CN113424121A - Vehicle speed control method and device based on automatic driving and computer equipment - Google Patents
Vehicle speed control method and device based on automatic driving and computer equipment Download PDFInfo
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Abstract
An autonomous driving based vehicle speed control method, apparatus, computer device and storage medium, comprising: collecting point cloud data during the driving of the vehicle (100); recognizing obstacles in the driving environment by using the point cloud data; acquiring positioning information of a vehicle (100), and acquiring an electronic map according to the positioning information; identifying a driving area where the vehicle (100) is located according to the positioning information and the electronic map; projecting the point cloud data corresponding to the obstacle to an electronic map, and identifying the area where the obstacle is located; calculating a distance between the obstacle and the vehicle (100) when the obstacle and the vehicle (100) are in the same driving area; and acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle (100) to decelerate according to the deceleration strategy. Therefore, the vehicle (100) can be effectively controlled to decelerate in the automatic driving process, and the collision between the vehicle and the obstacle is avoided.
Description
The present application relates to an autonomous driving based vehicle speed control, apparatus, computer device and storage medium.
With the development of artificial intelligence technology, the automatic driving technology is rapidly developed. In the automatic driving, it is necessary to recognize obstacles and a traveling path around the vehicle and control the automatic traveling of the vehicle. The vehicle is inevitably subjected to abnormal conditions in the automatic driving process, for example, other vehicles suddenly appear in front of the vehicle or pedestrians need to stop in time, otherwise, collision can be caused to cause traffic accidents. Under the circumstance, how to effectively control the vehicle to avoid collision becomes a technical problem to be solved at present.
Disclosure of Invention
According to various embodiments disclosed herein, an autonomous driving based vehicle speed control, apparatus, computer device, and storage medium are provided.
An autonomous driving based vehicle speed control method comprising:
collecting point cloud data in the driving process of a vehicle;
identifying an obstacle in a driving environment using the point cloud data;
acquiring positioning information of a vehicle, and acquiring an electronic map according to the positioning information;
identifying a driving area where the vehicle is located according to the positioning information and the electronic map;
projecting the point cloud data corresponding to the obstacle to the electronic map, and identifying the area where the obstacle is located;
calculating a distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; and
and acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
An automatic driving-based vehicle speed control apparatus comprising:
the data acquisition module is used for acquiring point cloud data in the vehicle driving process;
a first identification module for identifying an obstacle in a driving environment using the point cloud data;
the positioning module is used for acquiring positioning information of the vehicle and acquiring an electronic map according to the positioning information; identifying a driving area where the vehicle is located according to the positioning information and the electronic map;
the second identification module is used for projecting the point cloud data corresponding to the obstacle to the electronic map and identifying the area where the obstacle is located;
the distance calculation module is used for calculating the distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; and
and the vehicle speed control module is used for acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance and controlling the vehicle to decelerate according to the deceleration strategy.
A computer device comprising a memory and one or more processors, the memory having stored therein computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform the steps of:
collecting point cloud data in the driving process of a vehicle;
identifying an obstacle in a driving environment using the point cloud data;
acquiring positioning information of a vehicle, and acquiring an electronic map according to the positioning information;
identifying a driving area where the vehicle is located according to the positioning information and the electronic map;
projecting the point cloud data corresponding to the obstacle to the electronic map, and identifying the area where the obstacle is located;
calculating a distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; and
and acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
One or more non-transitory computer-readable storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:
collecting point cloud data in the driving process of a vehicle;
identifying an obstacle in a driving environment using the point cloud data;
acquiring positioning information of a vehicle, and acquiring an electronic map according to the positioning information;
identifying a driving area where the vehicle is located according to the positioning information and the electronic map;
projecting the point cloud data corresponding to the obstacle to the electronic map, and identifying the area where the obstacle is located;
calculating a distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; and
and acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below. Other features and advantages of the application will be apparent from the description and drawings, and from the claims.
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a diagram of an application scenario of an autonomous driving based vehicle speed control method according to one or more embodiments.
FIG. 2 is a flow diagram of a method for autonomous vehicle speed control according to one or more embodiments.
FIG. 3 is a flow diagram illustrating steps for identifying obstacles in a driving environment using point cloud data in accordance with one or more embodiments.
Fig. 4 is a flowchart illustrating a step of projecting point cloud data corresponding to an obstacle onto an electronic map to identify an area where the obstacle is located according to one or more embodiments.
Fig. 5 is a flowchart of the obstacle travel area prediction step in another embodiment.
FIG. 6 is a block diagram of an autonomous driving based vehicle speed control apparatus according to one or more embodiments.
Fig. 7 is a block diagram of an automatic driving based vehicle speed control apparatus in another embodiment.
FIG. 8 is a block diagram of a computer device in accordance with one or more embodiments.
