Method for determining occupancy condition of position and related equipment

Abstract

The embodiment of the invention provides a method for determining the occupancy condition of a position and related equipment, and the method comprises the steps: obtaining measurement data, the measurement data comprises a first distance and an angle, and the angle comprises a first azimuth angle and / or a first pitch angle; according to the first distance, a first parameter value used for indicating the object occupation condition of the first position is determined; determining an angle adjustment factor corresponding to the first position according to the angle; according to the first parameter value and the angle adjustment factor, a second parameter value corresponding to the first position is determined, and the second parameter value indicates the object occupation condition of the first position. The object occupancy condition of the first position is determined by means of more information, the influence of the distance and the angle on the object occupancy condition of the first position is independently determined, and the accuracy of the finally obtained position occupancy condition is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the computer technology field, and in particular, to a method for determining an occupancy of a position and related equipment. BACKGROUND

[0002] In the related art, a distance corresponding to an object in a field of view can be measured by a sensor, and an occupancy of an object at a position in a surrounding environment can be calculated based on the distance. However, the accuracy of the occupancy of the object at the position obtained by the foregoing method is not high enough, and thus a more accurate method for determining the occupancy of the object at the position is urgently needed. SUMMARY

[0003] The present application provides a method for determining an occupancy of a position and related equipment, which determines the occupancy of an object at a first position by using more information, and improves the accuracy of the occupancy of the object at the position. The influences of a distance and an angle on the occupancy of the object at the first position are determined respectively and independently, and the accuracy of the occupancy of the object at the first position is further improved.

[0004] The present application provides the following technical solutions:

[0005] In a first aspect, the present application provides a method for determining an occupancy of a position. The method can be used in the field of intelligent driving. In the method, a first device obtains measurement data, where the measurement data includes a first distance and an angle (hereinafter referred to as a “first angle” for convenience of description), and the first angle includes a first azimuth angle and / or a first pitch angle. The measurement data is obtained by a sensor, for example. The first device determines a first parameter value indicating the occupancy of an object at a first position based on the first distance, determines an angle adjustment factor corresponding to the first position based on the first angle, and then determines a second parameter value corresponding to the first position based on the first parameter value and the angle adjustment factor, where the second parameter value indicates the occupancy of the object at the first position. The first position can be a position in a field of view of the sensor, for example.

[0006] The first device can be a vehicle, a mobile robot, or another type of device, for example.

[0007] In one case, the second parameter value can represent a probability that the first position is occupied by an object, the first parameter value can be a first occupancy probability of the first position, the first occupancy probability can be a probability that the first position is occupied by an object based on the distance in the measurement data, and the second occupancy probability can be a probability that the first position is occupied by an object based on the distance and the angle in the measurement data.

[0008] In another case, the second parameter value can be a parameter value obtained by transforming the second occupancy probability, for example, the second parameter value can be a ratio equivalent form, denoted as: odd=p r,a (r,a) / (1-p r,a (r,a)),wherein odd is the second parameter value, p r,a (r,a) is the second occupancy probability; for another example, the second parameter value can be a logarithmic equivalent form, denoted as: log-odd=ln*p r,a (r,a) / (1-p r,a (r,a))+, wherein log-odd is the second parameter value, p r,a (r,a) is the second occupancy probability, and the like, or other transformed second parameter values of the second occupancy probability, for example, can be 1-p r,a (r,a), i.e. the free probability, and correspondingly, there can be a corresponding ratio or logarithmic ratio form; or other equivalent forms derived in other ways, which generally satisfy a one-to-one mapping relationship or a one-to-one mapping relationship within a specified range, and the specific transformation manner can be determined in combination with actual application scenarios.

[0009] In the present implementation, when each set of measurement data is acquired by the sensor, not only the distance but also the azimuth angle and / or the elevation angle are acquired, so that not only the first parameter value for indicating the object occupancy of the first position is determined according to the acquired distance, but also an angle adjustment factor is determined according to the aforementioned azimuth angle and / or the elevation angle, and then the second parameter value of the first position is finally determined according to the first parameter value and the aforementioned angle adjustment factor, and the second parameter value indicates the object occupancy of the first position. Since more information is used to determine the object occupancy of the first position, the accuracy of the finally obtained second parameter value is improved. Moreover, the first parameter value for indicating the object occupancy of the first position is determined based on the distance, the angle adjustment factor is determined according to the azimuth angle and / or the elevation angle, i.e. the influence of the distance and the angle on the object occupancy of the first position is determined independently, the first parameter value is adjusted based on the angle adjustment factor to determine the final object occupancy of the first position, which improves the fineness of the process of determining the object occupancy of the first position, and further improves the accuracy of the finally obtained second parameter value.

[0010] In a possible implementation, the first angle adjustment factor is determined based on the first azimuth angle and first quality information, the first quality information being used to reflect an error of the first azimuth angle in the measurement data, or the first quality information can also be understood as being used to reflect a quality of the first azimuth angle in the measurement data. And / or, the second angle adjustment factor is determined based on the first elevation angle and second quality information, the second quality information being used to reflect an error of the first elevation angle in the measurement data, or the first quality information can also be understood as being used to reflect a quality of the first elevation angle in the measurement data. The first angle adjustment factor and / or the second angle adjustment factor are used to determine the angle adjustment factor.

[0011] For example, the first quality information indicates a standard deviation and / or a variance and / or a root mean square error and / or a mean square error of the azimuth angle measurement error. It should be noted that the "azimuth angle measurement error" in the present application can also be referred to as "error of the azimuth angle dimension in the measurement data".

[0012] For example, the second quality information indicates a standard deviation and / or a variance and / or a root mean square error and / or a mean square error of the elevation angle measurement error. It should be noted that the "elevation angle measurement error" in the present application can also be referred to as "error of the elevation angle dimension in the measurement data".

[0013] In the implementation, the first angle adjustment factor in the azimuth angle dimension is determined based on the first azimuth angle in the measurement data and first quality information reflecting an error of the azimuth angle in the measurement data obtained by the sensor, so that the first angle adjustment factor obtained can fully consider the influence of the error in the azimuth angle measurement by the sensor, which is not only beneficial to obtain a more accurate angle adjustment factor, and further beneficial to obtain a more accurate object occupancy, but also beneficial to make the present scheme self-adaptive to different specifications in the error dimension in the azimuth angle measurement, that is, beneficial to make the present scheme applicable to more sensors, and beneficial to expand the application range of the present scheme.

[0014] The first angle adjustment factor in the elevation angle dimension is determined based on the first elevation angle in the measurement data and second quality information reflecting an error of the elevation angle in the measurement data obtained by the sensor, so that the second angle adjustment factor obtained can fully consider the influence of the error in the elevation angle measurement by the sensor, which is not only beneficial to obtain a more accurate angle adjustment factor, and further beneficial to obtain a more accurate object occupancy, but also beneficial to make the present scheme self-adaptive to different specifications in the error dimension in the elevation angle measurement, that is, beneficial to make the present scheme applicable to more sensors, and beneficial to expand the application range of the present scheme.

[0015] In a possible implementation, the first angle adjustment factor is determined according to the first azimuth angle, the second azimuth angle, and the first quality information based on a first function, where the first function is a Gaussian function, or the first function is based on an error function erf, or the first function is based on an activation function sigmoid. And / or, the second angle adjustment factor is determined according to the first elevation angle, the second elevation angle, and the second quality information based on a second function, where the second function is a Gaussian function, or the second function is based on an error function erf, or the second function is based on an activation function sigmoid.

[0016] In the implementation, the first angle adjustment factor and the second angle adjustment factor are determined based on the function, which improves the realizability of the scheme, and the first function (or the second function) is a Gaussian function, or is based on an error function, or is based on an activation function, which provides a simple and effective implementation scheme.

[0017] In a possible implementation, the first parameter value is a first occupancy probability, the second parameter value is a second occupancy probability, and the second occupancy probability, the first occupancy probability, and the angle adjustment factor satisfy the following relationship: p r,a (r, a) = c0 + (p r (r) - c1) · F(a) where p r,a (r, a) is the second occupancy probability, p r (r) is the first occupancy probability, F(a) is the angle adjustment factor, and the values of c0 and c1 are preset values.

[0018] In the implementation, the relationship between the second occupancy probability, the first occupancy probability, and the angle adjustment factor is disclosed, which improves the realizability of the scheme; the closer the relative position between the first position and the sensor to the measurement data, the greater the object occupancy probability of the first position, the second occupancy probability obtained by using the method provided in the application is expected, and since the relationship between the distance in the measurement data and the idle probability at the first position no longer needs to be modeled in the scheme provided in the application, the implementation difficulty of the scheme is greatly reduced.

