A method for identifying curves in advance

Abstract

The present invention provides a method for identifying curves in advance, which is applied to intelligent driving vehicles. The method includes: step 1, a camera collects ambient light information, lane line image information, and road ahead image information around the vehicle; step 2, an image acquisition device converts the ambient light information into an image electronic signal, and transmits and stores the collected electronic information as an image, and an image processor performs image recognition processing on the image; step 3, calculating the actual length of the lane line; step 4, obtaining the slope of the vehicle's current driving position; step 5, performing image analysis on the road ahead image information to identify the vehicle position and stop line in the image; step 6, if the current weather condition is sunny, the length of the lane line is not greater than a preset threshold, the road ahead is not a slope, there is no vehicle blocking the lane line ahead, and there is no stop line ahead, then it is determined that the road ahead is a curve.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a method for identifying curves in advance. Background Art

[0002] Intelligent Cruise Control (ICC) can control the vehicle's speed based on the radius of a curve to ensure smooth maneuvering. However, curve detection relies on high-precision maps. Without high-precision map coverage, it's difficult to detect curve information before the curve. Typically, the vehicle doesn't detect the curve until it's halfway through the curve, resulting in premature deceleration and the vehicle running out of the lane and unable to center itself. Therefore, in the absence of high-precision maps, early curve recognition is crucial. Existing technologies are affected and limited by camera detection results in complex road environments. For example, they may not be able to provide sufficiently accurate detection results in the face of occlusion and complex road conditions. The camera's detection capabilities are also reduced in complex and adverse weather conditions. Summary of the Invention

[0003] In view of this, the present invention provides a method for identifying curves in advance to solve the technical problem that the existing technology cannot use cameras to identify curves in advance in complex road environments, thereby avoiding vehicle control delays that cause vehicles to run out of lanes and collide.

[0004] The present invention provides a method for identifying curves in advance, which is applied to intelligent driving vehicles. The method includes: step 1, a camera collects ambient light information, lane line image information, and road ahead image information around the vehicle; step 2, an image acquisition device converts the ambient light information into an image electronic signal, and performs image transmission and storage on the collected electronic information, and an image processor performs recognition processing on the image to identify the current weather conditions; step 3, detecting the lane line image information through image processing technology, and calculating the actual length of the lane line according to the coordinates of the pixel points in the image; step 4, obtaining the slope of the vehicle's current driving position based on the information obtained by the camera, in combination with information obtained by an on-board GPS module, a high-precision map, and vehicle sensors; step 5, performing image analysis on the road ahead image information to identify the vehicle position and stop line in the image; step 6, if the current weather condition is sunny, the length of the lane line is not greater than a preset threshold, the road ahead is not a slope, there is no vehicle blocking the lane line, and there is no stop line, then it is determined that the road ahead is a curve.

[0005] Furthermore, the ambient light information includes image data of the sky and the ground.

[0006] Furthermore, the image processor performs recognition processing on the image to identify the current weather conditions, specifically: step 21, segmenting the weather image into a sky image and a ground image; step 22, extracting global features from the weather image, extracting sky features from the sky image, and extracting ground features from the ground image; step 23, fusing all features and importing them into a classifier to identify different weather conditions.

[0007] Furthermore, the weather conditions include sunny days, cloudy days, snowy days, rainy days, foggy days and dusty days.

[0008] Furthermore, the preset threshold is 50m.

[0009] Furthermore, step 3 includes: step 31, the camera collects lane line images in real time; step 32, grayscale conversion and binarization processing are performed on the collected image information; step 33, lane line feature extraction is performed using edge detection and Hough transform methods; step 34, lane lines are detected by image processing technology; step 35, the actual length of the lane lines is calculated by the coordinates of the pixel points in the image.

[0010] Furthermore, the identification of the vehicle position in the image of the road ahead specifically includes: step 511, analyzing the image of the road ahead captured by the camera in real time; step 512, denoising, brightness adjustment, and color correction preprocessing of the image; step 513, using a deep learning model to analyze the image and identify the vehicle in the image; step 514, detecting and locating the target in the image, and calculating the position and size of the target.