In order to make the technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. In one embodiment, the vehicle control method based on network monitoring provided by the application can be particularly applied to the field of automatic driving, for example, the vehicle control method based on network monitoring can be applied to the application environment shown in fig. 1. The vehicle 100 is pre-installed with sensors 102, a positioning device 104 and a computer device 106. Point cloud data in the autonomous driving environment is collected by the sensors 102. The positioning information of the vehicle 100 is collected in real time by the positioning device 104. The computer device 106 acquires the corresponding electronic map from the positioning information of the vehicle 100. The computer device 106 identifies the driving area where the vehicle 100 is located according to the positioning information and the electronic map. The computer device 106 identifies obstacles in the driving environment by using the point cloud data, projects the point cloud data corresponding to the obstacles to the electronic map, and identifies the area where the obstacles are located. When the obstacle is in the same driving area as the vehicle 100, the computer device 106 calculates the distance between the obstacle and the vehicle 100. And acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle 100 to decelerate according to the deceleration strategy. Therefore, the vehicle can be effectively controlled to decelerate in the automatic driving process, and the collision between the vehicle and the obstacle is avoided.
In one embodiment, as shown in fig. 2, there is provided an automatic driving-based vehicle speed control method, which is described by taking the method as an example applied to the computer device in fig. 1, and includes the steps of:
The vehicle is pre-installed with sensors and computer equipment. Environmental data in the visual range can be collected by the sensor. The sensor may be a lidar or an ultrasonic detector, for example, the lidar may emit a laser beam as a detection signal. The sensor compares the signal reflected by the object in the environment with the detection signal to obtain the surrounding environment data. The environmental data may specifically be point cloud data. The point cloud data refers to a set of point data corresponding to a plurality of points on the surface of an object, which is recorded in the form of points by an object in a scanning environment. The plurality may specifically mean two or more. The sensor can collect according to preset frequency to obtain multi-frame point cloud data.
And step 204, identifying obstacles in the driving environment by using the point cloud data.
The point cloud data may be three-dimensional point cloud data, and each frame of point cloud data may include point data corresponding to a plurality of points. The point cloud data includes at least one of three-dimensional coordinates, laser reflection intensity, color information, and the like. For example, objects in the driving environment may be represented in different colors. To accurately identify obstacles in a driving environment, a computer device needs to segment point cloud data, i.e., segment the point cloud data into a set of ground points and a set of non-ground points. Wherein the set of ground points represents the ground in the driving environment. The non-ground set represents objects in the driving environment. And the computer equipment utilizes the non-ground set to perform obstacle identification to obtain obstacles in the driving environment.
And step 206, acquiring the positioning information of the vehicle, and acquiring the electronic map according to the positioning information.
And step 208, identifying the driving area where the vehicle is located according to the positioning information and the electronic map.
The vehicle is also provided with a positioning device in advance, and the positioning information of the vehicle can be acquired in real time through the positioning device. And the computer equipment acquires the corresponding electronic map according to the positioning information of the vehicle. The electronic map includes road information, position information of the vehicle in the map, and the like. Based on the vehicle's location in the electronic map and the road information, the computer device may identify the road and the particular driving area on which the vehicle is currently located. The travel area may be a lane, and the travel area may include one or more lanes.
And step 210, projecting the point cloud data corresponding to the obstacle to an electronic map, and identifying the area where the obstacle is located.
In order to accurately identify whether the obstacle in the driving environment is an obstacle to be avoided, the computer device further needs to identify an area where the obstacle is located according to the point cloud data. The computer equipment marks the point cloud data of the obstacle identified in the non-ground set as point cloud data corresponding to the obstacle, and can also be called the obstacle point cloud for short. In order to accurately identify the position of the obstacle in the electronic map, the computer device projects the obstacle point cloud to the electronic map. Specifically, the three-dimensional coordinates of the point cloud data may be coordinates based on a sensor coordinate system. The coordinates of the electronic map may be based on map coordinates. And converting the sensor coordinate system and the map coordinate system, projecting the obstacle point cloud to the electronic map, and generating a projection image. The computer device identifies an area in the projected image where the obstacle is located.
And 212, when the obstacle and the vehicle are in the same driving area, calculating the distance between the obstacle and the vehicle.
And 214, acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
The area where the obstacle is located includes a driving area and a non-driving area. When the obstacle is located in the driving area, the computing device identifies whether the obstacle and the vehicle are in the same driving area according to the position of the obstacle in the electronic map and the position of the vehicle in the electronic map. If the vehicle is in the same driving area, the obstacle and the vehicle are in the same lane currently. The computer device calculates a distance between the obstacle and the vehicle. The distance between the obstacle and the vehicle may be referred to as an obstacle distance. Different obstacle distances may employ different deceleration strategies. Different accelerations can be selected according to the proportion between the distance from the obstacle and the safe distance, and the vehicle is controlled to decelerate at the current running speed according to the selected acceleration. So that the decelerated vehicle avoids collision with an obstacle. When the ratio of the distance to the safe distance is smaller than a first ratio, controlling the vehicle to decelerate at a first acceleration; when the ratio of the distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; and when the distance is smaller than the safe distance, controlling the vehicle to decelerate at a third acceleration until the vehicle stops.