[0019] In a possible implementation, c0 and c1 are both 0.5. In this implementation, the values of c0 and c1 are further disclosed, which further improves the realizability of the scheme, and the product of the difference between the first occupancy probability and 0.5 and the angle adjustment factor is added to 0.5 to obtain the final second occupancy probability, and the final value of the second occupancy probability fluctuates on the basis of 0.5, the occupancy probability of a region outside the predetermined range of the position corresponding to the measurement data and close to the sensor is lower than 0.5, and tends to a minimum value as the distance difference and the angle difference decrease, which meets the expectation that the occupancy state tends to be idle. In addition, the occupancy probability of a position outside the predetermined range of the position corresponding to the measurement data and far from the sensor tends to 0.5, which meets the expectation of uncertainty, because the occupancy probability of a position is 0.5, which means that it is completely uncertain whether the position is occupied by an object (the occupancy probability is 0.5 and the idle probability is also 0.5). More importantly, the occupancy probability of the position corresponding to the measurement data is the highest, and the occupancy probability of a position in the specified range near the position corresponding to the measurement data gradually decreases, which meets the expectation that the occupancy probability of the position corresponding to the measurement data is the highest. Because the occupancy probability of a position in the specified range near the position corresponding to the measurement data is generated depending on the measurement error, it is also expected that the occupancy probability of a position in the specified range near the position corresponding to the measurement data gradually decreases. The measurement data obtained based on the sensor can increase or decrease the probability that the position is occupied by an object, and the design that the values of c0 and c1 are both 0.5 is more conducive to making the final second occupancy probability meet the foregoing expectations, so as to be conducive to obtaining a more accurate second occupancy probability of a position through the formula, and to be conducive to more accurately determining the object occupancy of the position.

[0020] In a possible implementation, the first position and the measurement data satisfy a preset condition, and the preset condition includes at least one of the following: an absolute value of a difference between a distance corresponding to the first position (hereinafter referred to as a "second distance" for convenience of description) and the first distance is less than or equal to a distance threshold value, and / or an absolute value of a difference between an azimuth angle corresponding to the first position (hereinafter referred to as a "second azimuth angle" for convenience of description) and the first azimuth angle is less than or equal to a first angle value, and / or an absolute value of a difference between a pitch angle corresponding to the first position (hereinafter referred to as a "second pitch angle" for convenience of description) and the first pitch angle is less than or equal to a second angle value.

[0021] The second distance corresponding to the first position can be a distance between the first position and an origin of the sensor coordinate system or the carrier coordinate system, the second azimuth angle corresponding to the first position can be an azimuth angle of the first position relative to the sensor coordinate system or the carrier coordinate system, and the second pitch angle corresponding to the first position can be a pitch angle of the first position relative to the sensor coordinate system or the carrier coordinate system. It should be understood that the first distance, the second distance, the first azimuth angle, the second azimuth angle, the first pitch angle, and the second pitch angle are all relative to data in the same coordinate system.

[0022] Exemplarily, if the first device is a vehicle, the carrier coordinate system is a vehicle body coordinate system, and the origin of the vehicle body coordinate system can be located at the center of the rear axle of the vehicle body or the landing point of the center of the rear axle; if the first device is a mobile robot, the carrier coordinate system is a robot coordinate system, and the specific form of the carrier coordinate system can be determined in combination with an actual application scenario.

[0023] Since a specific measurement data is generated based on an object at a specific position in the surrounding environment, theoretically, it is meaningful to determine the object occupancy of the specific position based on the specific measurement data, and it is meaningless to determine the object occupancy of a position other than the specific position based on the specific measurement data. In the present application, a preset condition is set, so that a specific measurement data in the present application is only used to determine the object occupancy of a first position that meets the preset condition with the measurement data. Compared with the case where each measurement data is used to determine the object occupancy of each position, the meaningless calculation is greatly reduced, thereby effectively reducing the computing power requirement of the present application, and the effective utilization rate of computer resources is improved.

[0024] Further, the distance, azimuth angle, and / or pitch angle in the measurement data are used for screening, which provides a simple and easy-to-operate screening method. In addition, considering that the distance, azimuth angle, and / or pitch angle in the measurement data may have errors, so that there may be errors between the position of the object of the measurement data and the position pointed by the measurement data, the distance threshold value, the first angle value, and / or the second angle value are set in the preset condition. Compared with the case where the preset condition is set as “the distance corresponding to the first position is equal to the first distance, and / or, the azimuth angle corresponding to the first position is equal to the first azimuth angle, and / or, the pitch angle corresponding to the first position is equal to the first pitch angle”, the object occupancy of the position where the measurement data is generated can be determined by using a specific measurement data under the premise of avoiding waste of computer resources as much as possible.

[0025] In a possible implementation, the distance threshold value is determined based on a standard deviation or a root mean square error reflecting a distance measurement error, the first angle value is determined based on a standard deviation or a root mean square error reflecting an azimuth angle measurement error, and the second angle value is determined based on a standard deviation or a root mean square error reflecting a pitch angle measurement error.

[0026] Optionally, the distance threshold value can be a product of a standard deviation or a root mean square error reflecting a distance measurement error and a first multiple, for example, the first multiple can be 1.5 times, 2 times, 2.5 times, 2.6 times, 2.8 times, 3 times, 4 times, or other values.

[0027] Optionally, the first angle value can be a product of a standard deviation reflecting an azimuth angle measurement error and a second multiple, for example, the second multiple can be 2 times, 2.5 times, 2.6 times, 2.8 times, 3 times, 3.5 times, or other values. The second angle value can be a product of a standard deviation reflecting a pitch angle measurement error and a third multiple, the third multiple and the second multiple can be the same or different, for example, the third multiple can be 2 times, 2.5 times, 2.6 times, 2.8 times, 3 times, 3.5 times, or other values.

[0028] In the implementation, the standard deviation or the root mean square error reflecting the measurement distance error can indicate an error of the distance in the measurement data, the standard deviation or the root mean square error reflecting the measurement azimuth angle error can indicate an error of the azimuth angle in the measurement data, and the standard deviation or the root mean square error reflecting the measurement pitch angle error can indicate an error of the pitch angle in the measurement data. In the application, the distance threshold value is determined based on the standard deviation or the root mean square error reflecting the measurement distance error, the first angle value is determined based on the standard deviation or the root mean square error reflecting the measurement azimuth angle error, and the second angle value is determined based on the standard deviation or the root mean square error reflecting the measurement pitch angle error, thereby facilitating compatibility of sensors with different error specifications and expansion of application scenarios of the application.

[0029] In a possible implementation, the first parameter value is obtained based on the first distance and third quality information, and the third quality information is used to reflect an error of the first distance in the measurement data, or the third quality information can also be understood as quality used to reflect the first distance in the measurement data.

[0030] For example, the third quality information indicates a standard deviation and / or variance and / or root mean square error and / or mean square error of the distance measurement error. It should be noted that the "distance measurement error" in the application can also be referred to as "error of the distance dimension in the measurement data".

[0031] In the present implementation, the object occupancy of a certain position in the distance dimension (i.e., the first parameter value) is determined based on the first distance in the measurement data and the third quality information which can accurately reflect the error of the distance in the measurement data obtained by the sensor, so that the first parameter value obtained can fully consider the influence of the error of the sensor when measuring the distance, which is not only conducive to obtaining a more accurate first parameter value, and thus conducive to obtaining a more accurate object occupancy, but also conducive to making the present scheme adaptive to different specifications in the dimension of the error when measuring the distance, i.e., conducive to making the present scheme applicable to more sensors, and conducive to expanding the scope of application of the present scheme.

[0032] In a possible implementation, at least one measurement data is obtained by the sensor, and the at least one measurement data is used to obtain a map corresponding to the field of view range of the sensor, and each measurement data is used to obtain the second parameter value of one or more first positions in the map.

[0033] Optionally, the above map can be specifically represented as a grid map, and the positions in the map can be represented as grid units in the grid map. For example, the grid map can include a plurality of grids and the second parameter value of each grid.

[0034] In the present implementation, a specific application scenario of the occupancy of the position obtained based on the measurement data is provided, and the degree of combination between the present scheme and the specific application scenario is improved.

[0035] In a second aspect, the present application provides a determination apparatus of the occupancy of a position, which comprises: an acquisition module, configured to acquire measurement data, the measurement data comprising a first distance and an angle, the angle comprising a first azimuth angle and / or a first pitch angle; a determination module, configured to determine a first parameter value for indicating the object occupancy of a first position according to the first distance; the determination module is further configured to determine an angle adjustment factor corresponding to the first position according to the angle; and the determination module is further configured to determine a second parameter value corresponding to the first position according to the first parameter value and the angle adjustment factor, the second parameter value indicating the object occupancy of the first position.

[0036] In the second aspect of the present application, the determination apparatus of the occupancy of a position is further configured to perform the steps performed by the first device in the first aspect and the various possible implementation manners of the first aspect, and the meanings of the terms in the second aspect of the present application and the various possible implementation manners, and the beneficial effects brought by each possible implementation manner can be referred to the description in the various possible implementation manners of the first aspect, which will not be repeated here.

[0037] In a third aspect, an apparatus is provided. The apparatus includes a processor and a memory coupled with the processor. The memory is configured to store a program. The processor is configured to execute the program in the memory to cause the apparatus to perform the method of the first aspect.

[0038] In a fourth aspect, a vehicle is provided. The vehicle includes a processor and a memory coupled with the processor. The memory is configured to store a program. The processor is configured to execute the program in the memory to cause the vehicle to perform the method of the first aspect.

[0039] In a fifth aspect, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. When the computer program is executed on a computer, the computer is caused to perform the method of the first aspect.

[0040] In a sixth aspect, a computer program product is provided. The computer program product includes a program. When the program is executed on a computer, the computer is caused to perform the method of the first aspect.