[0011] Furthermore, the identification of the stop line in the image of the road ahead specifically includes: step 521, the vehicle-mounted camera collects the image information of the road ahead; step 522, grayscale processing is performed on the collected image, and the region of interest is set according to the distribution characteristics of the road traffic lines in the image; step 523, the grayscale image in the region of interest is smoothed by using Gaussian filtering, and the image in the region of interest is grayscale stretched to enhance the contrast of the image; step 524, the grayscale gradient value of each pixel point in the region of interest in the horizontal and vertical directions, as well as the gradient direction angle, is calculated to form an effective gradient map; step 525, the initial effective gradient map is traversed, interference points are removed, and a region growth source map is obtained; step 526, the region growth source map is scanned, Obtain an initial seed point set for region growth, perform region growth, and obtain a region growth result map composed of valid points containing lane information; Step 527, perform Hough transform on the region growth result map to obtain an initial straight line set, analyze and screen the initial straight line set to obtain an initial target straight line that meets the requirements; Step 528, determine an initial scanning area based on the initial target straight line, scan the initial scanning area, and obtain a final scanning area based on the scanning results; Step 529, scan the final scanning area for the number of valid points, and calculate the percentage of the number of valid points to the total number of pixel points in the final scanning area. When the percentage of valid points in the final scanning area is greater than or equal to the density threshold, determine that the preliminary target straight line is a valid straight line, and then identify the stop line.

[0012] Furthermore, the method also includes: step 7, after determining that the road ahead is a curve, the vehicle slows down and continues to move forward, and determines whether the road ahead is a curve in sequence until the vehicle stops moving forward.

[0013] Furthermore, the method also includes: step 0, activating the intelligent cruise control function of the vehicle.

[0014] This invention provides a method for pre-curve identification. Using a camera to capture various image information around the vehicle, the method then uses automotive hardware and software, including an image processor, an onboard GPS module, high-precision maps, and vehicle sensors, to calculate and identify current weather conditions, the actual length of lane markings, the slope of the vehicle's current driving position, the position of the vehicle ahead, and the stop line. Finally, the method combines this data to determine if the road ahead is a curve. This technical solution addresses the existing problem of cameras being unable to pre-identify curves in complex road environments, thereby preventing vehicle control delays that could cause vehicles to run out of their lanes and cause collisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for identifying curves in advance provided by the present invention;

[0016] Figure 2This is a control process link diagram of a method for identifying curves in advance provided by the present invention;

[0017] Figure 3 This is a flow chart of another method for identifying curves in advance provided by the present invention;

[0018] Figure 4 It is a schematic flow chart of the method for calculating the actual length of a lane line provided by the present invention;

[0019] Figure 5 2. It is a flow chart of the method for determining the position of the vehicle ahead provided by the present invention;

[0020] Figure 6 The figure is a flow chart of a method for identifying a stop line provided by the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Example 1:

[0023] The present invention provides a method for identifying curves in advance, which is applied to intelligent driving vehicles, such as Figure 1 As shown, the method steps are as follows.

[0024] Step 1: The camera collects ambient light information around the vehicle, lane line image information, and front road image information;

[0025] Step 2: The image acquisition device converts the ambient light information into an electronic image signal, transmits and stores the collected electronic information, and the image processor processes the image to identify the current weather conditions;

[0026] Step 3: Detect lane line image information through image processing technology and calculate the actual length of the lane line based on the coordinates of the pixel points in the image;

[0027] Step 4: Based on the information obtained by the camera, combined with the information obtained by the vehicle GPS module, high-precision map and vehicle sensors, the slope of the vehicle's current driving position is obtained;

[0028] Step 5: Perform image analysis on the road ahead image information to identify the vehicle position and stop line in the image;

[0029] Step 6: If the current weather condition is sunny, the length of the lane line is not greater than the preset threshold, the road ahead is not a slope, there is no vehicle blocking the lane line ahead, and there is no stop line, then it is determined that the road ahead is a curve.

[0030] This invention provides a method for pre-curve identification. Using a camera to capture various image information around the vehicle, the method then uses automotive hardware and software, including an image processor, an onboard GPS module, high-precision maps, and vehicle sensors, to calculate and identify current weather conditions, the actual length of lane markings, the slope of the vehicle's current driving position, the position of the vehicle ahead, and the stop line. Finally, the method combines this data to determine if the road ahead is a curve. This technical solution addresses the existing problem of cameras being unable to pre-identify curves in complex road environments, thereby preventing vehicle control delays that could cause vehicles to run out of their lanes and cause collisions.