In this embodiment, by collecting point cloud data, obstacles in the driving environment can be identified. By acquiring the positioning information of the vehicle, the driving area where the vehicle is located can be identified according to the positioning information and the electronic map. After the point cloud data corresponding to the obstacle is projected to the electronic map, the area where the obstacle is located can be identified. And when the obstacle and the vehicle are in the same driving area, calculating the distance between the obstacle and the vehicle, and calling a corresponding deceleration strategy to control the vehicle to decelerate according to the relation between the distance and the safe distance. Therefore, the vehicle can be effectively controlled to decelerate in the automatic driving process, and the collision between the vehicle and the obstacle is avoided.
In one embodiment, as shown in fig. 3, the step of identifying an obstacle in the driving environment by using the point cloud data specifically includes:
and step 302, segmenting the point cloud data to obtain a non-ground point set.
And 304, clustering the non-ground point set, and identifying the obstacles in the driving environment according to the clustering result.
To accurately identify obstacles in the driving environment, the computer device needs to filter out ground points in the point cloud data. In one embodiment, segmenting the point cloud data to obtain the non-ground point set includes: dividing the point cloud data into a plurality of sub-point clouds; generating a segmentation threshold corresponding to the sub-point cloud based on the plurality of point data in the sub-point cloud; and traversing a plurality of points in the sub-point cloud, and segmenting the plurality of points according to the segmentation threshold value to obtain a non-ground point set.
The computer device may divide the point cloud data into a plurality of sub-point clouds according to the point data corresponding to each point in the point cloud data. Specifically, the computer device may record the position of the sensor as the origin, and divide the horizontal plane in which the sensor is located into a plurality of grids. The computer equipment can determine the grids to which the points belong according to the horizontal axis coordinates and the vertical axis coordinates corresponding to the points, and a set formed by all the points in the same grid is recorded as a sub-point cloud. The sub-point clouds are a subset of the corresponding point clouds. The computer device may use one of a plurality of division methods to divide the horizontal plane in which the sensor is located into a plurality of grids.
The computer equipment can acquire point data corresponding to each point from the sub-point cloud and generate a segmentation threshold corresponding to the sub-point cloud according to the point data corresponding to all the points. The segmentation threshold refers to a threshold for segmenting points in the sub-point cloud into ground points and non-ground points, and may be a maximum height value generated from the point data. The segmentation threshold between different sub-point clouds may be the same or different. The computer equipment can obtain the vertical axis coordinate corresponding to the midpoint of the sub-point cloud, and the vertical axis coordinate can reflect the height of the point in the vertical axis direction. The computer device can evaluate the height of the ground in the grid according to the corresponding vertical axis coordinate to obtain a ground height coordinate, wherein the ground height coordinate can be the vertical axis coordinate corresponding to the ground highest point in the grid corresponding to the sub-point cloud. The computer equipment can record the coordinate value of the ground height coordinate as a segmentation threshold corresponding to the sub-point cloud, and perform segmentation processing on the points in the sub-point cloud by using the segmentation threshold.
The computer equipment can traverse a plurality of points in the sub-point cloud, and sequentially compare the vertical axis coordinate corresponding to each point with the segmentation threshold. When the vertical axis coordinate of the point is less than the segmentation threshold, the computer device may mark the point as a ground point. The computer device may mark the point as a non-ground point when the vertical axis coordinate of the point is greater than or equal to the segmentation threshold. And traversing each point in the sub-point cloud by the computer equipment, sequentially labeling the points, and segmenting the sub-point cloud until all the points in the sub-point cloud are segmented and labeled.
The computer device may obtain the ground points included in the sub-point cloud after the traversal of the points in the sub-point cloud is finished. The computer device may perform statistical analysis on the non-ground points corresponding to each of the plurality of sub-point clouds after performing segmentation processing on each of the plurality of sub-point clouds. The computer device may generate a set of non-ground points from the plurality of non-ground points corresponding to the plurality of divided sub-point clouds. The non-ground point set is a set consisting of non-ground points in the point cloud data collected by the sensor, and points in the point cloud data except the non-ground point set are ground points.
The computer equipment carries out clustering processing on the non-ground point set, and the clustering mode can be various. The computer equipment can determine the number of clusters, randomly select a corresponding number of points in a cluster to be clustered as initial class centers, calculate the distance from each point to each class center respectively, and select the class center closest to the point as a group of the points. And for each class, recalculating the clustering center through the geometric gravity center or the mean value respectively, and repeating for multiple times until the clustering center is converged. Through clustering, there may be multiple clustering results, with different numbers of points corresponding to different clustering results. And selecting the clustering result with the largest number of points as the optimal clustering result by the computer equipment, and taking the point cloud data corresponding to the optimal clustering result as the point cloud data corresponding to the obstacle.