[0041] In a seventh aspect, a chip system is provided. The chip system includes a processor configured to support implementation of functions involved in the above aspects, such as sending or processing data and / or information involved in the above method. In a possible design, the chip system further includes a memory configured to save necessary program instructions and data of a terminal device or a communication device. The chip system can be composed of a chip, or include a chip and other discrete devices. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A structural schematic diagram of an artificial intelligence subject framework provided by the present application;

[0043] Figure 2 A schematic diagram of a method for determining occupancy of a position provided by an embodiment of the present application;

[0044] Figure 3 A schematic diagram of a second function term provided by an embodiment of the present application;

[0045] Figure 4 Several schematic diagrams of a first occupancy probability provided by an embodiment of the present application;

[0046] Figure 5 Several schematic diagrams of a first function provided by an embodiment of the present application;

[0047] Figure 6 Two schematic diagrams of a second parameter value obtained by using the method provided by an embodiment of the present application;

[0048] Figure 7 Another schematic diagram of the method for determining the occupancy of a location provided by the embodiments of the present application;

[0049] Figure 8 Another schematic diagram of the method for determining the occupancy of a location provided by the embodiments of the present application;

[0050] Figure 9 A structural schematic diagram of the device for determining the occupancy of a location provided by the embodiments of the present application;

[0051] Figure 10 A structural schematic diagram of the equipment provided by the embodiments of the present application;

[0052] Figure 11 A structural schematic diagram of the vehicle provided by the embodiments of the present application. DETAILED DESCRIPTION

[0053] The embodiments of the present application will be described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Those skilled in the art can know that, as new application scenarios appear, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0054] The terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, and this is only a distinguishing way used in the description of the embodiments of the present application to describe the objects with the same attributes. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, so that the processes, methods, systems, products or equipment containing a series of units do not have to be limited to those units, but can include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0055] In the embodiments of the present application, the indication can include direct indication and indirect indication, and can also include explicit indication and implicit indication. The information indicated by certain information (indication information described below) is referred to as to-be-indicated information. In the implementation process, there are many ways to indicate the to-be-indicated information, for example, but not limited to, the to-be-indicated information can be directly indicated, such as the to-be-indicated information itself or an index of the to-be-indicated information. The to-be-indicated information can also be indirectly indicated by indicating other information, where the other information and the to-be-indicated information have an association relationship. The to-be-indicated information can also be indicated only by a part of the to-be-indicated information, and the other part of the to-be-indicated information is known or agreed in advance. For example, the arrangement order of each information agreed in advance (for example, protocol predefined) can be used to indicate a specific information, thereby reducing the indication overhead to a certain extent. The specific manner of indication is not limited in the present application. It can be understood that the indication information can be used to indicate the to-be-indicated information for the sender of the indication information, and the indication information can be used to determine the to-be-indicated information for the receiver of the indication information.

[0056] First, the overall workflow of the artificial intelligence system is described, please refer to Figure 1 , Figure 1 A structural schematic diagram of an artificial intelligence subject framework provided by the present application is shown in the following two dimensions of "intelligent information chain" (horizontal axis) and "IT value chain" (vertical axis). The "intelligent information chain" reflects a series of processes from data acquisition to processing. For example, it can be a general process of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision-making, intelligent execution and output. In this process, data undergoes a condensation process of "data-information-knowledge-wisdom". The "IT value chain" reflects the value brought by artificial intelligence to the information technology industry from the bottom infrastructure of human intelligence, information (provision and processing technology implementation) to the industrial ecological process of the system.

[0057] (1) Infrastructure

[0058] The infrastructure provides computing power support for the artificial intelligence system, realizes communication with the external world, and realizes support through the underlying platform. Communication with the outside world is realized through sensors; computing power is provided by an intelligent chip, which can specifically adopt a central processing unit (CPU), a neural-network processing unit (NPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), or a field programmable gate array (FPGA) hardware acceleration chip; the underlying platform includes a distributed computing framework and related platform guarantees and supports such as a network, which can include cloud storage and computing, an interconnection network, etc. For example, the sensor and external communication obtain data, which are provided to the intelligent chip in the distributed computing system provided by the underlying platform for calculation.

[0059] (2) Data

[0060] The data of the upper layer of the infrastructure is used to represent the data source in the field of artificial intelligence. The data relates to graphics, images, voice, text, and also relates to the Internet of Things data of traditional devices, including the business data of existing systems and sensing data such as force, displacement, liquid level, temperature, and humidity.

[0061] (3) Data processing

[0062] Data processing usually includes data training, machine learning, deep learning, search, reasoning, decision-making, etc.

[0063] Among them, machine learning and deep learning can model, extract, preprocess, train, etc. symbolic and formalized intelligent information of data.

[0064] Reasoning refers to the process of simulating human intelligent reasoning methods in a computer or intelligent system, using formalized information to perform machine thinking and solve problems according to reasoning control strategies, and the typical function is search and matching.

[0065] Decision-making refers to the process of decision-making after intelligent information is reasoned, which usually provides functions such as classification, sorting, and prediction.

[0066] (4) General capabilities

[0067] After the data is processed as mentioned above, some general capabilities can be formed based on the results of the data processing, such as algorithms or a general system, for example, translation, text analysis, computer vision processing, speech recognition, image recognition, etc.

[0068] (5) Intelligent product and industry application

[0069] Intelligent product and industry application refers to the product and application of artificial intelligence system in various fields, which is the packaging of the overall solution of artificial intelligence, and realizes the application of intelligent information decision productization. The application fields mainly include: intelligent terminal, intelligent manufacturing, intelligent transportation, smart home, intelligent medical treatment, intelligent security, intelligent driving, smart city, etc.

[0070] The method provided in the present application can be applied to various application scenarios that need to determine the object occupancy of one or more positions; for example, the method provided in the present application can be used in an application scenario of obtaining measurement data through a sensor, and determining the object occupancy of the aforementioned positions according to the measurement data, wherein the aforementioned positions can be located within the visual range of the sensor, and the “position” in the present application can also be referred to as “position area”. The various application scenarios of the method provided in the present application are exemplified as follows.

[0071] 1. Intelligent driving

[0072] For example, when the driving system in the vehicle needs to determine which objects exist in the surrounding environment, the measurement data corresponding to the objects within the visual range can be obtained through a sensor, and the object occupancy of one or more positions within the visual range of the sensor can be determined based on the measurement data, so as to understand which objects exist in the surrounding environment; for example, the vehicle can be a car, a truck, a motorcycle, a bus, a ship, an airplane, a helicopter, an entertainment vehicle, an amusement park vehicle, a trolley, a golf cart or a train, etc., which is not particularly limited in the present application.

[0073] For another example, when the driving system in the vehicle needs to determine the object occupancy of one or more positions, the measurement data corresponding to the objects within the visual range can be obtained through a sensor, and the object occupancy of the aforementioned positions can be determined based on the measurement data, etc.

[0074] 2. Intelligent terminal

[0075] Exemplarily, when a mobile robot or an agent needs to determine which objects exist in the surrounding environment, the sensor can be used to obtain measurement data corresponding to the objects in the field of view, determine the object occupancy of one or more positions in the field of view of the sensor based on the measurement data, and then understand which objects exist in the surrounding environment. For example, the mobile robot can be a sweeping robot, a food delivery robot or other types of mobile robots, which can be determined in combination with actual application scenarios. It should be noted that the present application can also be applied to other application scenarios, which will not be enumerated one by one here.

[0076] In the related art, the distance measured by the sensor is generally used to calculate the object occupancy of one or more positions in the field of view of the sensor, but the accuracy of the final occupancy of the aforementioned positions is not high. In order to solve the foregoing problem, the present application discloses: obtaining measurement data obtained by a sensor, the measurement data including a first distance and an angle (for convenience of description, hereinafter referred to as "first angle"), the first angle including a first azimuth angle and / or a first pitch angle; determining a first parameter value for indicating the object occupancy of a first position according to the first distance, the first position being one position in the field of view of the sensor; determining an angle adjustment factor corresponding to the first position according to the first angle, and then determining a second parameter value of the first position according to the first parameter value and the angle adjustment factor, the second parameter value indicating the object occupancy of the first position.

[0077] In the embodiments of the present application, more information is used to determine the object occupancy of the first position, which is beneficial to improve the accuracy of the final obtained second parameter value. The first parameter value for indicating the object occupancy of the first position is determined based on the distance, and the angle adjustment factor is determined based on the azimuth angle and / or the pitch angle, that is, the influence of the distance and the angle on the object occupancy of the first position is determined independently, respectively. The first parameter value is adjusted based on the angle adjustment factor to determine the final object occupancy of the first position, which improves the fineness of the process of determining the object occupancy of the first position and is beneficial to further improve the accuracy of the final obtained second parameter value.

[0078] In combination with the above description, the detailed implementation process of the method for determining the occupancy of a position provided by the present application is introduced as follows, and for details, please refer to Figure 2 , Figure 2 One schematic diagram of the method for determining the occupancy of a position provided by the embodiments of the present application is shown in Figure 2 , which can include the following steps:

[0079] 201, obtaining measurement data, the measurement data including a first distance and a first angle, the first angle including a first azimuth angle and / or a first pitch angle.

[0080] For example, the first device can obtain at least one measurement data obtained by the sensor, each of the at least one measurement data comprising a first distance and a first angle. The first device can be a vehicle, a mobile robot, or another type of device, which can be determined in combination with an actual application scenario.

[0081] Specifically, in one case, the measurement data obtained by the sensor is point cloud data, for example, the sensor can be an ultrasonic sensor, a lidar sensor, a millimeter wave radar sensor, or another sensor capable of measuring point cloud data, and the like. Step 201 can include: the first device can obtain at least one measurement data measured by the sensor, so that the measurement data obtained by the sensor can be a distance and an angle in the sensor coordinate system.

