Unmanned vehicle and driving route generation method, device, medium, and product

Abstract

The present invention discloses an unmanned vehicle and a driving route generation method, device, medium, and product, which are applied to the field of autonomous driving technology. The method includes determining a target object contained in a road surface to be detected based on the three-dimensional shape information of the road surface to be detected, and obtaining the three-dimensional shape data of the target object; the target object is a road raised obstacle or a pothole area; according to the three-dimensional shape data of the target object and the vehicle parameters, determining the hazard of the target object to the target vehicle and the hazard of the driving state, respectively, and obtaining the target object hazard assessment result; based on the target object hazard assessment result corresponding to each road raised obstacle and / or pothole area contained in the road surface to be detected, generating the vehicle driving route of the target vehicle. The present invention can solve the problem that the vehicle driving route of the related technology cannot meet the needs of users, and can generate a vehicle driving route that is both comfortable and safe.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a vehicle driving route generation method, electronic equipment, non-volatile storage medium, computer program product and unmanned vehicle. Background Art

[0002] With the rapid development of artificial intelligence and Internet of Things technologies, autonomous driving technology has also developed accordingly. When an unmanned vehicle is driving, a vehicle route will be automatically generated, which will affect the normal operation of the autonomous vehicle and the comfort of the passengers.

[0003] Related technologies usually automatically generate vehicle routes based on road geometry, obstacle locations, road congestion and traffic rules. Among them, obstacles focus on large obstacles that affect normal driving. This causes the vehicle to be bumpy when driving along the route, affecting the comfort of the passengers and reducing the safety of the vehicle.

[0004] In view of this, generating a vehicle driving route with both high comfort and high safety is a technical problem that needs to be solved by those skilled in the art. It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0005] The present invention provides a vehicle driving route generation method, electronic equipment, non-volatile storage medium, computer program product and unmanned vehicle, which can generate a vehicle driving route with both high comfort and high safety.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] One aspect of the present invention provides a method for generating a vehicle driving route, comprising:

[0008] Acquire three-dimensional shape information of a road surface to be detected and vehicle parameters of a target vehicle; determine a target object contained in the road surface to be detected based on the three-dimensional shape information, and obtain three-dimensional shape data of the target object; the target object is a raised obstacle or a pothole area on the road; determine the hazard of the target object to the target vehicle and the hazard of the driving state respectively according to the three-dimensional shape data of the target object and the vehicle parameters, and obtain a target object hazard assessment result; based on the target object hazard assessment results corresponding to each road raised obstacle and / or pothole area contained in the road surface to be detected, generate a vehicle driving route of the target vehicle.

[0009] In a first exemplary embodiment, determining the hazards of the target object to the target vehicle according to the three-dimensional shape data of the target object and the vehicle parameters includes:

[0010] The probability of the target object scratching the chassis of the target vehicle is determined based on the maximum elevation of the target object and the chassis height of the vehicle; and the hazard of the target object scratching the chassis of the target vehicle is determined based on the chassis scratch probability and the vehicle parameters.

[0011] In a second exemplary implementation, determining the chassis scratch hazard of the target object to the target vehicle according to the chassis scratch probability and the vehicle parameter includes:

[0012] The maximum scratch time of the target vehicle is determined according to the vehicle length and the vehicle movement speed in the vehicle parameters; and the chassis scratch hazard value of the target object to the target vehicle is obtained according to the product of the chassis scratch probability and the maximum scratch time.

[0013] In a third exemplary embodiment, the target object is a pothole area, and determining the hazard of the target object to the target vehicle and the hazard of the driving state of the target vehicle according to the three-dimensional shape data of the target object and the vehicle parameters includes:

[0014] The probability of the target vehicle falling into the pothole area is determined based on the angle between the driving direction of the target vehicle and the pothole area and the tire width in the vehicle parameters; the hazard of the target vehicle falling into the pothole area is determined based on the three-dimensional topography data of the pothole area, the vehicle parameters, the probability of falling into the pothole and the time of passing through the pothole area.

[0015] In a fourth exemplary embodiment, determining the probability of the target vehicle falling into the pothole area according to the angle between the driving direction of the target vehicle and the pothole area and the tire width in the vehicle parameters includes:

[0016] The projection length of the pothole area in the tire width direction is determined according to the width of the pothole area and the angle between the horizontal plane of the pothole area and the lane line; if the tire width of the target vehicle is less than or equal to the projection length, the target vehicle falls into the pothole area; if the tire width of the target vehicle is greater than the projection length, the probability of falling into the pothole is determined according to the ratio of the projection length to the tire width.

[0017] In a fifth exemplary embodiment, determining the entrapment hazard of the target vehicle according to the three-dimensional shape data of the pothole area, the vehicle parameters, the entrapment probability and the time of passing through the entrapment area includes:

[0018] The target vehicle's entrapment hazard level is determined based on the average elevation of the pothole area and the vehicle chassis height in the vehicle parameters; the target vehicle's entrapment length is determined based on the angle between the target vehicle's driving direction and the pothole area and the length of the pothole area; the target vehicle's entrapment time is determined based on the entrapment length and the vehicle's running speed in the vehicle parameters; the target vehicle's entrapment hazard value is determined based on the entrapment probability, the entrapment hazard level and the entrapment time.

[0019] In a sixth exemplary implementation, determining the hazard of the target object to the target vehicle and the hazard of the driving state of the target vehicle respectively according to the three-dimensional shape data of the target object and the vehicle parameters includes:

[0020] The length of the bumpy area of ​​the target vehicle is determined based on the angle between the driving direction of the target vehicle and the pothole area and the length of the target object; the bumpy area time is determined based on the bumpy area length and the vehicle running speed of the target vehicle; and the bumpy hazard value of the target vehicle during driving is determined based on the elevation mean square error in the three-dimensional morphology data of the target object and the bumpy area time.

[0021] In a seventh exemplary implementation, determining the hazard of the target object to the target vehicle and the hazard of the driving state of the target vehicle respectively according to the three-dimensional shape data of the target object and the vehicle parameters includes:

[0022] The road surface to be inspected is divided into a plurality of areas to be analyzed according to the number of lanes and lane widths, and the area ratio hazard degree value is determined according to the size of the area to be analyzed, the number of lanes occupied by the target object and the three-dimensional shape data.

[0023] In an eighth exemplary implementation, the road surface to be detected is divided into a plurality of areas to be analyzed according to the number of lanes and the lane width, and the area ratio hazard degree value is determined according to the size of the area to be analyzed, the number of lanes occupied by the target object and the three-dimensional shape data, including:

[0024] According to the product of the preset area length, the number of lanes and the lane width as the area width, the road surface to be detected is divided into multiple areas to be analyzed at equal distances along the road direction; the road area is determined according to the number of lanes occupied by the target object, the preset area length and the lane width; the area ratio hazard degree value is determined according to the ratio of the obstacle area area in the three-dimensional shape data of the target object to the road area area.

[0025] In a ninth exemplary embodiment, determining the hazard of the target object to the target vehicle and the hazard of the driving state of the target vehicle according to the three-dimensional shape data of the target object and the vehicle parameters, and obtaining the target object hazard assessment result includes:

[0026] Obtain the chassis scratch hazard value, entrapment hazard value, bump hazard value and area ratio hazard degree value of the target object; determine the single obstacle vehicle hazard value based on the chassis scratch hazard value, entrapment hazard value and bump hazard value of the target object; determine the target object hazard assessment result based on the numerical relationship between the product of the single obstacle vehicle hazard value and the area ratio hazard degree value and a first preset hazard threshold.