In the embodiment, after the point cloud data acquired by the sensor is acquired by the computer device, the point cloud data is divided into a plurality of sub-point clouds according to the point data in the point cloud data, each sub-point cloud is relatively independent, the segmentation efficiency of the point cloud data is effectively improved, so that the non-ground point set in the point cloud data can be rapidly extracted, and the obstacle in the driving environment can be rapidly and accurately identified through the clustering processing of the non-ground point set.
In one embodiment, as shown in fig. 4, the step of projecting the point cloud data corresponding to the obstacle onto an electronic map and identifying the area where the obstacle is located specifically includes:
And step 404, projecting the point cloud data corresponding to the obstacle to an electronic map through the transformation matrix.
And step 406, identifying the area where the obstacle is located according to the position of the projected point cloud in the electronic map.
The three-dimensional coordinates in the point cloud data are coordinates based on a sensor coordinate system. The electronic map is a planar image established based on a map coordinate system. In order to accurately identify the position of the obstacle in the electronic map, the point cloud data needs to be projected into the electronic map through conversion between coordinate systems. Specifically, the computer device obtains a transformation matrix between the sensor coordinate system and the map coordinate system, which may also be referred to as an external reference between the sensor coordinate system and the map coordinate system. The transformation matrix comprises a rotation matrix and a translation matrix, wherein the rotation matrix is used for rotation transformation between the sensor coordinate system and the map coordinate system, and the translation matrix is used for translation transformation between the sensor coordinate system and the map coordinate system. The original point of the sensor coordinate system can be coincided with the original point of the map coordinate system by performing rotation transformation and translation transformation on the transformation matrix, so that the sensor coordinate system and the point cloud data are aligned to the map coordinate system.
And through the transformation matrix, the computer equipment aligns the point cloud data under the sensor coordinate system to the map coordinate system, projects the aligned point cloud data to an electronic map under the map coordinate system, and generates a projection image. The point cloud data are projected into the original image to generate a projected image, so that the area where the obstacle is located in the electronic map can be quickly and accurately identified according to the position of the projected point cloud in the electronic map. And then the computer equipment can identify whether the obstacle is an obstacle needing to be avoided according to the area where the obstacle is located, so that the control of the vehicle speed is facilitated, and the collision is avoided.
In one embodiment, calculating the distance between the obstacle and the vehicle includes: acquiring position information of the obstacle according to the point cloud data corresponding to the obstacle; acquiring position information of a sensor as position information of a vehicle; and calculating the distance between the obstacle and the vehicle by using the position information of the obstacle and the position information of the vehicle.
The point cloud data corresponding to the obstacle may be referred to as an obstacle point cloud for short. The obstacle point cloud includes a plurality of points, and different points correspond to different three-dimensional coordinates. Different calculation results may exist when the distance between the obstacle and the vehicle is calculated through different three-dimensional coordinates, and the calculation amount is large, the time consumption is long, and the vehicle deceleration is not favorable for fast control.
In order to effectively reduce the computation amount and improve the computation efficiency, after the obstacle point cloud is projected to the electronic map, the computer equipment can determine the position of the obstacle through the point cloud position in the electronic map. The position information of the obstacle may be expressed in two-dimensional coordinates. The positioning information of the vehicle may also be expressed in two-dimensional coordinates. The computer device may obtain the distance between the obstacle and the vehicle by calculating the distance between the two-dimensional coordinates corresponding to the obstacle and the two-dimensional coordinates corresponding to the vehicle.
In one embodiment, obtaining a corresponding deceleration strategy according to a relationship between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy comprises: when the ratio of the distance to the safe distance is smaller than a first ratio, controlling the vehicle to decelerate to a first speed at a first acceleration; continuing to calculate the distance between the obstacle and the vehicle according to the first speed; when the ratio of the recalculated distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; and when the recalculated distance is smaller than the safe distance, controlling the vehicle to decelerate to stop at the third acceleration.
The distance between the obstacle and the vehicle may be referred to as an obstacle distance. In order to avoid a collision of the vehicle with an obstacle during autonomous driving, the computer device sets a corresponding safety distance for the vehicle in advance. Wherein, the corresponding safe distance is different for different driving speeds of the vehicle. The safe distance can be calculated using the following formula (1).
Wherein S issafeFor a safe distance, v0Is the current running speed of the vehicle, amaxFor maximum acceleration, S, when braking the vehiclebufferIs the buffer distance.
Different driving speeds, different corresponding buffer distances, different maximum accelerations during braking, and different corresponding calculated safety distances. In the face of different running speeds and obstacle distances of different vehicles, the computer device can adopt different deceleration strategies to control the vehicle to decelerate. Specifically, when the ratio of the distance to the safe distance is smaller than a first ratio, the vehicle is controlled to decelerate at a first acceleration so that the vehicle decelerates from the current running speed to the first speed. And after the first speed, detecting the distance between the obstacle and the vehicle again, and if the ratio of the distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration so that the vehicle is decelerated from the first speed to the second speed, wherein the first ratio is larger than the second ratio, and the first ratio and the second ratio are both larger than 1. And when the distance is smaller than the safe distance, controlling the vehicle to decelerate until the vehicle stops at the third acceleration.