[0031] Example 2:

[0032] The present invention provides a method for identifying curves in advance, which is applied to intelligent driving vehicles, such as Figure 2 As shown, the method steps are as follows.

[0033] Step 1: The camera collects ambient light information around the vehicle, lane line image information, and front road image information;

[0034] The ambient light information includes image data of the sky and the ground, and the subsequent steps will divide the image into two parts, the sky and the ground, for processing.

[0035] Step 2: The image acquisition device converts the ambient light information into an electronic image signal, transmits and stores the collected electronic information, and the image processor processes the image to identify the current weather conditions;

[0036] The weather conditions include sunny, cloudy, snowy, rainy, foggy and dusty. The specific process of identifying the current weather conditions is as follows.

[0037] Step 21, segmenting the weather image into a sky image and a ground image;

[0038] Step 22: extracting global features from the weather image, extracting sky features from the sky image, and extracting ground features from the ground image;

[0039] In step 23, all features are fused and imported into the classifier to identify different weather conditions.

[0040] Since the present invention is applicable to scenes with high visibility, that is, when the weather condition is clear, the weather condition can be identified and the technical solution can be used in appropriate situations to improve safety.

[0041] Step 3: Detect lane line image information through image processing technology and calculate the actual length of the lane line based on the coordinates of the pixel points in the image;

[0042] Step 4: Based on the information obtained by the camera, combined with the information obtained by the vehicle GPS module, high-precision map and vehicle sensors, the slope of the vehicle's current driving position is obtained;

[0043] As can be seen from this step, the technical solution provided by the present invention requires combining the data provided by the vehicle's onboard GPS (Global Positioning System) module, high-precision maps, and vehicle sensors to make the final curve determination, rather than simply analyzing the image data provided by the camera. Existing technologies use forward-looking cameras to detect the distance to the lane ahead, monitor the slope of the road ahead, monitor whether the vehicle ahead is pressing against the lane ahead, identify stop lines on the road, and determine weather conditions. However, the camera's detection results are affected and limited in complex road environments. For example, it may not provide sufficiently accurate detection results when faced with occlusion or complex road conditions. Furthermore, the camera's detection capabilities are also reduced in complex and harsh weather conditions. The present invention, however, combines the data collected by the camera with other software and hardware on the vehicle to perform high-precision recognition and calculation, enabling the vehicle to better identify the road environment, especially curves, in specific driving scenarios.

[0044] Step 5: Perform image analysis on the road ahead image information to identify the vehicle position and stop line in the image;

[0045] Step 6: If the current weather condition is sunny, the length of the lane line is not greater than the preset threshold, the road ahead is not a slope, there is no vehicle blocking the lane line ahead, and there is no stop line, then it is determined that the road ahead is a curve.

[0046] The preset threshold described here is usually 50m, which can of course be adjusted according to actual conditions.

[0047] This invention provides a method for pre-curve identification. Using a camera to capture various image information around the vehicle, the method then uses automotive hardware and software, including an image processor, an onboard GPS module, high-precision maps, and vehicle sensors, to calculate and identify current weather conditions, the actual length of lane markings, the slope of the vehicle's current driving position, the position of the vehicle ahead, and the stop line. Finally, the method combines this data to determine if the road ahead is a curve. This technical solution addresses the existing problem of cameras being unable to pre-identify curves in complex road environments, thereby preventing vehicle control delays that could cause vehicles to run out of their lanes and cause collisions.

[0048] Example 3:

[0049] The present invention provides a method for identifying curves in advance, which is applied to intelligent driving vehicles, such as Figure 3 As shown, the method steps are as follows.

[0050] Step 0: Activate the vehicle's smart cruise control function.

[0051] The technical solution provided by the present invention is generally implemented when ICC (Intelligent Cruise Control) is activated.

[0052] Step 1: The camera collects ambient light information around the vehicle, lane line image information, and front road image information;

[0053] Step 2: The image acquisition device converts the ambient light information into an electronic image signal, transmits and stores the collected electronic information, and the image processor processes the image to identify the current weather conditions;

[0054] Step 3: Detect lane line image information through image processing technology and calculate the actual length of the lane line based on the coordinates of the pixel points in the image;

[0055] like Figure 4 As shown in FIG, the specific process of calculating the actual length of the lane line is as follows.