[0082] In another case, the distance and the angle measured by the first device through the sensor can be in the sensor coordinate system, and the first device can also convert the distance and the angle in the sensor coordinate system to the carrier coordinate system to obtain the distance and the angle in the carrier coordinate system. Thus, the measurement data obtained by the sensor can also be the distance and the angle in the carrier coordinate system.

[0083] In another case, the measurement data obtained by the sensor can be an image, for example, the sensor can be a photoelectric sensor, for example, the photoelectric sensor can be a camera or an event camera. Before step 201 is performed, the first device can also collect an image through the sensor. Step 201 can include: the first device obtains at least one measurement data according to the image collected by the sensor. For example, the first device can post-process the collected image to indirectly obtain the distance and the angle in the measurement data. It should be noted that the distance and the angle in the measurement data can be in the sensor coordinate system or in the carrier coordinate system.

[0084] Optionally, the first device can obtain the distance and the angle based on signal processing and / or computational vision or view geometry, etc. from the image collected by the sensor. Optionally, the first device can input the image collected by the sensor into a first machine learning model to obtain at least one measurement data output by the first machine learning model. For example, the first machine learning model can be a convolutional neural network, a residual neural network, a neural network based on an attention mechanism, a multiple layer perceptron (MLP), or another type of first machine learning model, which can be determined in combination with an actual application scenario.

[0085] For example, the first distance and the first angle can be a distance and an angle of a position on an object in the field of view of the sensor relative to a sensor coordinate system or a carrier coordinate system; that is, the first distance can be a distance between the position on the object in the field of view of the sensor and an origin of the sensor coordinate system, and the first azimuth angle can be an azimuth angle of the position relative to the sensor coordinate system; or the first distance can be a distance between the position on the object in the field of view of the sensor and an origin of the carrier coordinate system, and the first azimuth angle can be an azimuth angle of the position relative to the carrier coordinate system. The sensor coordinate system can be a spherical coordinate system or a rectangular coordinate system.

[0086] For example, if the first device is a vehicle and the carrier coordinate system is a vehicle body coordinate system, the origin of the vehicle body coordinate system can be located at the center of the rear axle of the vehicle body or the landing point of the center of the rear axle; if the first device is a mobile robot and the carrier coordinate system is a robot coordinate system, etc., the specific form of the carrier coordinate system can be determined in combination with the actual application scenario.

[0087] The first pitch angle can be a pitch angle of the position relative to the sensor coordinate system or the carrier coordinate system; for example, the first pitch angle can be an angle between a line connecting the position and an origin of the sensor coordinate system or the carrier coordinate system and a first plane, and the first plane can refer to an XOY plane of the sensor coordinate system or the carrier coordinate system, which can coincide with a ground plane on which the first device is located, or can be a plane parallel to the ground plane on which the first device is located, etc., and the specific form can be understood in combination with the actual application scenario.

[0088] 202. Determine, according to the first distance, a first parameter value for indicating an object occupancy of the first position.

[0089] For example, the first position can be a position in the field of view of the sensor.

[0090] For example, in an implementation, the first parameter value is obtained based on the first distance and third quality information in the measurement data, and the third quality information is used to reflect the quality of the distance in the measurement data, or the third quality information can also be understood as being used to reflect an error of the distance in the measurement data. Then, step 202 can include: determining, according to the first distance, the second distance and the third quality information, the first parameter value for indicating the object occupancy of the first position.

[0091] The second distance is a distance between the first position and an origin of the coordinate system; for example, if the coordinate system is a sensor coordinate system, the second distance can be a distance between the first position and an origin of the sensor coordinate system; if the coordinate system is a carrier coordinate system, the second distance can be a distance between the first position and an origin of the carrier coordinate system, and the carrier coordinate system can refer to the description above, and details are not described herein. Since the first parameter value is obtained based on the distance included in the measurement data, the first parameter value can also be understood as an object occupancy of the first position obtained based on the first distance; for example, the first parameter value can be understood as a probability that the first position is occupied by an object, and the first parameter value can be a first occupancy probability of the first position, and further, the first occupancy probability can be understood as a probability that the first position is occupied by an object obtained based on the first distance.

[0092] For example, the third quality information indicates a standard deviation and / or variance and / or root mean square error and / or mean square error of the distance measurement error. It should be noted that the distance measurement error in the present application can also be referred to as the error of the distance dimension in the measurement data. In the following, there can be two concepts of the error of the azimuth angle dimension in the measurement data and the error of the pitch angle dimension in the measurement data. The above two concepts will be described later, and details are not described herein.

[0093] Further, the third quality information can include a standard deviation and / or variance and / or root mean square error and / or mean square error reflecting the distance measurement error, or the third quality information can include an inverse of the standard deviation and / or variance and / or root mean square error and / or mean square error reflecting the distance measurement error, or the third quality information can include an inverse of the standard deviation and / or variance and / or root mean square error and / or mean square error reflecting the distance measurement error, or the third quality information can include other information capable of calculating the standard deviation and / or variance and / or root mean square error and / or mean square error reflecting the distance measurement error, and the present application does not exhaustively list them. It should be noted that the distance measurement in the present application not only includes the case of directly obtaining the distance through the information collected by the sensor, but also includes the case of indirectly obtaining the distance through processing the information collected by the sensor.

[0094] The standard deviation, variance, root mean square error, and mean square error can reflect the quality or measurement accuracy or error of the measurement data obtained by the sensor. Generally, the standard deviation, variance, root mean square error, and mean square error can reflect the dispersion degree of the distance measured by the sensor at the same position for multiple times, wherein multiple distance values can be obtained by the sensor at the same position for multiple times, and the greater the dispersion degree between the multiple distance values, the greater the distance measurement error; the smaller the dispersion degree between the multiple distance values, the smaller the distance measurement error.

[0095] Further, in the case that the sensor is an ultrasonic sensor, a laser radar sensor, a millimeter wave radar sensor, or other sensors capable of measuring point cloud data, etc., the third quality information may, for example, include one quality information, and the aforementioned one quality information may include a standard deviation and / or a variance for reflecting a distance measurement error; optionally, a root mean square error and / or a mean square error; it should be noted that the information collected by the sensor directly or indirectly obtained distance can be understood as measuring distance by the sensor, that is, the "distance measurement" in the present application includes the case of directly obtaining distance by the information collected by the sensor, and also includes the case of indirectly obtaining distance by the information collected by the sensor.

[0096] In the case that the sensor is a camera, since the measurement distance error of the camera increases as the distance between the photographed object and the camera increases, the third quality information may, for example, include one quality information; or the third quality information may also include at least two quality information corresponding to at least two distance ranges, and each of the aforementioned at least two quality information may include a standard deviation and / or a variance reflecting the distance error of the camera; optionally, a root mean square error and / or a mean square error. Then the first device can determine a target distance range containing the first distance from the at least two distance ranges, and then obtain one quality information corresponding to the target distance range from the at least two quality information, that is, one quality information corresponding to the first distance is obtained; and then determine the first parameter value according to the first distance, the second distance, and the quality information corresponding to the first distance.

[0097] For further understanding of the present scheme, for example, the at least two distance ranges include distance range 1, distance range 2, and distance range 3, the distance range 1 can be greater than or equal to 0 meters and less than 10 meters, the distance range 2 is greater than or equal to 10 meters and less than 30 meters, and the distance range 3 is greater than or equal to 30. Among them, the distance range 1 corresponds to the quality information 1, the quality information 1 includes the standard deviation 1 and the variance 1 reflecting the distance measurement error of the camera; the distance range 2 corresponds to the quality information 2, the quality information 2 includes the standard deviation 2 and the variance 2 reflecting the distance measurement error of the camera; the distance range 3 corresponds to the quality information 3, and the quality information 3 includes the standard deviation 3 and the variance 3 reflecting the distance measurement error of the camera. The first distance is 15 meters, since 15 meters is contained in the distance range 2, the first device can obtain the standard deviation 2 and the variance 2 included in the quality information 2, and then determine the first parameter value according to the first distance, the second distance, the standard deviation 2, and the variance 2, it should be understood that the example here is only for the convenience of understanding the present scheme, and is not used to limit the present scheme.

[0098] Further, in one case, the third quality information can be pre-stored in the first device, for example, the third quality information can be stored in the first device in the process that the sensor is deployed to the first device; alternatively, the first device can also update the aforementioned third quality information at intervals. In another case, the first device can obtain the third quality information through measurement before using the sensor, or through other manners, which are not exhaustively listed in the embodiments of the present application.

[0099] Alternatively, in one implementation, the first device can further obtain a first threshold value and a second threshold value, the first threshold value is set to avoid the value of the first parameter being equal to 0, and the second threshold value is set to avoid the value of the first parameter being equal to 1; the first parameter value can be determined based on the first distance, the third quality information, the first threshold value and the second threshold value in the measurement data according to a third function. For example, the first device can determine the first parameter value for indicating the object occupancy of the first position according to the first distance, the second distance and the third quality information, which can include: the first device brings the first distance, the second distance, the third quality information, the first threshold value and the second threshold value into the third function to obtain the first parameter value.

[0100] In the third function, the first function term corresponding to the first threshold value and the second threshold value, and the second function term corresponding to the first distance and the third quality information (or the quality information corresponding to the first distance) can be included, the second function term can be a monotonically increasing function, and the value range of the second function term can be from 0 to 1.

