processing on the vehicle road image to obtain a lateral distance and an endpoint distance of a first lane line of a preset number of frames, and a lateral distance and an endpoint distance of a second lane line of the preset number of frames, wherein the second lane line is located at a different position from the first lane line; It can be understood that the preset number of frames refers to 5 consecutive frames that are preset.

[0059] In a specific implementation, image processing is performed on the road image outside the vehicle captured by the camera to obtain the lateral distance and endpoint distance between two lane lines in the same direction for five consecutive frames before and after, that is, the lateral distance and endpoint distance corresponding to the first lane line and the second lane line for five consecutive frames before and after are obtained.

[0060] Step S12, performing scene recognition according to the lateral distance and endpoint distance of the first lane line of the preset number of frames and the lateral distance and endpoint distance of the second lane line of the preset number of frames to obtain an initial scene recognition result; It can be understood that the initial scene recognition result refers to the lane scene recognition result determined by image recognition.

[0061] In a specific implementation, the lateral distance and endpoint distance corresponding to the first lane line of the five consecutive frames are compared with the lateral distance and endpoint distance corresponding to the second lane line of the five consecutive frames, respectively, to obtain a lane scene recognition result determined by image recognition.

[0062] In a feasible implementation, step S12 may include steps A121 to A123: Step A121, comparing the lateral distance of the first lane line of the preset number of frames with the lateral distance of the second lane line of the preset number of frames to obtain a lateral distance comparison result; It can be understood that the lateral distance comparison result refers to the lateral distance size comparison result.

[0063] In a specific implementation, the lateral distances corresponding to the first lane line of the five consecutive frames are compared with the lateral distances corresponding to the second lane lines of the five consecutive frames. Then, the lateral distances corresponding to the first lane line of the five consecutive frames are compared with the lateral distances corresponding to the second lane lines of the five consecutive frames. That is, the lateral distance comparison result is obtained.

[0064] Step A122, comparing the endpoint distance of the first lane line of the preset number of frames with the endpoint distance of the second lane line of the preset number of frames to obtain an endpoint distance comparison result; It can be understood that the endpoint distance comparison result refers to the result of the comparison of the size of the endpoint distances.

[0065] In a specific implementation, the endpoint distance corresponding to the first lane line of the five consecutive frames is compared with the endpoint distances corresponding to the second lane lines of the five consecutive frames. Then, the comparison result of the endpoint distance corresponding to the first lane line of the five consecutive frames is obtained, that is, the endpoint distance comparison result is obtained.

[0066] Step A123, when the lateral distance comparison result satisfies a preset lateral distance condition and the endpoint distance comparison result satisfies a preset endpoint distance condition, determining that the initial scene recognition result is a target road merging scene.

[0067] It can be understood that the preset lateral distance condition refers to the preset lateral distance condition for determining whether it is a Y-type road merging scene, and the preset endpoint distance condition refers to the preset endpoint distance condition for determining whether it is a Y-type road merging scene.

[0068] In a specific implementation, when the lateral distance comparison result satisfies the condition that the difference between the lateral distance corresponding to the first lane line and the lateral distance corresponding to the second lane line in the five consecutive frames of data is getting smaller and smaller, and the endpoint distance comparison result satisfies that the endpoint distance corresponding to the first lane line is the same as the endpoint distance corresponding to the second lane line, the initial scene recognition result is determined to be a Y-type road merging scene.

[0069] Step S13, determining the current road scene according to the initial scene recognition result, vehicle positioning information and navigation map information.

[0070] It can be understood that the current road scene refers to the road scene where the vehicle is finally determined to be located.

[0071] In a specific implementation, when the initial scene recognition result is a Y-shaped road merge scene, verification is performed in combination with the vehicle's positioning information and the map information in the navigation map module to obtain the final road scene where the vehicle is located.

[0072] In a feasible implementation, step S13 may include steps A131 to A133: Step A131, when the initial scene recognition result is a target road merge scene, obtaining current navigation road information according to vehicle positioning information and navigation map information; It can be understood that the current navigation road information refers to the information of the current road in the vehicle navigation.

[0073] In a specific implementation, when the initial scene recognition result is a Y-shaped road merging scene, the current road information of the vehicle is determined based on the positioning information of the vehicle and the map information in the navigation map module.