[0027] In a tenth exemplary embodiment, generating a vehicle driving route of a target vehicle based on target object hazard assessment results corresponding to each road bulge obstacle and / or pothole area included in the road surface to be detected includes:

[0028] According to the target object hazard assessment results corresponding to each road bulge obstacle and each pothole area contained in the road surface to be detected, the target object hazard average value of the road surface to be detected is calculated, and the hazard average value of each type of hazard is counted; according to the numerical relationship between the target object hazard average value and a second preset hazard threshold, the target object hazard degree assessment result is determined; the maximum value is selected from each hazard mean value, and the hazard type corresponding to the maximum value is used as the target object hazard category of the road surface to be detected; according to the target object hazard degree assessment result and the target object hazard category, the obstacle avoidance area of ​​the road surface to be detected is determined, and the vehicle driving route of the target vehicle is generated based on the obstacle avoidance area.

[0029] The present invention also provides an electronic device, comprising a processor, wherein the processor is used to implement a vehicle driving route generation method when executing a computer program stored in a memory.

[0030] The present invention also provides a non-volatile storage medium, on which a computer program is stored, and when the computer program is executed by a processor, a vehicle driving route generation method is implemented.

[0031] The present invention also provides a computer program product, including a computer program / instruction, which implements the vehicle driving route generation method when executed by a processor.

[0032] Finally, the present invention also provides an unmanned vehicle, including an automatic driving perception system, wherein the automatic driving perception system is used to implement a vehicle driving route generation method when executing a computer program stored in a memory.

[0033] The advantage of the technical solution provided by the present invention is that when automatically generating a vehicle driving route for a target vehicle, accurate three-dimensional morphology information is first obtained, and based on the accurate three-dimensional morphology information, it is identified whether there are road obstacles or potholes on the road ahead, and the hazards of the road obstacles or potholes to the vehicle itself and the operation of the vehicle are analyzed. The analysis results can reflect whether it is safe for the vehicle to drive on the road surface and the comfort of the user. According to the analysis results, a vehicle driving route with both high comfort and high safety can be generated. Since the vehicle driving route takes into account the safety of the vehicle and the comfort of the user, when the target vehicle drives along the route, the safety and comfort of the self-driving vehicle are effectively improved.

[0034] In addition, the present invention also provides corresponding electronic devices, non-volatile storage media, computer program products and unmanned vehicles for implementing the vehicle driving route generation method, further making the method more practical, and the electronic devices, non-volatile storage media, computer program products and unmanned vehicles have corresponding advantages.

[0035] The technical features mentioned above, the technical features to be mentioned below, and the technical features shown separately in the drawings can be arbitrarily combined with each other, as long as the combined technical features are not contradictory. All feasible feature combinations are technical contents clearly recorded in this article. Any of the multiple sub-features contained in the same sentence can be applied independently, and does not have to be applied together with other sub-features. It should be understood that the above general description and the detailed description below are only exemplary and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic diagram of a process flow of a vehicle driving route generation method provided by the present invention;

[0038] Figure 2 A schematic diagram of coordinate system definition in an exemplary example is provided for the present invention;

[0039] Figure 3 A schematic diagram of three-dimensional shape data of a target object provided by the present invention in an exemplary application scenario;

[0040] Figure 4 A schematic diagram of a target object provided by the present invention falling into harm in an exemplary application scenario;

[0041] Figure 5 A schematic diagram of the degree of harm of an exemplary application scenario provided by the present invention;

[0042] Figure 6 A schematic diagram of overall hazard assessment in an exemplary application scenario provided by the present invention;

[0043] Figure 7 A structural diagram of a specific implementation of the vehicle driving route generating device provided by the present invention;

[0044] Figure 8 A structural diagram of a specific implementation of an electronic device provided by the present invention;

[0045] Fig. 9 A schematic diagram of target object recognition for an unmanned vehicle provided by the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Among them, the terms "first", "second", "third", "fourth", etc. in the specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations of the two are intended to cover non-exclusive inclusions. The term "exemplary" means "used as an example, embodiment or illustrative". Any embodiment described here as "exemplary" is not necessarily interpreted as being superior or better than other embodiments.

[0047] It is understandable that when an autonomous vehicle is driving on a road with potholes or low raised obstacles, it will cause the vehicle to be unstable and then bumpy, which will not only affect the comfort of the passengers, but also cause the vehicle to suddenly lose control and cause a traffic accident. If a vehicle is driven on a road with potholes and low raised obstacles for a long time, it will damage the vehicle's suspension system, tires, and sensors, which will in turn affect the vehicle's performance and safety. In addition, potholes will damage the road surface material and cause the roadbed to settle. If they are not repaired in time, the pothole area will easily expand and deepen, accelerating the damage to the road infrastructure and affecting the service life and carrying capacity of the road.

[0048] In order to avoid the above problems, the impact and vibration caused by potholes / low raised obstacles on the road can be absorbed and offset by the intelligent suspension system, but it is also necessary to monitor the pothole area or raised obstacle area, and it will lead to an increase in the cost of the entire vehicle. When planning a driving route for an autonomous vehicle, it is possible to obtain whether there are potholes or raised obstacle areas in front of the road, and avoid potholes or raised obstacle areas to plan the autonomous driving route. This requires monitoring the potholes or low raised obstacles on the road and evaluating their danger to the driving vehicle. At present, the traditional way to monitor whether there are potholes or low raised obstacles on the road is manual visual inspection, and structural engineers and certified inspectors regularly check whether there are potholes or low raised obstacles on the road, and report the location of such target areas. This method relies on the personal experience of inspectors and engineers, which not only cannot guarantee the accuracy of the detection results, but also has low efficiency, high cost, and may be dangerous. In order to solve the drawbacks of manual visual inspection, relevant technologies use computer vision technology to achieve more efficient and safer detection of road target areas. The target areas here include road obstacle areas that affect the normal operation of vehicles, including but not limited to potholes and low obstacle areas.