In the embodiment, different deceleration strategies are adopted, so that the vehicle can be effectively prevented from colliding with obstacles at different distances in the automatic driving process, and the safety of automatic driving is effectively improved.
In one embodiment, the method further comprises: as shown in fig. 5, the obstacle travel area prediction step specifically includes:
step 502, extracting point cloud characteristic information according to point cloud data corresponding to the obstacle.
And step 504, performing feature extraction on the electronic map to obtain a map feature image.
And 506, fusing the point cloud characteristic information and the map characteristic image through the track prediction model, calculating the fused characteristic information, and outputting the predicted driving path of the obstacle.
When the obstacle is a vehicle traveling on a road, the obstacle may change a traveling path in different sections, particularly, a turning section. In order to be able to continuously and accurately identify the obstacle, the computer device may further predict the travel path of the obstacle after identifying the travel area in which the obstacle is located.
The computer equipment extracts the characteristics of the point cloud data (namely the obstacle point cloud) corresponding to the obstacle. Wherein, a rasterization processing mode can be adopted to extract the point cloud characteristic information. The computer equipment can divide the signal area where the obstacle point cloud is located according to the preset size, and therefore a plurality of grid units are obtained. The size of the preset size may represent the size of the grid cell. When the computer device divides the signal area, the obstacle point clouds can be divided into corresponding grid cells. And the computer equipment extracts the features of the point cloud of the obstacle in the grid unit so as to obtain point cloud feature information corresponding to the obstacle.
The computer device extracts map elements in the electronic map, which may include lane lines, stop lines, pedestrian channels, traffic lights, traffic signs, and the like. After the map elements are extracted by the computer equipment, the map elements corresponding to each element channel can be identified in the extracted map elements according to a plurality of preset element channels, and the map elements corresponding to each element channel are rendered according to the plurality of element channels to obtain the map feature image. And the computer equipment converts the map feature image to obtain a map feature vector. And the computer equipment fuses the point cloud characteristic vector and the map characteristic vector through the track prediction model to obtain fused characteristic information. And performing prediction operation on the fused characteristic information through a track prediction model to obtain the driving direction and the driving path of the obstacle in a preset time period.
Specifically, the point cloud contextual features corresponding to the point cloud feature information and the map contextual features corresponding to the map feature image are extracted through a perception layer of the track prediction model. And inputting the point cloud context features and the map context features into a semantic analysis layer, and fusing the point cloud context features and the map context features through the semantic analysis layer to obtain fused feature information. And inputting the fused characteristic information into a prediction layer, performing prediction operation on the fused characteristic information through the prediction layer, and outputting the driving direction and the driving path of the obstacle in a preset time period.
In the embodiment, the driving direction and the driving path of the obstacle in the preset time period are predicted by using the track prediction model, so that the driving area of the obstacle can be accurately identified in the turning road section, the vehicle can be effectively controlled to decelerate, and the safety of automatic driving is improved.
It should be understood that although the various steps in the flow charts of fig. 2-5 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-5 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided an automatic driving-based vehicle speed control apparatus including: a data acquisition module 602, a first identification module 604, a location module 606, a second identification module 608, a distance calculation module 610, and a vehicle speed control module 612, wherein:
and the data acquisition module 602 is used for acquiring point cloud data in the driving process of the vehicle.
A first identification module 604 for identifying obstacles in the driving environment using the point cloud data.
The positioning module 606 is used for acquiring positioning information of the vehicle and acquiring an electronic map according to the positioning information; and identifying the driving area of the vehicle according to the positioning information and the electronic map.
The second identification module 608 is configured to project the point cloud data corresponding to the obstacle to the electronic map, and identify an area where the obstacle is located.
And the distance calculating module 610 is used for calculating the distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area.
And the vehicle speed control module 612 is configured to obtain a corresponding deceleration strategy according to the relationship between the distance and the safe distance, and control the vehicle to decelerate according to the deceleration strategy.
In one embodiment, the first identification module 604 is further configured to segment the point cloud data to obtain a non-ground point set; and clustering the non-ground point set, and identifying the obstacles in the driving environment according to the clustering result.
In one embodiment, the first identification module 604 is further configured to divide the point cloud data into a plurality of sub-point clouds; generating a segmentation threshold corresponding to the sub-point cloud based on the plurality of point data in the sub-point cloud; and traversing a plurality of points in the sub-point cloud, and segmenting the plurality of points according to the segmentation threshold value to obtain a non-ground point set.
In one embodiment, the second identification module 604 is further configured to obtain a transformation matrix between the sensor coordinate system and the map coordinate system; projecting the point cloud data corresponding to the barrier to an electronic map through a transformation matrix; and identifying the area where the barrier is located according to the position of the projected point cloud in the electronic map.
In one embodiment, the distance calculation module 610 is further configured to use the position information of the projected point cloud in the electronic map as the position information of the obstacle; using the positioning information as the position information of the vehicle; and calculating the distance between the obstacle and the vehicle by using the position information of the obstacle and the position information of the vehicle.