[0056] Step 31: The camera collects lane line images in real time;

[0057] Step 32, performing grayscale conversion and binarization processing on the collected image information;

[0058] Step 33: extract lane line features using edge detection and Hough transform methods;

[0059] Step 34: Detect lane lines using image processing technology;

[0060] Step 35: Calculate the actual length of the lane line using the coordinates of the pixel points in the image.

[0061] Step 4: Based on the information obtained by the camera, combined with the information obtained by the vehicle GPS module, high-precision map and vehicle sensors, the slope of the vehicle's current driving position is obtained;

[0062] Step 5: Perform image analysis on the road ahead image information to identify the vehicle position and stop line in the image;

[0063] like Figure 5 As shown, the specific steps of identifying the vehicle position in the front road image are as follows.

[0064] Step 511, analyzing the front road image captured by the camera in real time;

[0065] Step 512: performing denoising, brightness adjustment, and color correction preprocessing on the image;

[0066] Step 513: Analyze the image using a deep learning model to identify the vehicle in the image;

[0067] Step 514: Detect and locate the target in the image, and calculate the position and size of the target.

[0068] like Figure 6 As shown, the specific steps of identifying the stop line in the front road image are as follows.

[0069] Step 521: The vehicle-mounted camera collects image information of the road ahead;

[0070] Step 522: grayscale the collected image and set a region of interest based on the distribution characteristics of road traffic lines in the image;

[0071] Step 523: smoothing the grayscale image within the region of interest using Gaussian filtering, and performing grayscale stretching on the image within the region of interest to enhance the contrast of the image;

[0072] Step 524 , calculating the grayscale gradient values ​​in the horizontal and vertical directions, as well as the gradient direction angles, of each pixel point in the region of interest to form an effective gradient map;

[0073] Step 525, traverse the initial effective gradient map, remove interference points, and obtain a region growing source map;

[0074] Step 526 , scanning the region growing source map to obtain a set of initial seed points for region growing, and performing region growing to obtain a region growing result map consisting of valid points containing lane information;

[0075] Step 527: Perform Hough transform on the region growing result image to obtain an initial line set, analyze and screen the initial line set to obtain an initial target line that meets the requirements;

[0076] Step 528, determining an initial scanning area based on the initial target line, scanning the initial scanning area, and obtaining a final scanning area based on the scanning results;

[0077] Step 529, scan the final scanning area for the number of valid points, calculate the percentage of the number of valid points to the total number of pixels in the final scanning area, and when the percentage of valid points in the final scanning area is greater than or equal to the density threshold, determine that the preliminary target straight line is a valid straight line, and then identify the stop line.

[0078] Step 6: If the current weather condition is sunny, the length of the lane line is not greater than the preset threshold, the road ahead is not a slope, there is no vehicle blocking the lane line ahead, and there is no stop line, then it is determined that the road ahead is a curve.

[0079] Step 7: After determining that the road ahead is a curve, the vehicle decelerates and continues to move forward, and determines whether the road ahead is a curve in sequence until the vehicle stops moving forward.

[0080] This invention provides a method for pre-curve identification. Using a camera to capture various image information around the vehicle, the method then uses automotive hardware and software, including an image processor, an onboard GPS module, high-precision maps, and vehicle sensors, to calculate and identify current weather conditions, the actual length of lane markings, the slope of the vehicle's current driving position, the position of the vehicle ahead, and the stop line. Finally, the method combines this data to determine if the road ahead is a curve. This technical solution addresses the existing problem of cameras being unable to pre-identify curves in complex road environments, thereby preventing vehicle control delays that could cause vehicles to run out of their lanes and cause collisions.