[0049] Related technologies use computer vision algorithms to achieve three-dimensional road imaging through 3D point cloud modeling, and then detect road obstacle areas based on segmentation methods to improve the accuracy of road target area detection. This method uses some algorithms, such as RANSAC (Random Sample Consensus) to improve the robustness of road point cloud modeling and DCNN (Deep Convolution Neural Network) to improve image classification accuracy. However, this type of method requires a small field of view and is based on a single-frame 3D road point cloud that is a plane or quadratic surface. The detection accuracy and scene adaptability are still difficult to achieve practicality, and it is difficult to obtain the width, depth, volume and other geometric characteristics of road pits with high precision. For example, a related technology realizes the detection of potholes on the road surface of unmanned vehicles through multi-sensor fusion. This method uses sound waves to scan the road surface ahead. When a pothole is detected, the camera is used to capture the road image, and then the pre-processed image is threshold segmented to obtain a binary image. The number of white pixels in the binary image is counted to determine whether there is water in the pothole. The image information captured by the camera is used to calculate the width of the pothole edge from the road edge on both sides, and the minimum torque for the unmanned vehicle to pass through the pothole is calculated. Finally, the relationship between the minimum torque and the maximum torque is used to determine whether the unmanned vehicle can pass safely. This method is greatly affected by the threshold value for image binarization, is difficult to adapt to various lighting conditions, and cannot provide information such as the depth of the pothole. Another related technology uses an RGB-D (Red Green Blue-Depth, a color image with depth information) depth camera to detect low raised obstacles and potholes on the road. This method uses the original point cloud of the RGB-D depth camera for preprocessing to obtain the corresponding plane point cloud, extracts the raised low obstacle point cloud data from the plane point cloud, and projects the extracted raised low obstacle point cloud onto the plane model parameter plane, downsamples the raised low obstacle point cloud on the model parameter plane, performs convex hull segmentation, transforms the convex hull to the / map (map) coordinate system and publishes it, merges the projected raised low obstacle point cloud into the ground plane point cloud to form a new plane point cloud, transforms the new plane point cloud to the base_footprint (robot body coordinate system) into a ground plane two-dimensional grid map, performs pseudo pothole filtering on the ground plane two-dimensional grid map, and extracts potholes using an edge extraction method. Since the effective working distance of RGB-D cameras is very limited, usually within 10 meters, and they are basically ineffective under strong outdoor light, the accuracy of the point cloud generated by the RGB-D camera is poor due to the influence of the projection density of the generator. The effectiveness of the actual use process needs to be verified, the usage scenarios are limited, and the final detection accuracy cannot be guaranteed.Another related technology is to detect potholes on the road based on neural networks and binocular stereo vision. This method uses a binocular camera installed in front of the vehicle to obtain left and right views of the road scene; and uses a target detection neural network to detect the left view. When a pothole is detected, the left and right views are stereo matched to obtain a depth map. The pothole position information predicted by the neural network is used to cut out the depth map of the pothole and the road surface around the pothole. Finally, a combination of RANSAC and genetic algorithms is used to fit the road plane around the pothole, so as to calculate the distance from the pothole point cloud to the fitting plane, and the distance is summed and averaged to obtain the pothole depth. The minimum circumscribed rectangle of the projection point is determined by projecting the pothole point cloud onto the fitting road plane, and the area of ​​the pothole is obtained by determining the area of ​​the minimum circumscribed rectangle. However, due to the existence of void points in the parallax, this method has a large estimation error in the depth direction, and it is difficult to obtain high-precision pothole depth and bump height, resulting in low detection accuracy of road target areas.

[0050] It can be seen that the related art fails to accurately estimate the depth or elevation of the pothole / obstacle area, resulting in low detection accuracy of road potholes and low raised obstacles on the road. In addition, the related art does not evaluate the danger of the pothole / obstacle area to the driving vehicle, which leads to the inability of the related art to generate a vehicle driving route that takes into account both high safety and high comfort. The present invention first obtains high-precision three-dimensional morphological information, and then comprehensively evaluates the harmfulness of the road raised obstacles and / or pothole areas from the local to the whole, and comprehensively evaluates the spatial distribution of all obstacles on the road surface, providing more accurate data for obstacle avoidance, which helps to improve the safety and comfort of self-driving vehicles. After introducing the technical solution of the present invention, various non-limiting embodiments of the present invention are described in detail below. In order to better illustrate the present invention, many specific details are given in the specific embodiments below. Those skilled in the art should understand that the present invention can also be implemented without these specific details. In some other examples, the methods, means, components and circuits well known to those skilled in the art are not described in detail to highlight the main purpose of the present invention.

[0051] First see Figure 1 , Figure 1 A schematic diagram of a process flow of a vehicle driving route generation method provided in this embodiment, this embodiment may include the following contents:

[0052] S101: Acquire the three-dimensional shape information of the road surface to be detected and the vehicle parameters of the target vehicle.

[0053] In this embodiment, the road surface to be detected is a pre-set area that needs to be evaluated for target hazards. Exemplarily, it may include two parts: a far-field area and a near-field area. The length of the near-field area and the length of the road surface corresponding to the far-field area can be flexibly set according to the actual scene. For example, if the length of the near-field area is 20 meters and the length of the far-field area is 30 meters, then the range of the road surface to be detected is: 50 meters × 20 meters. Three-dimensional shape information refers to the three-dimensional shape of the road surface to be detected, including but not limited to the length, width, and road surface flatness. The target vehicle can be any vehicle with an automatic driving function, and is not limited to automatic driving vehicles or unmanned vehicles. Vehicle parameters include but are not limited to vehicle chassis height, vehicle length, and vehicle movement speed. According to the length and vehicle movement speed in the three-dimensional shape information of the road surface to be detected, the time required for the vehicle to pass through a certain area can be obtained. For example, the range of the evaluation area is: 50 meters × 20 meters. Then the time required for the vehicle to pass through the evaluation area .

[0054] In order to facilitate subsequent data processing, the present invention also defines the coordinate system involved accordingly, such as Figure 2 As shown in the figure, the vehicle includes a far-field camera, a near-field camera, a lidar, an odometer, and an inertial measurement unit. For the convenience of drawing, the far-field camera and the near-field camera are not distinguished, and the odometer and inertial measurement unit are not drawn. Figure 2 middle, is the camera coordinate system, Along the camera optical axis. is the standard camera coordinate system, which is Along Rotation, where Vertically downward. is the laser radar coordinate system, is the local world coordinate system, which is defined on the road surface, where Vertically upward, For horizontal forward, To the left horizontally, the vehicle coordinate system can usually adopt the lidar coordinate system, and the local world coordinate system is the coordinate system of the road surface on which the vehicle is traveling.

[0055] S102: Determine a target object contained in the road surface to be detected based on the three-dimensional shape information, and obtain three-dimensional shape data of the target object.

[0056] When the three-dimensional shape information of the road to be detected is obtained in the previous step, by analyzing the three-dimensional shape information, it can be determined whether the road to be detected contains a target object with a small or relatively small height in the direction perpendicular to the road surface. The target object in this embodiment is a low raised obstacle or pothole area on the road. The low raised obstacles on the road are speed bumps and raised cracks. The pothole area includes deep pits, shallow pits or puddles. When it is determined that there is a target object, the three-dimensional shape data of the target object is obtained from the corresponding position. If the target object is simply described using a rectangular frame, the description is not comprehensive enough. This embodiment uses multiple dimensions to describe each target object, such as Figure 3 As shown, the three-dimensional shape data of the target object i includes at least the length ,width , Maximum elevation , mean elevation , elevation mean square error ,area Angle between lane line .

[0057] S103: Determine the hazard status of the target object to the target vehicle and the hazard status of the driving state of the target vehicle according to the three-dimensional shape data of the target object and the vehicle parameters, and obtain a target object hazard assessment result.

[0058] After the accurate three-dimensional shape data of the target object is obtained in the previous step, the hazard of the target object can be evaluated through the three-dimensional shape data and vehicle parameters. The hazards include but are not limited to hazards to the vehicle itself, such as chassis scratch hazards, and hazards that affect the normal and safe driving of the vehicle, such as hazard assessment of falling into potholes, bump hazards, and area share hazards. Of course, those skilled in the art can flexibly select one or more hazards for evaluation according to actual conditions, or other types of hazards for evaluation, which will not affect the implementation of the present invention. According to different hazard types, corresponding data is selected from the three-dimensional shape data and vehicle parameters of the target object for evaluation to obtain the hazard assessment result of the target object. If there are multiple targets on the road to be detected, it is necessary to evaluate the hazard of each target object to the target vehicle and the hazard of the driving state, and generate the final target hazard assessment result according to the corresponding hazard assessment results.