[0074] Step A132, determining a corresponding navigation road scene according to the current navigation road information; It is understandable that by analyzing the current road information of the vehicle, the road scene of the vehicle in the navigation road module is determined. For example, by checking the map and GPS positioning information of the navigation map module, the road scene of the vehicle in the navigation is obtained.

[0075] Step A133, when the navigation road scene is consistent with the target road merging scene, determining that the current road type is the target road merging scene.

[0076] It can be understood that the road scene where the vehicle is located in the navigation is compared with the Y-type road merging scene. When the road scene where the vehicle is located in the navigation is consistent with the Y-type road merging scene, it is determined that the current road type of the vehicle is a Y-type road merging scene.

[0077] It should be noted that the schematic diagram of driving to the Y-shaped road intersection / merging point is as follows Figure 7 As shown in the figure, the Y-shaped road merging scene recognition process is as follows Figure 8As shown, when the vehicle is driving on the road, the coefficients of the lane lines recognized by the camera are continuously updated, and the C0 parameters of the two lane lines in the same direction are compared. If it is detected that the coefficients of the two lane lines in the same direction, such as L0_C0 / L1_C0, are getting smaller and smaller and tending to be consistent in the data of several consecutive frames before and after, this indicates that the two lane lines are about to cross. When the vehicle is driving towards the intersection, the far end points of the two lane lines are the same distance L0_Far_End / L1_Far_End and will approach 0 at the same time, indicating that the two lane lines are about to disappear at the same time. At this time, the scene recognition module will determine that there is a road merging situation here, and further check the map and GPS positioning information of the navigation map module, and compare it with the map lane line scene where the vehicle is located to confirm that the place detected by the vehicle is indeed a Y-shaped road merging scene.

[0078] This embodiment processes the vehicle road image to obtain the lateral distance and endpoint distance of the first lane line of the preset frame number, and the lateral distance and endpoint distance of the second lane line of the preset frame number, and the second lane line is located at a different position from the first lane line; scene recognition is performed based on the lateral distance and endpoint distance of the first lane line of the preset frame number and the lateral distance and endpoint distance of the second lane line of the preset frame number to obtain an initial scene recognition result; the current road scene is determined based on the initial scene recognition result, vehicle positioning information, and navigation map information. In the above manner, the efficiency and accuracy of identifying the Y-shaped road merging scene are improved.

[0079] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the vehicle intelligent driving control method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0080] This application also provides a vehicle intelligent driving control device, please refer to Fig. 9 , the vehicle intelligent driving control device comprises: The recognition module 10 is used to determine the current road scene according to the vehicle road image, the vehicle positioning information and the navigation map information; A fitting module 20, configured to perform curve fitting according to lane line historical data and lane line perception data to obtain virtual lane line data when the current road scene is a target road merge scene; The control module 30 is used to perform intelligent driving control of the vehicle according to the virtual lane line data.

[0081] Optionally, the identification module 10 is further used for: Performing image processing on the vehicle road image to obtain a lateral distance and an endpoint distance of a first lane line of a preset number of frames, and a lateral distance and an endpoint distance of a second lane line of the preset number of frames, where the second lane line is located at a different position from the first lane line; Performing scene recognition based on the lateral distance and endpoint distance of the first lane line of the preset number of frames and the lateral distance and endpoint distance of the second lane line of the preset number of frames to obtain an initial scene recognition result; The current road scene is determined according to the initial scene recognition result, the vehicle positioning information and the navigation map information.

[0082] Optionally, the identification module 10 is further used for: Comparing the lateral distance of the first lane line of the preset number of frames with the lateral distance of the second lane line of the preset number of frames to obtain a lateral distance comparison result; Comparing the endpoint distance of the first lane line of the preset number of frames with the endpoint distance of the second lane line of the preset number of frames to obtain an endpoint distance comparison result; When the lateral distance comparison result satisfies a preset lateral distance condition and the endpoint distance comparison result satisfies a preset endpoint distance condition, the initial scene recognition result is determined to be a target road merging scene.

[0083] Optionally, the identification module 10 is further used for: When the initial scene recognition result is a target road merge scene, obtaining current navigation road information according to the vehicle positioning information and the navigation map information; Determining a corresponding navigation road scene according to the current navigation road information; When the navigation road scene is consistent with the target road merging scene, it is determined that the current road type is the target road merging scene.