In one embodiment, the vehicle speed control module 612 is further configured to control the vehicle to slow down at a first acceleration when the distance to safe distance ratio is less than a first ratio; continuing to calculate the distance between the obstacle and the vehicle according to the first speed; when the ratio of the recalculated distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; and when the recalculated distance is smaller than the safe distance, controlling the vehicle to decelerate to stop at the third acceleration.
In one embodiment, as shown in fig. 7, the apparatus further includes a prediction module 614 for extracting point cloud feature information according to point cloud data corresponding to the obstacle; carrying out feature extraction on the electronic map to obtain a map feature image; and fusing the point cloud characteristic information and the map characteristic image through a track prediction model, calculating the fused characteristic information, and outputting a predicted driving path of the obstacle.
For specific limitations of the vehicle speed control device based on automatic driving, reference may be made to the above limitations of the vehicle speed control method based on automatic driving, which are not described herein again. The respective modules in the above-described automatic driving-based vehicle speed control apparatus may be wholly or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, or can be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer readable instructions, and a database. The internal memory provides an environment for the operating system and execution of computer-readable instructions in the non-volatile storage medium. The database of the computer device is used for storing point cloud data, electronic maps and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer readable instructions, when executed by a processor, implement an autopilot-based vehicle speed control method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and one or more processors, the memory having stored therein computer-readable instructions that, when executed by the processors, cause the one or more processors to perform the method steps provided in the various embodiments described above.
In one embodiment, one or more non-transitory computer-readable storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the method steps provided in the various embodiments described above are provided.
It will be understood by those of ordinary skill in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a non-volatile computer readable storage medium, and when executed, can include processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (20)
- An autonomous driving based vehicle speed control method comprising:collecting point cloud data in the driving process of a vehicle;identifying an obstacle in a driving environment using the point cloud data;acquiring positioning information of a vehicle, and acquiring an electronic map according to the positioning information;identifying a driving area where the vehicle is located according to the positioning information and the electronic map;projecting the point cloud data corresponding to the obstacle to the electronic map, and identifying the area where the obstacle is located;calculating a distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; andand acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
- The method of claim 1, wherein the identifying obstacles in a driving environment using the point cloud data comprises:segmenting the point cloud data to obtain a non-ground point set; andand clustering the non-ground point set, and identifying obstacles in the driving environment according to the clustering result.
- The method of claim 2, wherein the segmenting the point cloud data to obtain a set of non-ground points comprises:dividing the point cloud data into a plurality of sub-point clouds;generating a segmentation threshold corresponding to the sub-point cloud based on a plurality of point data in the sub-point cloud; andand traversing a plurality of points in the sub-point cloud, and segmenting the plurality of points according to the segmentation threshold value to obtain a non-ground point set.
- The method of claim 1, wherein the projecting the point cloud data corresponding to the obstacle to the electronic map, and the identifying the area of the obstacle comprises:acquiring a transformation matrix between a sensor coordinate system and a map coordinate system;projecting the point cloud data corresponding to the obstacle to the electronic map through the transformation matrix; andand identifying the area where the barrier is located according to the position of the projected point cloud in the electronic map.
- The method of claim 4, wherein the calculating the distance between the obstacle and the vehicle comprises:taking the position information of the projected point cloud in the electronic map as the position information of the obstacle;using the positioning information as the position information of the vehicle; andand calculating the distance between the obstacle and the vehicle by using the position information of the obstacle and the position information of the vehicle.
- The method according to claim 1, wherein the obtaining of the corresponding deceleration strategy according to the relationship between the distance and the safe distance, and the controlling of the vehicle deceleration according to the deceleration strategy comprises:when the ratio of the distance to the safe distance is smaller than a first ratio, controlling the vehicle to decelerate at a first acceleration;continuing to calculate a distance between the obstacle and the vehicle according to the first speed;when the ratio of the recalculated distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; andand when the recalculated distance is smaller than the safe distance, controlling the vehicle to decelerate to stop at the third acceleration.
- The method of claim 1, further comprising:extracting point cloud characteristic information according to the point cloud data corresponding to the obstacle;carrying out feature extraction on the electronic map to obtain a map feature image; andand fusing the point cloud characteristic information and the map characteristic image through the track prediction model, calculating the fused characteristic information, and outputting the predicted driving path of the obstacle.
- An automatic driving-based vehicle speed control apparatus comprising:the data acquisition module is used for acquiring point cloud data in the vehicle driving process;a first identification module for identifying an obstacle in a driving environment using the point cloud data;the positioning module is used for acquiring positioning information of the vehicle and acquiring an electronic map according to the positioning information; identifying a driving area where the vehicle is located according to the positioning information and the electronic map;the second identification module is used for projecting the point cloud data corresponding to the obstacle to the electronic map and identifying the area where the obstacle is located;the distance calculation module is used for calculating the distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; andand the vehicle speed control module is used for acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance and controlling the vehicle to decelerate according to the deceleration strategy.