[0059] S104: Generate a vehicle driving route of the target vehicle based on the target object hazard assessment results corresponding to each road bulge obstacle and / or pothole area included in the road surface to be detected.

[0060] After the target object hazard result is determined in the previous step, generally speaking, the road surface to be inspected contains multiple targets. Based on the elevation, direction and other information of a single low raised obstacle / pothole area, a comprehensive assessment is made on the spatial distribution of all targets on the road surface to be inspected. The autonomous driving route is planned according to the overall hazard assessment result to provide more accurate data for obstacle avoidance, which is conducive to improving the safety of vehicle tasks in roadside perception scenarios, smart traffic scenarios, V2X (vehicle to everything, that is, vehicle-to-external information exchange) scenarios, smart security scenarios, and collaborative perception scenarios, ensuring efficient and safe passage of vehicles.

[0061] In the technical solution provided in this embodiment, when automatically generating a vehicle driving route for a target vehicle, accurate three-dimensional morphology information is first obtained, and based on the accurate three-dimensional morphology information, it is identified whether there are road obstacles or potholes on the road ahead, and the hazards of the road obstacles or potholes to the vehicle itself and the operation of the vehicle are analyzed. The analysis results can reflect whether it is safe for the vehicle to drive on the road surface and the comfort of the user. According to the analysis results, a vehicle driving route with both high comfort and high safety can be generated. Since the vehicle driving route takes into account the safety of the vehicle and the comfort of the user, when the target vehicle drives along the route, the safety and comfort of the self-driving vehicle are effectively improved.

[0062] In the above embodiment, there is no limitation on the hazard situation of the target object to the target vehicle and the hazard situation of the driving state. Based on the above embodiment, the present invention also provides hazard situation assessments corresponding to multiple hazard types, each hazard situation is independent of each other, and may include the following contents:

[0063] Exemplarily, the hazard of the target object to the target vehicle may be chassis scratches, and the chassis scratch hazard assessment process may be: determining the probability of the target object scratching the target vehicle based on the maximum elevation of the target object and the vehicle chassis height; determining the chassis scratch hazard of the target object to the target vehicle based on the chassis scratch probability and vehicle parameters.

[0064] The maximum elevation can be obtained from the three-dimensional shape data of the target object, and the chassis scratch probability is used to evaluate the probability of chassis scratches, which can be calculated based on the maximum elevation of the target object. and vehicle chassis height If the maximum elevation of the target object is greater than or equal to the chassis height of the vehicle, the chassis of the target vehicle will inevitably be scratched. If the maximum elevation of the target object is less than the chassis height of the vehicle, the chassis scratch probability of the target vehicle can be determined according to the ratio between the maximum elevation and the chassis height of the vehicle. The following relationship (1) can be used to be directly called to calculate the chassis scratch probability of the target object on the target vehicle . After determining the chassis scratch probability of the target object on the target vehicle, the chassis scratch hazard can be determined based on the chassis scratch probability and the maximum scratch time of the target vehicle. Exemplarily, the vehicle length and vehicle movement speed are obtained from the vehicle parameters, and the maximum scratch time of the target vehicle is determined based on the vehicle length and the vehicle movement speed; the chassis scratch hazard value of the target object on the target vehicle is obtained based on the product of the chassis scratch probability and the maximum scratch time. As an efficient method for determining the chassis scratch hazard, the following relationship (2) can be directly called to calculate the chassis scratch hazard value of the target object on the target vehicle.

[0065] Among them, the probability of chassis scratching of the i-th target object is The relationship can be expressed as:

[0066] . (1)

[0067] The maximum scratch hazard of this embodiment for The product of the maximum scratch time and the maximum scratch length is the vehicle length , maximum scratch time Therefore, the calculation formula of chassis scratch hazard value can be expressed as:

[0068] . (2)

[0069] Exemplarily, the hazard posed by the target object to the target vehicle may be a entrapment hazard, and the entrapment hazard assessment process may be: determining the entrapment probability of the target vehicle passing through the pothole area based on the angle between the target vehicle's driving direction and the pothole area, and the tire width in the vehicle parameters; determining the entrapment hazard of the target vehicle based on the three-dimensional morphology data of the pothole area, vehicle parameters, entrapment probability and time passing through the entrapment area.

[0070] In this embodiment, there may be an angle between the vehicle's driving direction and the pothole area. In order to accurately determine whether the vehicle is trapped in the target object, the line segment AB can be defined, such as Figure 4 As shown in FIG. 1 , line segment AB is the current tire-ground contact point position, and the projection length of the pothole area in the tire width direction. Correspondingly, the hazard of being trapped is the numerical relationship between the length of line segment AB and the width of the vehicle tire. For example, the calculation process of the probability of being trapped can be: according to the width of the pothole area , the angle between the horizontal plane of the pothole area and the lane line , determine the projection length of the pothole area in the tire width direction. For example, the ratio between the two is the projection length. If the target vehicle is smaller than or equal to the projection length, the target vehicle is trapped in the pothole area; if the tire width of the target vehicle is larger than the projection length, the probability of being trapped is determined according to the ratio of the projection length to the tire width. As an efficient way to determine the probability of being trapped, the following relationship (3) can be directly used to calculate the probability of being trapped :

[0071] . (3)

[0072] When the probability of being trapped is determined, the hazard value of being trapped can be determined based on the probability of being trapped, the degree of being trapped, and the time of being trapped in the area. Among them, the degree of being trapped can be determined based on the numerical relationship between the average elevation of the target object and the height of the vehicle chassis. If the target object's elevation is greater than or equal to the vehicle chassis height, it must be trapped. Accordingly, the trapping hazard value can be set to the maximum value, such as 1. If the target object's elevation mean is less than the vehicle chassis height, the trapping hazard degree is determined based on the ratio of the target object's elevation mean to the vehicle chassis height. As an efficient way to determine the trapping hazard degree, the following relationship (4) can be directly used to calculate the target vehicle's trapping hazard degree: For example, the target vehicle's hazard level is determined based on the average elevation of the pothole area and the vehicle chassis height in the vehicle parameters; the target vehicle's hazard level is determined based on the angle between the target vehicle's driving direction and the pothole area. , length of the pothole area Determine the target vehicle's trapped length. The trapped length can be The vehicle speed according to the length of the trap and the vehicle parameters , determine the target vehicle's entrapment time The entrapment time can be expressed as According to the probability of entrapment, the degree of entrapment and the time of entrapment, the entrapment hazard value of the target vehicle is determined. As an efficient method for determining the entrapment hazard value, the following relationship (5) can be directly used to calculate the entrapment hazard value of the target vehicle: :

[0073] ; (4)

[0074] . (5)

[0075] For example, the hazard of the target object to the target vehicle may be a bump hazard, and the bump hazard assessment process may be as follows: according to the angle between the driving direction of the target vehicle and the pothole area and the length of the target object, the length of the bump area of ​​the target vehicle is determined; according to the length of the bump area and the vehicle running speed of the target vehicle, the bump area time is determined; according to the mean square error of the elevation in the three-dimensional morphology data of the target object, the bump area length is determined; and bumpy area time to determine the bumpy hazard value of the target vehicle during driving.