[0101] In another implementation, the first mapping relationship can be stored in the first device, and each of the plurality of sub-mapping relationships included in the first mapping relationship is a mapping relationship between a combination of the distance in the measurement data, the distance between the position in the field of view (FOV) of the sensor and the sensor, and the standard deviation and / or variance reflecting the distance measurement error, and the first parameter value. For example, the first mapping relationship can be in the form of a table, an array or other data format. For example, the first mapping relationship can be obtained based on experimental data, or obtained by a technician based on experience, or obtained by another device based on the third function, which is not limited herein. Then, the first device can obtain one first parameter value corresponding to the combination of the first distance, the second distance and the standard deviation and / or variance based on the third quality information from the first mapping relationship based on the first distance, the second distance and the third quality information in the measurement data.

[0102] Optionally, a relationship between the second distance and the first parameter value obtained based on the first distance in the measurement data and the third quality information can be shown in the following formula:

[0103]

[0104] wherein p r (r) is the first occupancy probability of the first position, r is the second distance; lb and ub are two preset threshold values, lb is the lower limit of p r (r), which can be a first threshold value set to avoid p r (r) taking a value equal to or less than 0, and ub is the upper limit of p r (r), which can be a second threshold value set to avoid p r (r) taking a value equal to or greater than 1, for example, the value of lb can be 0.1, 0.15, 0.2 or other values, and for example, the value of ub can be 0.8, 0.85, 0.9 or other values, which can be set in combination with actual application scenarios; σ r contained in the third quality information can be a standard deviation or a root mean square error for reflecting the measurement error of the sensor in the distance dimension. f r = erf(·), that is, f r can be an error function, or f r = 2·sigmoid(·)-1, that is, f r can be obtained based on an activation function, wherein sigmoid(·) is an activation function.

[0105] In one case, L r is a preset parameter value, for example, the value of L r can be 0.1, 0.15, 0.2 or other values. In another case, L r is a function term constructed based on the standard deviation of the sensor in the distance dimension, for example, or L r = σ r , or , which can be set in combination with actual application scenarios.

[0106] r m is the first distance included in the measurement data, σ 2 is a variance or a root mean square error for reflecting the error of the distance in the measurement data, which can also be referred to as a variance or a root mean square error indicating the error of the measurement data; ln represents taking a natural logarithm. It should be understood that the above examples of formula (1) and formula (2) are only one example to prove the feasibility of the present application, and are not used to limit the present solution.

[0107] For a more intuitive understanding of this solution, please refer to [link / reference]. Figure 3 and Figure 4 , Figure 3 This is a schematic diagram of a second function term provided in an embodiment of this application. Figure 4 Several schematic diagrams illustrating the first occupancy probability provided in the embodiments of this application are provided below. Figure 3 , Figure 3 The second function term shown is a monotonically increasing function, and its value range is from 0 to 1. It should be understood that... Figure 3 The examples in this document are for illustrative purposes only and are not intended to limit the scope of this solution.

[0108] Continue reading Figure 4 , Figure 4 The horizontal axis represents the normalized value of the second distance (normalized using the standard deviation or mean square root error reflecting the distance measurement error), where the second distance is the distance between a certain location and the sensor. Figure 4 The vertical axis represents the initial occupancy probability of an object at that location. Figure 4 The three lines shown all take the relationship between the second distance and the first occupancy probability (i.e., an example of the first parameter value) as an example that satisfies the relationship in the above formula (1). The difference between the three lines is that they correspond to L respectively. r The different values ​​of L correspond to the three lines from top to bottom. r The values ​​are respectively σ r as well as like Figure 4 As shown, the position corresponding to the value 50 on the horizontal axis has the highest probability of being occupied by an object. Specifically, compared to the position corresponding to the value 50 on the horizontal axis, the probability of an object being occupied at positions closer to the sensor gradually decreases, eventually approaching 0.1; compared to the position corresponding to the value 50 on the horizontal axis, the probability of an object being occupied at positions farther from the sensor gradually decreases, eventually approaching 0.5. Since if an object (hereinafter referred to as the "target object" for ease of distinction) exists at the position corresponding to the value 50 on the horizontal axis, objects behind the target object will be occluded. Therefore, the probability of an object being occupied at positions farther from the sensor gradually approaches 0.5 (i.e., uncertain). Since the sensor has detected the target object, it means that there are no other objects occupying the position closer to the sensor, so the probability of an object being occupied at positions farther from the sensor gradually approaches 0.1 (i.e., no object). It should be understood that... Figure 4 The examples in this document are for illustrative purposes only and are not intended to limit the scope of this solution.

[0109] In another implementation, the first device can also input the first distance, the second distance, and the standard deviation and / or variance of the sensor in the dimension of measuring distance into the second machine learning model, and obtain the first parameter value output by the second machine learning model.

[0110] For example, the second machine learning model can be a convolutional neural network, a support vector machine, a multilayer perceptron (MLP), a neural network based on an attention mechanism, or other types of machine learning models, and the specific type can be determined in combination with the actual application scenario.

[0111] In the embodiments of the present application, the object occupancy of a certain position in the dimension of distance (i.e., the first parameter value) is determined based on the first distance in the measurement data and the third quality information, which can accurately reflect the error of the distance in the measurement data obtained by the sensor, so that the first parameter value obtained can fully consider the influence of the error of the sensor when measuring distance, which is not only conducive to obtaining a more accurate first parameter value, and thus conducive to obtaining a more accurate object occupancy; but also conducive to making the present scheme self-adaptive to different specifications in the dimension of the error when measuring distance, i.e., conducive to making the present scheme applicable to more sensors, and conducive to expanding the application range of the present scheme.

[0112] In another implementation, the first device can store a second mapping relationship, and each of the plurality of sub-mapping relationships included in the second mapping relationship is a mapping relationship between a combination of the distance of the measurement data and the distance between the position within the field of view of the sensor and the sensor and the first parameter value. The data format and source of the second mapping relationship can be referred to the description of the first mapping relationship above, which will not be repeated here. After determining the first distance in the measurement data and the second distance between the first position and the sensor, the first device can obtain the first parameter value corresponding to the combination of the first distance and the second distance from the second mapping relationship.

[0113] Alternatively, the first device can input the first distance and the second distance into a third machine learning model to obtain the first parameter value output by the third machine learning model. The specific form of the third machine learning model can be referred to the description of the second machine learning model above, which will not be repeated here.

[0114] 203、determine an angle adjustment factor corresponding to the first position according to the first angle.

[0115] The first angle includes a first azimuth angle and / or a first elevation angle. The first azimuth angle is used to determine the first angle adjustment factor, and the second azimuth angle is used to determine the second angle adjustment factor. The first angle adjustment factor and / or the second angle adjustment factor is used to determine the angle adjustment factor, which is used to adjust the first parameter value to obtain a second parameter value corresponding to the first position. The second parameter value indicates the object occupancy of the first position determined based on the measurement data. The angle adjustment factor indicates the influence of the angle dimension on the object occupancy of the first position. The angle adjustment factor in the present application can also be referred to as an angle weight factor or other names.

[0116] In one case, the angle adjustment factor corresponding to the first position is the first angle adjustment factor if the first angle only includes the first azimuth angle. In another case, the angle adjustment factor corresponding to the first position is the second angle adjustment factor if the first angle only includes the first elevation angle. In another case, the angle adjustment factor corresponding to the first position can be the product of the first angle adjustment factor and the second angle adjustment factor if the first angle includes the first azimuth angle and the first elevation angle. Alternatively, the angle adjustment factor corresponding to the first position can be the average of the first angle adjustment factor and the second angle adjustment factor. Alternatively, the angle adjustment factor corresponding to the first position can be the minimum or maximum value between the first angle adjustment factor and the second angle adjustment factor.

[0117] For the specific implementation of determining the first angle adjustment factor by the first device according to the first azimuth angle, the first device can determine the first angle adjustment factor corresponding to the first position according to the first azimuth angle and a second azimuth angle. The second azimuth angle is the azimuth angle of the first position relative to the sensor coordinate system or the carrier coordinate system. Alternatively, the second azimuth angle can be the azimuth angle of the first position relative to the sensor coordinate system, or the azimuth angle of the first position relative to the carrier coordinate system. Alternatively, the smaller the absolute value of the difference between the second azimuth angle and the first azimuth angle, the larger the value of the first angle adjustment factor. The larger the absolute value of the difference between the second azimuth angle and the first azimuth angle, the smaller the value of the first angle adjustment factor.

[0118] Optionally, the first angle adjustment factor is determined based on the first azimuth angle and first quality information, the first quality information being used to reflect the quality of the azimuth angle in the measurement data, or it can also be understood that the first quality information is used to reflect the error of the azimuth angle in the measurement data. The first device determines the first angle adjustment factor corresponding to the first position according to the first azimuth angle can comprise: the first device determines the first angle adjustment factor according to the first azimuth angle, the second azimuth angle and the first quality information.

[0119] Exemplarily, the first quality information indicates the standard deviation and / or variance and / or root mean square error and / or mean square error of the azimuth angle measurement error. It needs to be explained that the "azimuth angle measurement error" in the present application can also be replaced by "error of the azimuth angle in the measurement data".

[0120] Further, the first quality information comprises the standard deviation and / or variance and / or root mean square error and / or mean square error of the azimuth angle measurement error, or the first quality information comprises the inverse of the standard deviation and / or variance and / or root mean square error and / or mean square error of the azimuth angle measurement error, or the first quality information comprises the inverse of the standard deviation and / or variance and / or root mean square error and / or mean square error of the azimuth angle measurement error, or the first quality information can also comprise other information capable of calculating the standard deviation and / or variance and / or root mean square error and / or mean square error of the azimuth angle measurement error, etc. The present application does not make an exhaustive list. It needs to be explained that the "azimuth angle measurement" in the present application not only includes the case of directly obtaining the azimuth angle through the information collected by the sensor, but also includes the case of indirectly obtaining the azimuth angle through processing the information collected by the sensor.