- The apparatus of claim 8, wherein the first identification module is further configured to segment the point cloud data to obtain a set of non-ground points; and clustering the non-ground point set, and identifying obstacles in the driving environment according to the clustering result.
- The apparatus of claim 8, wherein the vehicle speed control module is further configured to control the vehicle to slow down at a first acceleration when the distance to safety distance ratio is less than a first ratio; continuing to calculate a distance between the obstacle and the vehicle according to the first speed; when the ratio of the recalculated distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; and when the recalculated distance is smaller than the safe distance, controlling the vehicle to decelerate to stop at the third acceleration.
- A computer device comprising a memory and one or more processors, the memory having stored therein computer-readable instructions that, when executed by the one or more processors, cause the one or more processors to perform the steps of:collecting point cloud data in the driving process of a vehicle;identifying an obstacle in a driving environment using the point cloud data;acquiring positioning information of a vehicle, and acquiring an electronic map according to the positioning information;identifying a driving area where the vehicle is located according to the positioning information and the electronic map;projecting the point cloud data corresponding to the obstacle to the electronic map, and identifying the area where the obstacle is located;calculating a distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; andand acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
- The computer device of claim 11, wherein the one or more processors further perform the steps of:segmenting the point cloud data to obtain a non-ground point set; andand clustering the non-ground point set, and identifying obstacles in the driving environment according to the clustering result.
- The computer device of claim 11, wherein the one or more processors further perform the steps of:acquiring a transformation matrix between a sensor coordinate system and a map coordinate system;projecting the point cloud data corresponding to the obstacle to the electronic map through the transformation matrix; andand identifying the area where the barrier is located according to the position of the projected point cloud in the electronic map.
- The computer device of claim 11, wherein the one or more processors further perform the steps of:when the ratio of the distance to the safe distance is smaller than a first ratio, controlling the vehicle to decelerate at a first acceleration;continuing to calculate a distance between the obstacle and the vehicle according to the first speed;when the ratio of the recalculated distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; andand when the recalculated distance is smaller than the safe distance, controlling the vehicle to decelerate to stop at the third acceleration.
- The computer device of claim 11, wherein the one or more processors further perform the steps of:extracting point cloud characteristic information according to the point cloud data corresponding to the obstacle;carrying out feature extraction on the electronic map to obtain a map feature image; andand fusing the point cloud characteristic information and the map characteristic image through the track prediction model, calculating the fused characteristic information, and outputting the predicted driving path of the obstacle.
- One or more non-transitory computer-readable storage media storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of:collecting point cloud data in the driving process of a vehicle;identifying an obstacle in a driving environment using the point cloud data;acquiring positioning information of a vehicle, and acquiring an electronic map according to the positioning information;identifying a driving area where the vehicle is located according to the positioning information and the electronic map;projecting the point cloud data corresponding to the obstacle to the electronic map, and identifying the area where the obstacle is located;calculating a distance between the obstacle and the vehicle when the obstacle and the vehicle are in the same driving area; andand acquiring a corresponding deceleration strategy according to the relation between the distance and the safe distance, and controlling the vehicle to decelerate according to the deceleration strategy.
- The storage medium of claim 16, wherein the one or more processors further perform the steps of:segmenting the point cloud data to obtain a non-ground point set; andand clustering the non-ground point set, and identifying obstacles in the driving environment according to the clustering result.
- The storage medium of claim 16, wherein the one or more processors further perform the steps of:acquiring a transformation matrix between a sensor coordinate system and a map coordinate system;projecting the point cloud data corresponding to the obstacle to the electronic map through the transformation matrix; andand identifying the area where the barrier is located according to the position of the projected point cloud in the electronic map.
- The storage medium of claim 16, wherein the one or more processors further perform the steps of:when the ratio of the distance to the safe distance is smaller than a first ratio, controlling the vehicle to decelerate at a first acceleration;continuing to calculate a distance between the obstacle and the vehicle according to the first speed;when the ratio of the recalculated distance to the safe distance is smaller than a second ratio, controlling the vehicle to decelerate at a second acceleration; andand when the recalculated distance is smaller than the safe distance, controlling the vehicle to decelerate to stop at the third acceleration.