[0076] In this embodiment, when the vehicle is driving along a straight line on the road to be tested, the bumps experienced by the vehicle are based on the driving time of the vehicle in the low obstacle / pothole area and the degree of ups and downs of the low raised obstacle / pothole area itself. In this embodiment, the bump hazard can be determined based on the bump degree and the time it takes to pass through the bump area. For example, the bump hazard is defined as the product of the bump degree and the time it takes to pass through the bump area, where the length of the bump area can be expressed as , time to pass through the bumpy area As an efficient way to determine the bump hazard value, the following equation (6) can be directly used to calculate the bump hazard value of the target vehicle: :

[0077] . (6)

[0078] Exemplarily, in order to accurately assess the hazard of the target object to the target vehicle, the present embodiment further combines the proportion of the target object in the entire road surface area for hazard assessment, which may include the following contents: dividing the road surface to be inspected into multiple areas to be analyzed according to the number of lanes and lane widths, and determining the area ratio hazard level value according to the size of the area to be analyzed, the number of lanes occupied by the target object and the three-dimensional morphology data.

[0079] In this embodiment, the area ratio hazard level is used to evaluate the area of ​​a low raised obstacle / pothole area in the road to be detected, that is, the area of ​​the target object i. The ratio of the area of ​​the entire road surface to be detected to the area where the road surface is located is harmful to the vehicle. For the convenience of statistics, the road driving area can be divided along the road direction. For the convenience of processing and improving the evaluation efficiency, the road surface to be detected can be divided at equal distances. Exemplarily, the road surface to be detected is divided into multiple areas to be analyzed at equal distances along the road direction according to the product of the preset area length, the number of lanes and the lane width as the area width; the road area area is determined according to the number of lanes occupied by the target object, the preset area length and the lane width; the area ratio hazard degree value is determined according to the ratio of the obstacle area area and the road area area in the three-dimensional morphology data of the target object. For example, the width of the road surface to be detected is: , is the number of lanes, is the lane width and the length is , such as 2 meters. As an efficient way to determine the area ratio hazard level, the following relationship (7) can be directly used to calculate the area ratio hazard level of the target vehicle: :

[0080] ; (7)

[0081] Where m is the number of lanes occupied by the target object, such as Figure 5 As shown, the value of m is 2.

[0082] From the above, it can be seen that this embodiment quantifies various hazards by respectively calculating the chassis scratch value, the entrapment hazard value, the bump hazard value and the proportion hazard value, and more clearly and accurately evaluates the hazard of the target object, which is conducive to generating a safer and high-capacity vehicle driving route.

[0083] The above embodiment is to perform a quantitative assessment on the hazard situation of a single target object. This embodiment further determines the hazard level of a single target object based on the above quantitative assessment value, which may include the following contents:

[0084] Obtain the chassis scratch hazard value, entrapment hazard value, bump hazard value and area ratio hazard value of the target object; determine the single obstacle vehicle hazard value based on the chassis scratch hazard value, entrapment hazard value and bump hazard value of the target object; determine the target object hazard assessment result based on the numerical relationship between the product of the single obstacle vehicle hazard value and the area ratio hazard value and a first preset hazard threshold.

[0085] In this embodiment, the first preset hazard threshold is a preset empirical value, and its numerical relationship with the target object hazard assessment value is used to determine the hazard level of a single target object. The target object hazard assessment value is determined based on the chassis scratch hazard value, the sinking hazard value, the bump hazard value, and the area ratio hazard level value of the target object. As an efficient method for determining the target object hazard assessment value, the following relationship (8) can be directly called to calculate the target object hazard assessment value of the target vehicle:

[0086] . (8)

[0087] Single target The value range is , taking the first preset hazard threshold of 0.5 as an example, the following relationship shows that when the target object hazard assessment value is greater than the first preset hazard threshold, the hazard level is high hazard; when the target object hazard assessment value is less than or equal to the first preset hazard threshold, the hazard level is low hazard:

[0088] . (9)

[0089] Of course, if the degree of harm to the target object is to be distinguished more finely, multiple harm thresholds can be set, and the harm degree corresponding to each harm threshold interval can be defined. For example, the first preset harm threshold is less than the third preset harm threshold. If it is greater than the third preset harm threshold, it is considered to be the maximum harm. If it is between the third preset harm threshold and the first preset harm threshold, it is considered to be medium harm. If it is less than the first preset harm threshold, it is considered to be low harm. After determining the harm degree of the target object, the target harm assessment result can be jointly generated based on the target harm assessment value and the target harm degree of the target object, so as to more clearly and accurately assess the harm of the target object.

[0090] The above-mentioned embodiment is to quantitatively evaluate the hazard situation of a single target object. When there are multiple target objects on the road surface to be detected, the process of generating the final target object evaluation result based on the hazard situation of the multiple target objects can be: according to the target object hazard evaluation results corresponding to each road bulge obstacle and each pothole area contained in the road surface to be detected, the target object hazard average value of the road surface to be detected is calculated, and the hazard average value of each type of hazard is counted; according to the numerical relationship between the target object hazard average value and the second preset hazard threshold, the target object hazard degree assessment result is determined; the maximum value is selected from each hazard mean value, and the hazard type corresponding to the maximum value is used as the target object hazard category of the road surface to be detected; according to the target object hazard degree assessment result and the target object hazard category, the obstacle avoidance area of ​​the road surface to be detected is determined, and the vehicle driving route of the target vehicle is generated based on the obstacle avoidance area.

[0091] In this embodiment, if Figure 6 As shown, the road surface to be inspected includes multiple targets, such as cracks, shallow pits, deep pits, and low obstacles. When the target hazard assessment value of each target object is obtained, the following relationship (10) can be directly called to calculate the average target hazard value of the entire road surface to be inspected:

[0092] . (10)

[0093] In this embodiment, the second preset hazard threshold is a preset empirical value, and its numerical relationship with the target object hazard assessment value is used to determine the hazard level of all targets. The value range is , accordingly, all the targets The value range is Taking the second preset hazard threshold of 0.5 as an example, the following relationship (11) shows that when the target object hazard average value is greater than the second preset hazard threshold, the hazard level is high hazard; when the target object hazard average value is less than or equal to the first preset hazard threshold, the hazard level is low hazard:

[0094] . (11)

[0095] Of course, if you want to distinguish the degree of harm to the target object more carefully, you can also set multiple harm thresholds and define the degree of harm corresponding to each harm threshold interval. For example, if the second preset harm threshold is less than the fourth preset harm threshold, if it is greater than the fourth preset harm threshold, it is considered to be the maximum harm. If it is between the fourth preset harm threshold and the second preset harm threshold, it is considered to be a medium harm. If it is less than the second preset harm threshold, it is considered to be a low harm. After determining the degree of harm to the target object, the main harm types can also be evaluated. For this area, the average values ​​of scratch harm, entrapment harm, area ratio harm, and bump harm are calculated to obtain the mean value of scratch harm. , falling into the danger mean , area proportion hazard average , mean value of turbulence hazard , and then take the hazard category corresponding to the maximum value of the four hazard means as the main hazard category of the area, that is, the hazard category of the target object , as shown in the following formula (12):

[0096] . (12)

[0097] Finally, according to the target object hazard level assessment results and target object hazard category, the obstacle avoidance area of ​​the road to be inspected can be determined more accurately, and a safer and high-capacity vehicle driving route for the target vehicle can be generated based on the obstacle avoidance area.