[0081] In summary, an embodiment of the present invention provides a method for pre-identifying curves. This method uses a camera to capture various image information around the vehicle. The method then utilizes automotive hardware and software, including an image processor, an onboard GPS module, a high-precision map, and vehicle sensors, to calculate and identify the current weather conditions, the actual length of the lane markings, the slope of the vehicle's current driving position, the position of the vehicle ahead, and the stop line. Finally, the method combines this data to determine if the road ahead is a curve. This technical solution addresses the existing inability to pre-identify curves using cameras in complex road environments, thereby avoiding vehicle control delays that could cause the vehicle to run out of its lane and cause a collision.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying curves in advance, applied to intelligent driving vehicles, characterized in that: The method comprises: Step 1: The camera collects ambient light information around the vehicle, lane line image information, and front road image information; Step 2: The image acquisition device converts the ambient light information into an electronic image signal, transmits and stores the collected electronic information, and the image processor processes the image to identify the current weather conditions; Step 3: Detect lane line image information through image processing technology and calculate the actual length of the lane line based on the coordinates of the pixel points in the image; Step 4: Based on the information obtained by the camera, combined with the information obtained by the vehicle GPS module, high-precision map and vehicle sensors, the slope of the vehicle's current driving position is obtained; Step 5: Perform image analysis on the road ahead image information to identify the vehicle position and stop line in the image; Step 6: If the current weather condition is sunny, the length of the lane line is not greater than the preset threshold, the road ahead is not a slope, there is no vehicle blocking the lane line ahead, and there is no stop line, then it is determined that the road ahead is a curve.

2. The method for identifying a curve in advance according to claim 1, characterized in that: The ambient light information includes image data of the sky and the ground.

3. The method for identifying a curve in advance according to claim 2, characterized in that: The image processor performs recognition processing on the image to identify the current weather conditions, specifically: Step 21, segmenting the weather image into a sky image and a ground image; Step 22: extracting global features from the weather image, extracting sky features from the sky image, and extracting ground features from the ground image; In step 23, all features are fused and imported into the classifier to identify different weather conditions.

4. The method for identifying a curve in advance according to claim 1 or 3, characterized in that: The weather conditions include sunny days, cloudy days, snowy days, rainy days, foggy days and dusty days.

5. The method for identifying a curve in advance according to claim 1, characterized in that: The preset threshold is 50m.

6. The method for identifying a curve in advance according to claim 1, characterized in that: The step 3 includes: Step 31: The camera collects lane line images in real time; Step 32, performing grayscale conversion and binarization processing on the collected image information; Step 33: Use edge detection and Hough transform method to extract lane line features; Step 34: Detect lane lines using image processing technology; Step 35: Calculate the actual length of the lane line using the coordinates of the pixel points in the image.

7. The method for identifying a curve in advance according to claim 1, characterized in that: The identifying of the vehicle position in the image specifically includes: Step 511, analyzing the front road image captured by the camera in real time; Step 512: performing denoising, brightness adjustment, and color correction preprocessing on the image; Step 513: Analyze the image using a deep learning model to identify the vehicle in the image; Step 514: Detect and locate the target in the image, and calculate the position and size of the target.

8. The method for identifying a curve in advance according to claim 1, characterized in that: The identifying stop line in the image specifically includes: Step 521: The vehicle-mounted camera collects image information of the road ahead; Step 522: grayscale the collected image and set a region of interest based on the distribution characteristics of road traffic lines in the image; Step 523: smoothing the grayscale image within the region of interest using Gaussian filtering, and performing grayscale stretching on the image within the region of interest to enhance the contrast of the image; Step 524 , calculating the grayscale gradient values ​​in the horizontal and vertical directions, as well as the gradient direction angles, of each pixel point in the region of interest to form an effective gradient map; Step 525, traverse the initial effective gradient map, remove interference points, and obtain a region growing source map; Step 526 , scanning the region growing source map to obtain a set of initial seed points for region growing, and performing region growing to obtain a region growing result map consisting of valid points containing lane information; Step 527: Perform Hough transform on the region growing result image to obtain an initial line set, analyze and screen the initial line set to obtain an initial target line that meets the requirements; Step 528, determining an initial scanning area based on the initial target line, scanning the initial scanning area, and obtaining a final scanning area based on the scanning results; Step 529, scan the final scanning area for the number of valid points, calculate the percentage of the number of valid points to the total number of pixels in the final scanning area, and when the percentage of valid points in the final scanning area is greater than or equal to the density threshold, determine that the initial target straight line is a valid straight line, and then identify the stop line.

9. The method for identifying a curve in advance according to claim 1, characterized in that: The method further comprises: Step 7: After determining that the road ahead is a curve, the vehicle decelerates and continues to move forward, and determines whether the road ahead is a curve in sequence until the vehicle stops moving forward.

10. The method for identifying a curve in advance according to claim 1, characterized in that: The method further comprises: Step 0: Activate the vehicle's smart cruise control function.

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