[0084] Optionally, the fitting module 20 is further used for: When the current road scene is a target road merge scene, obtaining a lateral distance in lane line history data, and a lane line curvature and a curvature change rate in lane line perception data; Curve fitting is performed according to the lateral distance in the lane line historical data and the lane line curvature and curvature change rate in the lane line perception data to obtain virtual lane line data.

[0085] Optionally, the control module 30 is further used for: When the vehicle is at a target road merging point, determining a corresponding lane line coefficient according to the virtual lane line data; Generate a lane centerline according to the lane line coefficient corresponding to the virtual lane line data and the preset lane line coefficient to obtain a path planning trajectory line; Determine the corresponding vehicle linear velocity and vehicle angular velocity according to the path planning trajectory line; Intelligent driving control of the vehicle is performed according to the vehicle linear velocity and the vehicle angular velocity.

[0086] Optionally, the control module 30 is further used for: Perform image processing on the current road image to obtain the endpoint distance of the virtual lane line and the endpoint distance of the target lane line; When the endpoint distance of the virtual lane line is equal to the endpoint distance of the target lane line and the endpoint distance of the virtual lane line is a preset distance, the path planning of the virtual lane line is stopped, and the lane line is switched to obtain the lane lines on both sides of the vehicle; Obtaining switching path planning information according to lane lines on both sides of the vehicle; Intelligent driving control of the vehicle is performed according to the switching path planning information.

[0087] The vehicle intelligent driving control device provided by the present application adopts the vehicle intelligent driving control method in the above embodiment, which can solve the technical problem of how to improve the accuracy of path planning during vehicle intelligent driving. Compared with the prior art, the beneficial effects of the vehicle intelligent driving control device provided by the present application are the same as the beneficial effects of the vehicle intelligent driving control method provided by the above embodiment, and the other technical features in the vehicle intelligent driving control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0088] The present application provides a vehicle intelligent driving control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the vehicle intelligent driving control method in the above-mentioned embodiment one.

[0089] Reference below Fig.10 , which shows a schematic diagram of the structure of a vehicle intelligent driving control device suitable for implementing an embodiment of the present application. The vehicle intelligent driving control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig.10The vehicle intelligent driving control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0090] like Fig.10 As shown, the vehicle intelligent driving control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the vehicle intelligent driving control device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle intelligent driving control device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a vehicle intelligent driving control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0091] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0092] The vehicle intelligent driving control device provided by the present application adopts the vehicle intelligent driving control method in the above embodiment, which can solve the technical problem of how to improve the accuracy of path planning during vehicle intelligent driving. Compared with the prior art, the beneficial effects of the vehicle intelligent driving control device provided by the present application are the same as the beneficial effects of the vehicle intelligent driving control method provided by the above embodiment, and the other technical features in the vehicle intelligent driving control device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0093] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0094] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0095] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle intelligent driving control method in the above-mentioned embodiment.

[0096] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0097] The above-mentioned computer-readable storage medium may be included in the vehicle intelligent driving control device; or it may exist independently without being assembled into the vehicle intelligent driving control device.

[0098] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle intelligent driving control device, the vehicle intelligent driving control device: determines the current road scene according to the vehicle road image, vehicle positioning information and navigation map information; when the current road scene is a target road merge scene, performs curve fitting according to the lane line history data and the lane line perception data to obtain virtual lane line data; and performs intelligent driving control of the vehicle according to the virtual lane line data.

[0099] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0100] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0101] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0102] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle intelligent driving control method, and can solve the technical problem of how to improve the accuracy of path planning during vehicle intelligent driving. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the vehicle intelligent driving control method provided in the above-mentioned embodiment, and will not be repeated here.

[0103] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned vehicle intelligent driving control method when executed by a processor.

[0104] The computer program product provided by this application can solve the technical problem of how to improve the accuracy of path planning during vehicle intelligent driving. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the vehicle intelligent driving control method provided by the above embodiment, which will not be repeated here.

[0105] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A vehicle intelligent driving control method, characterized in that: The vehicle intelligent driving control method comprises: Determine the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; When the current road scene is a target road merging scene, curve fitting is performed according to lane line historical data and lane line perception data to obtain virtual lane line data; Intelligent driving control of the vehicle is performed according to the virtual lane line data.