[0098] The above embodiment does not impose any limitation on how to obtain high-precision three-dimensional shape data of the target object. Based on this, this embodiment also provides a method for obtaining high-precision three-dimensional shape data of the target object, which may include the following contents:

[0099] A1: Obtain dense point cloud data of the road surface in the target area in front of the vehicle, and divide the dense point cloud data of the road surface into near-field dense point cloud data and far-field dense point cloud data according to the distance between the point cloud data and the vehicle.

[0100] Exemplarily, the vehicle motion data and vehicle motion posture data at the current moment are obtained as pose point cloud data, and the initial pose change information is determined based on the pose point cloud data and the spliced ​​point cloud data at the previous moment before the current moment; the initial pose information is used as the initial value of the iterative calculation, and the point cloud matching calculation is performed on the point cloud data at the current moment and the spliced ​​point cloud data at the previous moment to determine the rotation change matrix and the translation vector; the spliced ​​point cloud data at the previous moment is transformed according to the rotation change matrix and the translation vector, and the point cloud data that is not the position of the vehicle at the current moment in the transformed spliced ​​point cloud data is removed to serve as the dense point cloud data at the previous moment; the dense point cloud data at the previous moment is spliced ​​with the point cloud data at the current moment to obtain the dense point cloud data at the current moment; the image information of the driving area is obtained by performing semantic segmentation processing on the acquired driving scene image data; based on the image information of the driving area, the non-road data in the dense point cloud data at the current moment is eliminated according to the correspondence between the image pixels and the three-dimensional points of the point cloud, and the dense point cloud data of the road surface corresponding to the road surface in the target area is obtained.

[0101] A2: The first target sub-region corresponding to the far-field dense point cloud data is rasterized according to the first preset grid size, and the first target sub-region is segmented according to the clustering result of the average normal vector in each grid of the first target sub-region to obtain multiple types of first road surface regions; for each first road surface region, the current road surface region is rasterized according to the second preset grid size; the three-dimensional space plane equation of the corresponding grid is fitted according to the three-dimensional position information of the point cloud data inside each grid of the current first road surface region; based on the three-dimensional space plane equation of each grid of the current first road surface region, the average normal vector in each grid of the current first road surface region is calculated; according to the direction of the average normal vector, at intervals of a preset angle threshold, each average normal vector is voted on whether it is a road surface normal vector, and the target average normal vector with the most votes is used as the road surface normal vector, and the grid corresponding to the target average normal vector is used as the road surface flat area. Based on the spatial distance relationship, the remaining area of ​​the current first road surface region after eliminating the road surface flat area is merged as the spatial position of the suspected obstacle.

[0102] Among them, the target area includes a first target sub-area and a second target sub-area, the maximum distance between the second target sub-area and the vehicle along the driving direction of the vehicle is smaller than the minimum distance between the first target sub-area and the vehicle, the point cloud data corresponding to the first target sub-area is far-field dense point cloud data, the point cloud data corresponding to the second target sub-area is near-field dense point cloud data, and the second preset grid size is smaller than the first preset grid size.

[0103] A3: Verify whether the suspected obstacle is the target object based on the near-field dense point cloud data, and when the suspected obstacle is verified to be the target object, determine the three-dimensional shape information of the target object based on the point cloud data at adjacent moments and their matching results.

[0104] Exemplarily, the second target sub-region corresponding to the near-field dense point cloud data is rasterized according to the third preset grid size; the second target sub-region is divided into a plurality of second road surface regions according to the clustering result of the average normal vector of each grid of the second target sub-region; the corresponding point cloud data is corrected according to the inclination angle of each second road surface region, and the point cloud data of the flat road surface region in each second road surface region after the correction is eliminated according to the installation height of the laser radar; the remaining point cloud data of each second road surface region after the flat road surface region point cloud data is eliminated is spliced ​​according to the corresponding spatial position relationship to obtain the near-field non-flat road surface dense point cloud. According to the point cloud matching result of the point cloud data at the current moment and the dense point cloud data at the previous moment, the rotation matrix and the translation vector are obtained, and the spatial position information of the suspected obstacle in the vehicle coordinate is updated according to the rotation matrix and the translation vector; based on the spatial position information, the obstacle dense point cloud data of the suspected obstacle is obtained from the near-field non-flat road surface dense point cloud; by performing target recognition on the obstacle dense point cloud data, it is determined whether the suspected obstacle is a target object.

[0105] Perform three-dimensional shape fitting processing on the dense point cloud data of the obstacle of the target object to obtain the initial three-dimensional shape information of the target object, and remove the dense point cloud data of the obstacle from the near-field uneven road surface dense point cloud to serve as the dense point cloud data of the previous moment; obtain the point cloud data of the current moment when the vehicle continues to travel, and splice the point cloud data of the current moment with the dense point cloud data of the previous moment to obtain the dense point cloud data of the current moment; based on the new spatial position information of the target object in the vehicle coordinate system at the current moment, determine the new obstacle dense point cloud data of the target object from the dense point cloud data at the current moment, perform three-dimensional shape fitting on the new obstacle dense point cloud data, and obtain the three-dimensional shape information of the target object at the current moment to update the initial three-dimensional shape information.

[0106] As can be seen from the above, this embodiment, by acquiring high-precision three-dimensional shape data, is conducive to more accurately evaluating the hazard situation of each target object and is conducive to generating a safer and high-capacity vehicle driving route.

[0107] It should be noted that there is no strict order of execution between the steps in the present invention. As long as they comply with the logical order, these steps can be executed simultaneously or in a certain preset order. Figure 1 -This is just an illustration and does not mean that this is the only execution order.

[0108] The present invention also provides a corresponding device for the vehicle driving route generation method, which further makes the method more practical. Among them, the device can be described from the perspective of functional modules and hardware. The vehicle driving route generation device provided by the present invention is introduced below. The device is used to implement the vehicle driving route generation method provided by the present invention. In this embodiment, the vehicle driving route generation device may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to complete the vehicle driving route generation method disclosed in Example 1. The program module referred to in this embodiment refers to a series of computer program instruction segments that can complete specific functions, which is more suitable for describing the execution process of the vehicle driving route generation device in the storage medium than the program itself. The following description will specifically introduce the functions of each program module of this embodiment. The vehicle driving route generation device described below and the vehicle driving route generation method described above can be referenced to each other.

[0109] From the perspective of functional modules, see Figure 7 , Figure 7 The structure diagram of the vehicle driving route generating device provided in this embodiment under a specific implementation manner, the device may include:

[0110] The data acquisition module 701 is used to acquire the three-dimensional shape information of the road surface to be detected and the vehicle parameters of the target vehicle.

[0111] The target recognition module 702 is used to determine the target object contained in the road surface to be detected based on the three-dimensional shape information and obtain the three-dimensional shape data of the target object; the target object is a raised obstacle or a pothole area on the road.

[0112] The hazard assessment module 703 is used to determine the hazard status of the target object to the target vehicle and the hazard status of the driving state according to the three-dimensional shape data of the target object and the vehicle parameters, and obtain the target object hazard assessment result.

[0113] The route planning module 704 is used to generate a vehicle driving route of the target vehicle based on the target object hazard assessment results corresponding to each road bulge obstacle and / or pothole area contained in the road surface to be detected.

[0114] Exemplarily, in some implementations of this embodiment, the hazard assessment module 703 may also be used to: determine the probability of the target object scratching the chassis of the target vehicle based on the maximum elevation of the target object and the chassis height of the vehicle; determine the hazard of the target object scratching the chassis of the target vehicle based on the chassis scratch probability and vehicle parameters.