- The storage medium of claim 16, wherein the one or more processors further perform the steps of:extracting point cloud characteristic information according to the point cloud data corresponding to the obstacle;carrying out feature extraction on the electronic map to obtain a map feature image; andand fusing the point cloud characteristic information and the map characteristic image through the track prediction model, calculating the fused characteristic information, and outputting the predicted driving path of the obstacle.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114326718A (en) * | 2021-12-14 | 2022-04-12 | 科沃斯商用机器人有限公司 | Map construction method, self-moving robot and storage medium |
| CN114550476A (en) * | 2021-11-30 | 2022-05-27 | 深圳元戎启行科技有限公司 | Data processing method, vehicle management platform and computer readable storage medium |
| CN116166033A (en) * | 2023-04-21 | 2023-05-26 | 深圳市速腾聚创科技有限公司 | Vehicle obstacle avoidance method, device, medium and electronic equipment |
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| CN116413740B (en) * | 2023-06-09 | 2023-09-05 | 广汽埃安新能源汽车股份有限公司 | Laser radar point cloud ground detection method and device |
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| CN117238171B (en) * | 2023-09-28 | 2024-03-08 | 山东梁山华宇集团汽车制造有限公司 | Vehicle detection system |
| CN117742351B (en) * | 2024-02-19 | 2024-06-11 | 深圳竹芒科技有限公司 | Control method of self-mobile device, and readable storage medium |
| CN117901854B (en) * | 2024-03-06 | 2024-06-25 | 广东鑫龙电机科技有限公司 | Brake control method and system for electronic hand brake motor |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170104287A (en) * | 2016-03-07 | 2017-09-15 | 한국전자통신연구원 | Driving area recognition apparatus and method for recognizing driving area thereof |
| CN108791286A (en) * | 2018-06-21 | 2018-11-13 | 奇瑞汽车股份有限公司 | Driving collision avoidance method and apparatus |
| CN109808686A (en) * | 2019-04-02 | 2019-05-28 | 上海快仓智能科技有限公司 | Vehicle obstacle-avoidance method, apparatus and vehicle |
| CN109927719A (en) * | 2017-12-15 | 2019-06-25 | 百度在线网络技术（北京）有限公司 | A kind of auxiliary driving method and system based on barrier trajectory predictions |
| CN110221603A (en) * | 2019-05-13 | 2019-09-10 | 浙江大学 | A kind of long-distance barrier object detecting method based on the fusion of laser radar multiframe point cloud |
| CN110377029A (en) * | 2019-06-27 | 2019-10-25 | 北京汽车集团有限公司 | The control method and device of Vehicular automatic driving |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| MY185480A (en) * | 2012-12-17 | 2021-05-19 | Daihatsu Motor Co Ltd | Driving assistance device |
| CN103760904B (en) * | 2014-02-13 | 2016-10-19 | 北京工业大学 | A kind of voice broadcast type intelligent vehicle path planning apparatus and implementation |
| CN104002747B (en) * | 2014-06-10 | 2016-01-06 | 北京联合大学 | A kind of multilasered optical radar grating map emerging system based on intelligent car |
| CN110539638A (en) * | 2018-05-30 | 2019-12-06 | 深圳疆程技术有限公司 | method and device for assisting driving of traffic equipment |
| CN109318890A (en) * | 2018-06-29 | 2019-02-12 | 北京理工大学 | A kind of unmanned vehicle dynamic obstacle avoidance method based on dynamic window and barrier potential energy field |
| CN109491380A (en) * | 2018-10-26 | 2019-03-19 | 黄鑫 | A kind of intelligent automobile Unmanned Systems and application method |
| CN110296713B (en) * | 2019-06-17 | 2024-06-04 | 广州卡尔动力科技有限公司 | Roadside automatic driving vehicle positioning navigation system and single/multiple vehicle positioning navigation method |
-
2019
- 2019-12-31 CN CN201980037490.3A patent/CN113424121A/en active Pending
- 2019-12-31 WO PCT/CN2019/130421 patent/WO2021134441A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170104287A (en) * | 2016-03-07 | 2017-09-15 | 한국전자통신연구원 | Driving area recognition apparatus and method for recognizing driving area thereof |
| CN109927719A (en) * | 2017-12-15 | 2019-06-25 | 百度在线网络技术（北京）有限公司 | A kind of auxiliary driving method and system based on barrier trajectory predictions |
| CN108791286A (en) * | 2018-06-21 | 2018-11-13 | 奇瑞汽车股份有限公司 | Driving collision avoidance method and apparatus |
| CN109808686A (en) * | 2019-04-02 | 2019-05-28 | 上海快仓智能科技有限公司 | Vehicle obstacle-avoidance method, apparatus and vehicle |
| CN110221603A (en) * | 2019-05-13 | 2019-09-10 | 浙江大学 | A kind of long-distance barrier object detecting method based on the fusion of laser radar multiframe point cloud |
| CN110377029A (en) * | 2019-06-27 | 2019-10-25 | 北京汽车集团有限公司 | The control method and device of Vehicular automatic driving |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114550476A (en) * | 2021-11-30 | 2022-05-27 | 深圳元戎启行科技有限公司 | Data processing method, vehicle management platform and computer readable storage medium |
| CN114326718A (en) * | 2021-12-14 | 2022-04-12 | 科沃斯商用机器人有限公司 | Map construction method, self-moving robot and storage medium |
| CN116166033A (en) * | 2023-04-21 | 2023-05-26 | 深圳市速腾聚创科技有限公司 | Vehicle obstacle avoidance method, device, medium and electronic equipment |
| CN116166033B (en) * | 2023-04-21 | 2024-05-21 | 深圳市速腾聚创科技有限公司 | Vehicle obstacle avoidance method, device, medium and electronic equipment |
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