[0121] Generally, the standard deviation and / or variance and / or root mean square error and / or mean square error of the azimuth angle measurement error can reflect the dispersion degree of the azimuth angle of the sensor when measuring the same position for multiple times. The sensor can obtain multiple azimuth angles when measuring the same position for multiple times. If the dispersion degree of the multiple azimuth angles is larger, it represents that the azimuth angle measurement error is larger. If the dispersion degree of the multiple azimuth angles is smaller, it represents that the azimuth angle measurement error is smaller.

[0122] Further, in one case, the first quality information can be pre-stored in the first device, for example, the first quality information can be stored in the first device in the process of deploying the sensor to the first device. Optionally, the first device can also update the first quality information once every period of time. In another case, the first device can obtain the first quality information through measurement before using the sensor, and the first quality information can also be obtained through other manners. Or, the first quality information can be estimated in real time and provided one by one corresponding to the measurement data, and the present application embodiments do not make an exhaustive list.

[0123] Specifically, in an implementation, the first angle adjustment factor is determined based on a first function according to the first azimuth angle, the second azimuth angle and the first quality information; wherein the first function is a Gaussian function, or the first function is obtained based on an error (erf) function, or the first function is obtained based on a certain activation function (such as a sigmoid function); for example, the first function can be an axisymmetric function. Illustratively, the first device can take the first azimuth angle and the standard deviation and / or variance and / or root mean square error and / or mean square error reflecting the azimuth angle measurement error as parameters of the first function, so as to obtain the first angle adjustment factor corresponding to the second azimuth angle based on the first function.

[0124] In another implementation, the first device can store a third mapping relationship, and each of the plurality of sub-mapping relationships included in the third mapping relationship is a mapping relationship between a combination of the azimuth angle in the measurement data, the azimuth angle of the position within the sensor field of view relative to the coordinate system, and the standard deviation and / or variance or root mean square error or mean square error reflecting the azimuth angle measurement error, and the first angle adjustment factor; the aforementioned coordinate system can be a sensor coordinate system or a carrier coordinate system; the data format and source adopted by the third mapping relationship can refer to the description of the first mapping relationship above, which will not be repeated here. The first device can obtain a first angle adjustment factor corresponding to the combination of the first azimuth angle in the measurement data, the second azimuth angle of the first position relative to the coordinate system, and the standard deviation and / or variance and / or root mean square error and / or mean square error based on the first quality information from the third mapping relationship.

[0125] Optionally, based on the first azimuth angle in the measurement data, the relationship between the second azimuth angle and the first angle adjustment factor can be shown in the following formula:

[0126]

[0127] wherein F θ (θ) is the first angle adjustment factor; f θ is an error function, that is, f θ = erf(·), or f θ is obtained based on a certain activation function, that is, f θ = 2·sigmoid(·)-1; the domain of sigmoid(·) is real number, and sigmoid(·) is a certain activation function whose function value monotonically increases from 0 to 1, θ is the second azimuth angle corresponding to the first position; θ m is the first azimuth angle in the measurement data, and σ θ is the standard deviation of the azimuth angle measurement error of the sensor.

[0128] In one case, L θ is a preset parameter value, for example, L θ may be 0.6, 0.1 or other values, etc. In another case, L θ is a function term constructed based on the standard deviation of the azimuth angle measurement error of the sensor, for example, or, L θ = σ θ , or, or, L θ = 2σ θ , or, L θ = 3σ θ , etc. It should be understood that the examples in formula (3) are only for the convenience of understanding the scheme and are not used to limit the scheme.

[0129] For a more intuitive understanding of the scheme, please refer to Figure 5 , Figure 5 Some schematic diagrams of the first function provided by the embodiments of the present application are shown in Figure 5 , which shows four examples of the first function. In the four examples, the first function is a single-peak function with axial symmetry. The four examples include: an example in which the first function adopts a Gaussian function and three examples in which the first function is obtained based on an activation function. Figure 5 The three examples in which the first function is obtained based on an activation function shown in are all based on the formula shown in the above formula (3) as an example. The difference between the three examples is that the value of L θ is different. In the three examples, the value of L θ is σ θ and It should be understood that the examples in Figure 5 are only for the convenience of understanding the scheme and are not used to limit the scheme.

[0130] Alternatively, the fourth mapping relationship can also be stored in the first device. Each of the plurality of sub-mapping relationships included in the fourth mapping relationship is a mapping relationship between a combination of the pitch angle in the measurement data and the pitch angle corresponding to the position within the field of view of the sensor and the first angle adjustment factor. The data format and source used by the fourth mapping relationship can be referred to the description of the first mapping relationship above, and will not be described here. The first device can obtain the first angle adjustment factor corresponding to the combination of the first pitch angle and the second pitch angle from the fourth mapping relationship.

[0131] Alternatively, the first device can input the first pitch angle and the second pitch angle into a fourth machine learning model, and obtain a first angle adjustment factor output by the fourth machine learning model. The specific form of the fourth machine learning model can refer to the description of the second machine learning model above, and will not be described here.

[0132] For the specific implementation manner of determining the second angle adjustment factor according to the first pitch angle, the first device can determine the second angle adjustment factor corresponding to the first position according to the first pitch angle and a second pitch angle, where the second pitch angle is the pitch angle of the first position relative to the sensor coordinate system or the carrier coordinate system. For example, the second pitch angle can be the angle between the line connecting the first position and the origin of the sensor coordinate system or the carrier coordinate system and the first plane. The meaning of the first plane can refer to the description above, and will not be described here. Optionally, the smaller the absolute value of the difference between the second pitch angle and the first pitch angle, the larger the value of the second angle adjustment factor can be; the larger the absolute value of the difference between the second pitch angle and the first pitch angle, the smaller the value of the second angle adjustment factor can be.

[0133] Optionally, the second angle adjustment factor is determined based on the first pitch angle and second quality information, and the second quality information is used to reflect the quality of the pitch angle in the measurement data obtained by the sensor, which can also be referred to as the second quality information being used to reflect the error of the pitch angle in the measurement data obtained by the sensor.

[0134] For example, the second quality information indicates the standard deviation and / or variance and / or root mean square error and / or mean square error of the pitch angle measurement error. It should be noted that the "pitch angle measurement error" in the present application can also be referred to as "error of the pitch angle in the measurement data".

[0135] Further, the second quality information comprises a standard deviation and / or a variance and / or a root mean square error and / or a mean square error reflecting the pitch angle measurement error, or the second quality information comprises an inverse of a standard deviation and / or a variance and / or a root mean square error and / or a mean square error reflecting the pitch angle measurement error, or the second quality information comprises an inverse of a standard deviation and / or a variance and / or a root mean square error and / or a mean square error reflecting the pitch angle measurement error, or the second quality information can further comprise other information capable of calculating a standard deviation and / or a variance and / or a root mean square error and / or a mean square error reflecting the pitch angle measurement error, and the like, which will not be listed herein. It should be noted that the "pitch angle measurement" in the present application not only includes the case of directly obtaining the pitch angle through the information collected by the sensor, but also includes the case of indirectly obtaining the pitch angle through processing the information collected by the sensor.

[0136] Optionally, the second angle adjustment factor is determined based on a second function according to the first pitch angle, the second pitch angle and the second quality information, wherein the second function is a Gaussian function, or the second function is based on an error function, or the second function is based on an activation function, the second function can be an axisymmetric function, and the shape of the second function is similar to the shape of the first function shown in the above Figure 5 Optionally, the first device can take the first pitch angle and a standard deviation and / or a variance and / or a root mean square error and / or a mean square error reflecting the pitch angle measurement error as parameters of the second function to obtain the second angle adjustment factor corresponding to the first pitch angle.

[0137] It should be noted that the specific implementation manner of the first device for determining the second angle adjustment factor according to the first pitch angle can refer to the description of the specific implementation manner of the first device for determining the first angle adjustment factor according to the first pitch angle, and the difference lies in that the pitch angle in the above description is replaced by the pitch angle, the first angle adjustment factor in the above description is replaced by the second angle adjustment factor, and the first function in the above description is replaced by the second function, which will not be described herein.

[0138] Optionally, the relationship between the first pitch angle and the second angle adjustment factor can be shown in the following formula:

[0139]

[0140] wherein, the second angle adjustment factor is the second pitch angle is the error function is or, based on a certain activation function, and exemplarily, the second pitch angle corresponding to the first position is a first pitch angle in the measurement data, a standard deviation or a root mean square error of the pitch angle measurement error.

[0141] In one case, is a preset parameter value, for example, The value of can be 0.9, 0.6, 0.1 or other values, etc. In another case, is a function term constructed based on the standard deviation reflecting the pitch angle measurement error, for example, or, or, or, or, etc. It should be understood that the examples in formula (4) are only for the convenience of understanding the scheme and do not limit the scheme.