[0115] As an exemplary implementation method of the above embodiment, the above hazard assessment module 703 can be further used to: determine the maximum scratch time of the target vehicle according to the vehicle length and vehicle movement speed in the vehicle parameters; and obtain the chassis scratch hazard value of the target object to the target vehicle according to the product of the chassis scratch probability and the maximum scratch time.

[0116] Illustratively, in some other implementations of the present embodiment, the hazard assessment module 703 may also be used to: determine the probability of the target vehicle being trapped in a pothole area based on the angle between the target vehicle's driving direction and the pothole area and the tire width in the vehicle parameters; determine the hazard of the target vehicle being trapped based on the three-dimensional morphological data of the pothole area, vehicle parameters, the probability of being trapped and the time of passing through the trapped area.

[0117] As an exemplary implementation of the above embodiment, the hazard assessment module 703 can be further used to: determine the projection length of the pothole area in the tire width direction according to the width of the pothole area and the angle between the horizontal plane of the pothole area and the lane line; if the tire width of the target vehicle is less than or equal to the projection length, the target vehicle falls into the pothole area; if the tire width of the target vehicle is greater than the projection length, the probability of falling into the pothole is determined according to the ratio of the projection length to the tire width.

[0118] As another exemplary implementation of the above embodiment, the above hazard assessment module 703 can be further used to: determine the target vehicle's entrapment hazard level value based on the average elevation of the pothole area and the vehicle chassis height in the vehicle parameters; determine the target vehicle's entrapment length based on the angle between the target vehicle's driving direction and the pothole area and the length of the pothole area; determine the target vehicle's entrapment time based on the entrapment length and the vehicle running speed in the vehicle parameters; determine the target vehicle's entrapment hazard value based on the entrapment probability, the entrapment hazard level value and the entrapment time.

[0119] Illustratively, in some other implementations of the present embodiment, the hazard assessment module 703 may also be used to: determine the length of the bumpy area of ​​the target vehicle based on the angle between the driving direction of the target vehicle and the pothole area and the length of the target object; determine the bumpy area time based on the bumpy area length and the vehicle running speed of the target vehicle; determine the bumpy hazard value of the target vehicle during driving based on the elevation mean square error and the bumpy area time in the three-dimensional morphology data of the target object.

[0120] Exemplarily, in some other implementations of this embodiment, the hazard assessment module 703 can also be used to divide the road surface to be inspected into multiple areas to be analyzed according to the number of lanes and lane widths, and determine the area ratio hazard level value according to the size of the area to be analyzed, the number of lanes occupied by the target object and the three-dimensional morphology data.

[0121] As another exemplary implementation of the above embodiment, the above hazard assessment module 703 can be further used to: divide the road surface to be detected into multiple areas to be analyzed at equal distances along the road direction according to the product of the preset area length, the number of lanes and the lane width as the area width; determine the road area area according to the number of lanes occupied by the target object, the preset area length and the lane width; determine the area ratio hazard degree value according to the ratio of the obstacle area area and the road area area in the three-dimensional morphology data of the target object.

[0122] Illustratively, in some other implementations of the present embodiment, the hazard assessment module 703 may also be used to: obtain the chassis scratch hazard value, entrapment hazard value, bump hazard value and area ratio hazard value of the target object; determine the single obstacle vehicle hazard value based on the chassis scratch hazard value, entrapment hazard value and bump hazard value of the target object; determine the target object hazard assessment result based on the numerical relationship between the product of the single obstacle vehicle hazard value and the area ratio hazard value and a first preset hazard threshold.

[0123] Illustratively, in some other implementations of the present embodiment, the route planning module 704 may also be used to: calculate the target object hazard average value of the road surface to be detected, and count the hazard average values ​​of each type of hazard, based on the target object hazard assessment results corresponding to each road obstacle and each pothole area contained in the road surface to be detected; determine the target object hazard degree assessment result based on the numerical relationship between the target object hazard average value and a second preset hazard threshold; select the maximum value from each hazard mean value, and use the hazard type corresponding to the maximum value as the target object hazard category of the road surface to be detected; determine the obstacle avoidance area of ​​the road surface to be detected based on the target object hazard degree assessment result and the target object hazard category, and generate a vehicle driving route of the target vehicle based on the obstacle avoidance area.

[0124] The vehicle driving route generating device mentioned above is described from the perspective of functional modules. Furthermore, the present invention also provides an electronic device, which is described from the perspective of hardware. Figure 8 The following is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention in one implementation manner. Figure 8 As shown, the electronic device includes a memory 80 for storing a computer program; and a processor 81 for implementing the steps of the vehicle driving route generation method mentioned in any of the above embodiments when executing the computer program.

[0125] Among them, the processor 81 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the processor 81 may also be a controller, a microcontroller, a microprocessor or other data processing chip. The processor 81 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 81 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 81 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 81 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0126] The memory 80 may include one or more computer non-volatile storage media, which may be non-transitory. The memory 80 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash memory storage devices. In some embodiments, the memory 80 may be an internal storage unit of an electronic device, such as a hard disk of a server. In other embodiments, the memory 80 may also be an external storage device of an electronic device, such as a plug-in hard disk equipped on a server, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 80 may also include both an internal storage unit of an electronic device and an external storage device. The memory 80 may not only be used to store application software and various types of data installed in the electronic device, such as: the code of the program in the process of executing the vehicle driving route generation method, but also be used to temporarily store data that has been output or is to be output. In this embodiment, the memory 80 is at least used to store the following computer program 801, wherein, after the computer program is loaded and executed by the processor 81, the relevant steps of the vehicle driving route generation method disclosed in any of the aforementioned embodiments can be implemented. In addition, the resources stored in the memory 80 may also include an operating system 802 and data 803, etc., and the storage method may be temporary storage or permanent storage. The operating system 802 may include Windows, Unix, Linux, etc. The data 803 may include but is not limited to data corresponding to the vehicle driving route generation result, etc.

[0127] In some embodiments, the electronic device may further include a display screen 82, an input / output interface 83, a communication interface 84 or a network interface, a power supply 85 and a communication bus 86. Among them, the display screen 82 and the input / output interface 83 such as a keyboard belong to the user interface, and exemplary user interfaces may also include standard wired interfaces, wireless interfaces, etc. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface. The communication interface 84 may exemplarily include a wired interface and / or a wireless interface, such as a WI-FI interface, a Bluetooth interface, etc., which are generally used to establish a communication connection between the electronic device and other electronic devices. The communication bus 86 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0128] Those skilled in the art will understand that Figure 8 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, for example, it may also include a sensor 87 to implement various functions.

[0129] It is understandable that if the vehicle driving route generation method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes but is not limited to: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), electrically erasable programmable ROM, register, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, removable disk, CD-ROM, magnetic disk or optical disk, etc. Various media that can store program codes. Based on this, the present invention also provides a non-volatile storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the vehicle driving route generation method recorded in any of the above embodiments are executed.

[0130] It is understandable that if the vehicle driving route generation method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, the computer software product does not need to be stored in a physical storage medium. For example, it can be directly transmitted to a computer or other device with information processing capabilities through a wired network or a wireless network to perform all or part of the steps of the methods of various embodiments of the present invention. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product. Based on this, the present invention also provides a computer program product that stores a computer program. When the computer program is executed by a processor, the steps of the vehicle driving route generation method recorded in any of the above embodiments are performed.