2. The method according to claim 1, characterized in that The determining of the current road scene according to the vehicle road image, the vehicle positioning information and the navigation map information includes: Performing image processing on the vehicle road image to obtain a lateral distance and an endpoint distance of a first lane line of a preset number of frames, and a lateral distance and an endpoint distance of a second lane line of the preset number of frames, where the second lane line is located at a different position from the first lane line; Performing scene recognition based on the lateral distance and endpoint distance of the first lane line of the preset number of frames and the lateral distance and endpoint distance of the second lane line of the preset number of frames to obtain an initial scene recognition result; The current road scene is determined according to the initial scene recognition result, the vehicle positioning information and the navigation map information.

3. The method according to claim 2, characterized in that The performing scene recognition according to the lateral distance and the endpoint distance of the first lane line of the preset number of frames and the lateral distance and the endpoint distance of the second lane line of the preset number of frames to obtain an initial scene recognition result includes: Comparing the lateral distance of the first lane line of the preset number of frames with the lateral distance of the second lane line of the preset number of frames to obtain a lateral distance comparison result; Comparing the endpoint distance of the first lane line of the preset number of frames with the endpoint distance of the second lane line of the preset number of frames to obtain an endpoint distance comparison result; When the lateral distance comparison result satisfies a preset lateral distance condition and the endpoint distance comparison result satisfies a preset endpoint distance condition, the initial scene recognition result is determined to be a target road merging scene.

4. The method according to claim 2, characterized in that The determining the current road scene according to the initial scene recognition result, the vehicle positioning information and the navigation map information includes: When the initial scene recognition result is a target road merge scene, obtaining current navigation road information according to the vehicle positioning information and the navigation map information; Determining a corresponding navigation road scene according to the current navigation road information; When the navigation road scene is consistent with the target road merging scene, it is determined that the current road type is the target road merging scene.

5. The method according to claim 1, characterized in that When the current road scene is a target road merging scene, curve fitting is performed according to lane line historical data and lane line perception data to obtain virtual lane line data, including: When the current road scene is a target road merge scene, obtaining a lateral distance in lane line history data, and a lane line curvature and a curvature change rate in lane line perception data; Curve fitting is performed according to the lateral distance in the lane line historical data and the lane line curvature and curvature change rate in the lane line perception data to obtain virtual lane line data.

6. The method according to claim 1, characterized in that The intelligent driving control of the vehicle according to the virtual lane line data includes: When the vehicle is at a target road merging point, determining a corresponding lane line coefficient according to the virtual lane line data; Generate a lane centerline according to the lane line coefficient corresponding to the virtual lane line data and the preset lane line coefficient to obtain a path planning trajectory line; Determine the corresponding vehicle linear velocity and vehicle angular velocity according to the path planning trajectory line; Intelligent driving control of the vehicle is performed according to the vehicle linear velocity and the vehicle angular velocity.

7. The method according to claim 6, characterized in that After the intelligent driving control of the vehicle is performed according to the vehicle linear velocity and the vehicle angular velocity, the method further includes: Perform image processing on the current road image to obtain the endpoint distance of the virtual lane line and the endpoint distance of the target lane line; When the endpoint distance of the virtual lane line is equal to the endpoint distance of the target lane line and the endpoint distance of the virtual lane line is a preset distance, the path planning of the virtual lane line is stopped, and the lane line is switched to obtain the lane lines on both sides of the vehicle; Obtaining switching path planning information according to lane lines on both sides of the vehicle; Intelligent driving control of the vehicle is performed according to the switching path planning information.

8. A vehicle intelligent driving control device, characterized in that: The device comprises: A recognition module, used to determine the current road scene based on the vehicle road image, vehicle positioning information and navigation map information; A fitting module, used for performing curve fitting according to lane line historical data and lane line perception data to obtain virtual lane line data when the current road scene is a target road merge scene; A control module is used to perform intelligent driving control of the vehicle according to the virtual lane line data.

9. A vehicle intelligent driving control device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle intelligent driving control method as claimed in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle intelligent driving control method as described in any one of claims 1 to 7 are implemented.

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