[0142] In the embodiments of the present application, the first azimuth angle in the measurement data and the first quality information are used to determine the first angle adjustment factor in the azimuth angle dimension, and the first quality information reflects the error of the azimuth angle in the measurement data obtained by the sensor, so that the first angle adjustment factor obtained can fully consider the influence of the error when the azimuth angle is measured by the sensor. Not only is it beneficial to obtain more accurate angle adjustment factors, and then to obtain more accurate object occupancy; it is also beneficial to make the scheme adaptive to different specifications in the error dimension when measuring the azimuth angle, that is, it is beneficial to make the scheme applicable to more sensors, and it is beneficial to expand the application range of the scheme.

[0143] The first pitch angle in the measurement data and the second quality information are used to determine the first angle adjustment factor in the pitch angle dimension, and the second quality information reflects the error of the pitch angle in the measurement data obtained by the sensor, so that the second angle adjustment factor obtained can fully consider the influence of the error when the pitch angle is measured by the sensor. Not only is it beneficial to obtain more accurate angle adjustment factors, and then to obtain more accurate object occupancy; it is also beneficial to make the scheme adaptive to different specifications in the error dimension when measuring the pitch angle, that is, it is beneficial to make the scheme applicable to more sensors, and it is beneficial to expand the application range of the scheme.

[0144] In addition, it is further disclosed that the first angle adjustment factor and the second angle adjustment factor are determined based on a function, which improves the realizability of the scheme, and the first function (or the second function) is a Gaussian function, or is obtained based on an error function, or is obtained based on a certain activation function, which provides a simple and effective implementation scheme.

[0145] It should be noted that the execution order between step 202 and step 203 in the embodiments of the present application is not limited, and step 202 can be executed first, and then step 203 can be executed, or step 203 can be executed first, and then step 202 can be executed, or step 202 and step 203 can be executed simultaneously.

[0146] 204、According to the first parameter value and the angle adjustment factor, the second parameter value of the first position is determined.

[0147] Wherein, since the second parameter value is obtained based on the distance and the angle included in the measurement data, the second parameter value can also be understood as the object occupancy of the first position based on the first distance and the first angle.

[0148] Exemplarily, in one case, the second parameter value can represent the probability that the first position is occupied by an object, and the first parameter value can be the first occupancy probability of the first position, the first occupancy probability refers to the probability that the first position is occupied by an object based on the distance in the measurement data, and the second occupancy probability refers to the probability that the first position is occupied by an object based on the comprehensive determination of the distance and the angle in the measurement data. In another case, the second parameter value can be a parameter value obtained by transforming the second occupancy probability, for example, the second parameter value can be a ratio equivalent form, represented as: odd=p r,a (r,a) / (1-p r,a (r,a)), wherein odd is the second parameter value, p r,a (r,a) is the second occupancy probability; for another example, the second parameter value can be a logarithmic equivalent form, represented as: log-odd=ln*p r,a (r,a) / (1-p r,a (r,a)+, wherein log-odd is the second parameter value, p r,a (r,a) is the second occupancy probability, and the second parameter value can also be obtained by other transformations of the second occupancy probability, for example, it can be 1-p r,a (r,a), that is, the free probability, and correspondingly, there can be a corresponding ratio or logarithmic ratio form; or other ways to derive the equivalent form, which generally satisfies a one-to-one mapping relationship or satisfies a one-to-one mapping relationship within a specified range, and the specific transformation method can be determined in combination with the actual application scenario.

[0149] Optionally, the free probability of the first position can also be obtained by calculating 1-p r,a (r,a), p r,a (r,a) is the second occupancy probability.

[0150] Optionally, in one case, the second occupancy probability satisfies the following relationship with the first occupancy probability and the angle adjustment factor:

[0151] p r,a (r,a)=c0+(p r (r)-c1)·F(a); (5)

[0152] Where, p r,a (r,a) represents the second occupancy probability, p r (r) is the first occupancy probability, F(a) is the angle adjustment factor, c0 is the first preset value, and c1 is the second preset value. The values ​​of c0 and c1 can be the same or different, and can be determined in combination with the actual application scenario.

[0153] Optionally, the values ​​of c0 and c1 are both 0.5, that is, the values ​​of the first preset value and the second preset value are both 0.5. It should be noted that the first preset value and the second preset value can also be other values, such as c0 being 0.499 and c1 being 0.501, etc., which can be set according to the actual application scenario.

[0154] For a more intuitive understanding of this solution, please refer to [link / reference]. Figure 6 , Figure 6 Two schematic diagrams illustrating the second parameter values ​​obtained using the method provided in this application, as provided in the embodiments of this application. Figure 6 Includes two sub-diagrams, left and right, in which, Figure 6 The left sub-schematic diagram includes three axes: the distance to a certain position, the azimuth angle to that position, and the second occupancy probability of that position being occupied by an object. Figure 6 The right sub-schematic diagram includes three axes: the x-axis coordinate corresponding to a certain position, the y-axis coordinate corresponding to that position, and the second occupancy probability of that position being occupied by an object, that is... Figure 6 The left and right child diagrams are represented in different ways. For example... Figure 6 As shown, the second occupancy probability is highest at the location with a distance of 50 and an azimuth angle of 0, and the second occupancy probability is also relatively high at locations near the aforementioned locations. Compared to the aforementioned locations, the second occupancy probability is very low at locations that are even closer and also have an azimuth angle of 0. Since the location with a distance of 50 and an azimuth angle of 0 can be observed by the sensor, it means that there are likely no obstructions at locations that are even closer and also have an azimuth angle of 0. Figure 6The second occupancy probability calculated in the middle is as expected; the second occupancy probability of the remaining positions is 0.5, and the second occupancy probability of 0.5 represents uncertainty about whether an object exists at the position, which is also as expected. In addition, it should be particularly pointed out that, as can be seen from the figure, as the distance error standard deviation or the angle error standard deviation increases, the corresponding actual space area of the occupancy probability will expand accordingly, thereby being able to adapt to different sensor error specifications and accurately reflect the uncertainty range of the occupancy probability. Therefore, the second occupancy probability obtained by using the method provided in the present application can accurately reflect the object occupancy situation in the sensor field of view, and it should be understood that Figure 5 The examples in the above merely serve to facilitate the understanding of the present application and are not used to limit the present application.

[0155] Exemplarily, in an implementation manner, the step 204 can include that the first device takes the product of the difference between the first parameter value and the first preset value (i.e., the above-mentioned c1) and the angle adjustment factor, and takes the sum of the product and the second preset value (i.e., the above-mentioned c0) as the second parameter value.

[0156] In another implementation manner, the first device can store a fifth mapping relationship, and each of the plurality of sub-mapping relationships included in the fifth mapping relationship is a mapping relationship between a combination of the occupancy probability based on the distance in the measurement data and the angle adjustment factor based on the angle in the measurement data and the second parameter value. The second parameter value and the first occupancy probability and the angle adjustment factor in the third mapping relationship can satisfy the relationship shown in the above-mentioned formula (5). The step 204 can include that, after determining one first occupancy probability and one specific angle adjustment factor, the first device can obtain, from the fifth mapping relationship, the second parameter value corresponding to the combination of the one first occupancy probability and the one specific angle adjustment factor.

[0157] For a more intuitive understanding of the present application, the fifth mapping relationship is described in the form of a table as follows. Please refer to Table 1 as follows.

[0158] Table 1

[0159]

[0160] As shown in Table 1 as follows, four rows of data are taken as examples for description, and each row of data represents a sub-mapping relationship in the fifth mapping relationship, so that after obtaining one specific first occupancy probability and one specific angle adjustment factor, a second parameter value can be obtained in the form of table lookup. It should be noted that the examples in Table 1 merely serve to facilitate the understanding of the present application and are not used to limit the present application.

[0161] In another implementation, the first device can obtain first rectangular coordinate data based on measurement data, and acquire second rectangular coordinate data corresponding to the first position. Both the first and second rectangular coordinate data are coordinate data in a rectangular coordinate system. The rectangular coordinate data includes the x-axis and y-axis coordinates of a position relative to the rectangular coordinate system. Optionally, the rectangular coordinate data also includes the z-axis coordinate of a position relative to the rectangular coordinate system. This rectangular coordinate system can be a sensor coordinate system or a carrier coordinate system. The first device can store a sixth mapping relationship. Each of the multiple sub-mapping relationships in the sixth mapping relationship is a second parameter value corresponding to the combination of the rectangular coordinate data obtained based on the measurement data and the rectangular coordinate data of a position within the sensor's field of view. The first device can then obtain a second parameter value corresponding to the combination of the aforementioned first and second rectangular coordinate data from a fifth mapping relationship.

[0162] In this application embodiment, the relationship satisfied by the second occupancy probability, the first occupancy probability, and the angle adjustment factor is disclosed, improving the feasibility of this solution; and through the above... Figure 3 Based on the experimental data shown, the second occupancy probability obtained by the method provided in this application is as expected. That is, the closer the relative position between the first position and the sensor is to the measurement data, the greater the occupancy probability of the object at the first position. Since the solution provided in this application no longer needs to model the relationship between the distance in the measurement data and the idle probability at the first position, the implementation difficulty of this solution is greatly reduced.