[0131] Finally, the present invention also provides an unmanned vehicle, which fully reuses its own sensors, such as cameras and lidars, and does not require the installation of other additional sensors. A computer program for implementing the vehicle driving route generation method as recorded in any of the previous embodiments is built into the existing automatic driving perception system. The automatic driving perception system can be used to implement the steps of the vehicle driving route generation method as recorded in any of the previous embodiments when executing the computer program stored in the memory, and accurately evaluate the degree of hazard of various target objects in front of the vehicle, such as speed bumps and raised cracks, deep pits, shallow pits or puddles, and the degree of hazard of potholes such as water pits, such as Fig. 9 As shown in the figure, planning the vehicle route based on the degree of hazard can effectively improve the safety and comfort of the self-driving system.

[0132] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the hardware disclosed in the embodiments, including electronic devices, non-volatile storage media, computer program products and unmanned vehicles, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0133] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0134] The above is a detailed introduction to a vehicle driving route generation method, electronic device, non-volatile storage medium, computer program product and unmanned vehicle provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that based on the embodiments of the present invention, for ordinary technicians in this technical field, all other embodiments obtained without creative work are within the scope of protection of the present invention. Without departing from the principles of the present invention, the present invention may also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A method for generating a vehicle driving route, characterized in that: include: Obtaining three-dimensional topographic information of the road surface to be detected and vehicle parameters of the target vehicle; Determine the target object contained in the road surface to be detected based on the three-dimensional shape information, and obtain the three-dimensional shape data of the target object; the target object is a raised obstacle or a pothole area on the road; Determine the hazard status of the target object to the target vehicle and the hazard status of the driving state according to the three-dimensional shape data of the target object and the vehicle parameters, and obtain a target object hazard assessment result; Generating a vehicle driving route of the target vehicle based on the target object hazard assessment results corresponding to each road bulge obstacle and / or pothole area included in the road surface to be detected; Wherein, determining the hazard of the target object to the target vehicle and the hazard of the driving state respectively according to the three-dimensional shape data of the target object and the vehicle parameters includes: Determine the length of the bumpy area of ​​the target vehicle according to the angle between the driving direction of the target vehicle and the pothole area and the length of the target object; determine the bumpy area time according to the bumpy area length and the vehicle running speed of the target vehicle; determine the bumpy hazard value of the target vehicle during driving according to the elevation mean square error in the three-dimensional shape data of the target object and the bumpy area time; According to the product of the preset area length, the number of lanes and the lane width as the area width, the road surface to be detected is divided into a plurality of areas to be analyzed at equal distances along the road direction; the road area is determined according to the number of lanes occupied by the target object, the preset area length and the lane width; the area ratio hazard degree value is determined according to the ratio of the obstacle area in the three-dimensional shape data of the target object to the road area; Wherein, determining the hazard situation of the target object to the target vehicle and the hazard situation of the driving state respectively according to the three-dimensional shape data of the target object and the vehicle parameters, and obtaining the target object hazard assessment result includes: Obtaining a chassis scratch hazard value, a sinking hazard value, a bumping hazard value, and an area ratio hazard value of the target object; determining a single obstacle vehicle hazard value according to the chassis scratch hazard value, the sinking hazard value, and the bumping hazard value of the target object; determining a target object hazard assessment result according to a numerical relationship between the product of the single obstacle vehicle hazard value and the area ratio hazard value and a first preset hazard threshold; Wherein, the generating of the vehicle driving route of the target vehicle based on the target object hazard assessment result corresponding to each road bulge obstacle and / or pothole area contained in the road surface to be detected includes: According to the target object hazard assessment results corresponding to each road bulge obstacle and each pothole area contained in the road surface to be detected, the target object hazard average value of the road surface to be detected is calculated, and the hazard average value of each type of hazard is counted; according to the numerical relationship between the target object hazard average value and a second preset hazard threshold, the target object hazard degree assessment result is determined; the maximum value is selected from each hazard mean value, and the hazard type corresponding to the maximum value is used as the target object hazard category of the road surface to be detected; according to the target object hazard degree assessment result and the target object hazard category, the obstacle avoidance area of ​​the road surface to be detected is determined, and the vehicle driving route of the target vehicle is generated based on the obstacle avoidance area.

2. The vehicle driving route generation method according to claim 1, characterized in that: Determining the harm of the target object to the target vehicle according to the three-dimensional shape data of the target object and the vehicle parameters, including: Determining the probability of the target object scratching the chassis of the target vehicle according to the maximum elevation of the target object and the chassis height of the vehicle; The chassis scratch hazard of the target object to the target vehicle is determined based on the chassis scratch probability and the vehicle parameters.

3. The vehicle driving route generation method according to claim 2, characterized in that: Determining the chassis scratch hazard of the target object to the target vehicle according to the chassis scratch probability and the vehicle parameter, including: Determining the maximum scratch time of the target vehicle according to the vehicle length and the vehicle movement speed in the vehicle parameters; The chassis scratch hazard value of the target object on the target vehicle is obtained according to the product of the chassis scratch probability and the maximum scratch time.

4. The vehicle driving route generation method according to claim 1, characterized in that: The target object is a pothole area, and the hazard conditions of the target object to the target vehicle and the hazard conditions of the driving state are determined according to the three-dimensional shape data of the target object and the vehicle parameters, including: Determining the probability of the target vehicle falling into the pothole area according to the angle between the driving direction of the target vehicle and the pothole area and the tire width in the vehicle parameters; The entrapment hazard of the target vehicle is determined based on the three-dimensional topographic data of the pothole area, the vehicle parameters, the entrapment probability and the time of passing through the entrapment area.

5. The vehicle driving route generation method according to claim 4, characterized in that: Determining the probability of the target vehicle falling into the pothole area according to the angle between the driving direction of the target vehicle and the pothole area and the tire width in the vehicle parameters includes: Determining the projection length of the pothole area in the tire width direction according to the width of the pothole area and the angle between the horizontal plane of the pothole area and the lane line; If the tire width of the target vehicle is less than or equal to the projection length, the target vehicle falls into the pothole area; if the tire width of the target vehicle is greater than the projection length, the probability of falling into the pothole is determined according to the ratio of the projection length to the tire width.

6. The vehicle driving route generation method according to claim 4, characterized in that: Determining the entrapment hazard of the target vehicle according to the three-dimensional topographic data of the pothole area, the vehicle parameters, the entrapment probability and the time of passing through the entrapment area includes: Determining the degree of hazard of the target vehicle according to the average elevation of the pothole area and the vehicle chassis height in the vehicle parameters; Determining the trapped length of the target vehicle according to the angle between the driving direction of the target vehicle and the pothole area and the length of the pothole area; Determining the target vehicle's entrapment time according to the entrapment length and the vehicle running speed in the vehicle parameters; The entrapment hazard value of the target vehicle is determined according to the entrapment probability, the entrapment hazard level value and the entrapment time.

7. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to implement the vehicle driving route generation method as claimed in any one of claims 1 to 6 when executing the computer program stored in the memory.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a computer program, and when the computer program is executed by a processor, the vehicle driving route generation method according to any one of claims 1 to 6 is implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the vehicle driving route generation method described in any one of claims 1 to 6 is implemented.

10. An unmanned vehicle, characterized in that: It includes an autonomous driving perception system, which is used to implement the vehicle driving route generation method as described in any one of claims 1 to 6 when executing a computer program stored in a memory.

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