[0163] Further, the values of c0 and c1 are further disclosed, which further improves the realizability of the scheme, and the product of the difference between the first occupancy probability and 0.5 and the angle adjustment factor is added to 0.5 to obtain the final second occupancy probability, and the final value of the second occupancy probability fluctuates on the basis of 0.5, the occupancy probability of the area outside the predetermined range of the position corresponding to the measurement data and close to the sensor will be lower than 0.5, and will tend to a minimum value as the distance difference and the angle difference become smaller, which meets the expectation that the occupancy state tends to be idle; in addition, the occupancy probability of the position outside the predetermined range of the position corresponding to the measurement data and far from the sensor will tend to 0.5, since the occupancy probability of a certain position is 0.5, it indicates that it is completely uncertain whether the position is occupied by an object (the occupancy probability is 0.5, and the idle probability is also 0.5), therefore, it meets the expectation of uncertainty; more importantly, the occupancy probability of the position corresponding to the measurement data is the highest, and the occupancy probability of the positions within the specified range near the position corresponding to the measurement data gradually decreases, which meets the expectation that the occupancy probability of the position corresponding to the measurement data is the highest, since the occupancy probability of the positions within the specified range near the position corresponding to the measurement data is generated depending on the measurement error, then it is also expected that the occupancy probability of the positions within the specified range near the position corresponding to the measurement data gradually decreases. Based on the measurement data obtained by the sensor, the probability that the position is occupied by an object can be improved or reduced, and the design that the values of c0 and c1 are both 0.5 is more conducive to making the final second occupancy probability meet the aforementioned expectations, so as to be conducive to obtaining a more accurate second occupancy probability of a certain position through the formula, and to more accurately determine the object occupancy of the position.

[0164] Alternatively, the second occupancy probability can also be the product between the first occupancy probability and the angle adjustment factor, and the relationship between the second occupancy probability and the first occupancy probability and the angle adjustment factor can be determined in combination with the actual application scenario.

[0165] In the embodiments of the present application, when each set of measurement data is obtained by the sensor, not only the distance but also the azimuth angle and / or the pitch angle are obtained, so that not only the first parameter value for indicating the object occupancy of the first position is determined according to the obtained distance, but also an angle adjustment factor is determined according to the aforementioned azimuth angle and / or the pitch angle, and then the second parameter value of the first position is finally determined according to the first parameter value and the aforementioned angle adjustment factor, and the second parameter value indicates the object occupancy of the first position. Since more information is used to determine the object occupancy of the first position, the accuracy of the finally obtained second parameter value is improved. Moreover, the first parameter value for indicating the object occupancy of the first position is determined based on the distance, and the angle adjustment factor is determined according to the azimuth angle and / or the pitch angle, that is, the influence of the distance and the angle on the object occupancy of the first position is determined respectively and independently, and the first parameter value is adjusted based on the angle adjustment factor to determine the final object occupancy of the first position, which improves the fineness of the process of determining the object occupancy of the first position and further improves the accuracy of the finally obtained second parameter value.

[0166] In Figure 7 Based on the corresponding embodiments, optionally, in one application scenario, the first device obtains at least one measurement data by the sensor, and the at least one measurement data is used to obtain a map corresponding to the field of view range of the sensor, and each measurement data is used to obtain the second parameter value of one or more first positions in the map. In another application scenario, when the first device needs to determine the object occupancy at a specific first position, a plurality of measurement data are obtained by the sensor, and then the object occupancy at the specific first position is determined based on the aforementioned plurality of measurement data. Since the specific implementation manners in the aforementioned two application scenarios are different, they are described as follows.

[0167] I. The plurality of measurement data is used to obtain a map corresponding to the field of view range of the sensor

[0168] Exemplarily, please refer to Figure 7 , Figure 7 Another schematic diagram of the method for determining the occupancy of the position provided in the embodiments of the present application is shown in FIG. 7, and the method for determining the occupancy of the position provided in the present application can include the following steps. Figure 2

[0169] 701. Obtain at least one measurement data obtained by the sensor, and each measurement data includes a first distance and a first angle, the first angle includes a first azimuth angle and / or a first pitch angle, and the target measurement data is any one of the aforementioned at least one measurement data.

[0170] Exemplarily, in order to obtain a map corresponding to the field of view range of the sensor, the first device can obtain at least one measurement data by the sensor, and the specific implementation manner of the aforementioned step can be referred to​Figure 2 The description in step 201 in the corresponding embodiment is not repeated here.

[0171] The map is used to reflect the object occupancy of the environment within the sensor field of view, and the map can include a plurality of positions and the object occupancy of each position in the plurality of positions.

[0172] Optionally, the above-mentioned map can be specifically expressed as a grid map (GM), and the positions in the map can be expressed as grid cells in the grid map. The grid map is a kind of grid representation of the surrounding environment. For example, the grid map can include a plurality of grids and a second parameter value of each grid. Optionally, the second parameter value of each grid can be understood as the probability of each grid being occupied by an object. For example, the environment within the sensor field of view can be gridded. If a certain grid is occupied by an object, the grid is filled with color. If a certain grid is not occupied by an object, the grid is not filled with color.

[0173] For example, different colors can be filled in the grid to represent different occupancy probabilities of the grid. For example, a red grid represents a high occupancy probability of the grid, and a blue grid represents a low occupancy probability of the grid. Alternatively, different shades of the same color can be used to represent different occupancy probabilities of the grid. For example, the grid is filled with red, which represents that the grid is occupied. The deeper the red color, the higher the occupancy probability of the grid. The lighter the red color, the lower the occupancy probability of the grid, and so on. The specific implementation mode can be determined in combination with the actual application scenario.

[0174] For example, the grid map used to represent the environment within the sensor field of view can be a two-dimensional rectangular coordinate grid map. The two-dimensional grid map can include an x-axis and a y-axis. The plane formed by the x-axis and the y-axis can be parallel to the ground plane. The two-dimensional grid map can represent the environment within the sensor field of view from a top-down perspective. Alternatively, the grid map used to represent the environment within the sensor field of view can be a three-dimensional rectangular coordinate grid map. The three-dimensional grid map can include an x-axis, a y-axis, and a z-axis. The plane formed by the x-axis and the y-axis can be parallel to the ground plane. The z-axis can represent the height. The three-dimensional grid map can represent the surrounding environment from a three-dimensional perspective.

[0175] For example, the grid map used to represent the environment within the sensor field of view can be a two-dimensional polar coordinate (distance and azimuth angle representation) grid map or a three-dimensional spherical coordinate (distance, azimuth angle, and elevation angle representation) grid map, which is not further described.

[0176] It should be understood that the map can also not be a grid map, for example, instead of a regularly or irregularly discrete-valued position as described above, it can be a continuously-valued different position, and the specific type of map can be determined in combination with the actual application scenario.

[0177] 702、determining at least one first position corresponding to the target measurement data, wherein each first position is a position within the field of view of the sensor.

[0178] Exemplarily, after obtaining the target measurement data, the first device can determine at least one first position corresponding to the target measurement data. In one case, the field of view of the sensor is divided into a plurality of positions, the map includes the plurality of positions, and the at least one first position includes the plurality of positions, that is, the first device needs to determine the second occupancy probability of each position within the field of view of the sensor based on the target measurement data in the subsequent step.

[0179] In another case, each of the at least one first position satisfies a preset condition, and the preset condition includes at least one of the following: an absolute value of a difference between a second distance corresponding to the first position and a first distance is less than or equal to a distance threshold value, and / or an absolute value of a difference between a second azimuth angle corresponding to the first position and a first azimuth angle is less than or equal to a first angle value, and / or an absolute value of a difference between a second pitch angle corresponding to the first position and a first pitch angle is less than or equal to a second angle value. Wherein the second distance corresponding to the first position can be a distance between the first position and an origin of a sensor coordinate system or a carrier coordinate system, the second azimuth angle corresponding to the first position can be an azimuth angle of the first position relative to the sensor coordinate system or the carrier coordinate system, and the second pitch angle corresponding to the first position can be a pitch angle of the first position relative to the sensor coordinate system or the carrier coordinate system. It should be noted that the first distance, the second distance, the first azimuth angle, the second azimuth angle, the first pitch angle, and the second pitch angle are all relative to data in the same coordinate system.

[0180] For example, any one measurement data is called measurement data 1, and the map is specifically a grid map, the entire grid map includes 200 grids, and the at least one first position corresponding to the measurement data 1 can include grid 1, grid 2, grid 3, grid 4, and grid 5, wherein the aforementioned grid 1, grid 2, grid 3, grid 4, and grid 5 are screened from the 200 grids by using the preset condition, and then the measurement data 1 is used to determine the second parameter value of each grid in the grid 1, grid 2, grid 3, grid 4, and grid 5. It should be understood that the example is only for the convenience of understanding the scheme and is not used to limit the scheme.

[0181] Further, in one case, the distance threshold is determined based on a standard deviation reflecting a distance measurement error, the first angle value is determined based on a standard deviation reflecting an azimuth angle measurement error, and the second angle value is determined based on a standard deviation reflecting a pitch angle measurement error.

[0182] Exemplarily, the distance threshold can take a product between a standard deviation reflecting a distance measurement error and a first multiple, for example, the first multiple can be 1.5 times, 2 times, 2.5 times, 2.6 times, 2.8 times, 3 times, 4 times, or other values, etc., which can be determined in combination with actual application scenarios.

[0183] The first angle value can take a product between a standard deviation reflecting an azimuth angle measurement error and a second multiple, for example, the second multiple can be 2 times, 2.5 times, 2.6 times, 2.8 times, 3 times, 3.5 times, or other values, etc., which can be determined in combination with actual application scenarios. The second angle value can take a product between a standard deviation reflecting a pitch angle measurement error and a third multiple, the third multiple and the second multiple can be the same or different, for example, the third multiple can be 2 times, 2.5 times, 2.6 times, 2.8 times, 3 times, 3.5 times, or other values, etc., which can be determined in combination with actual application scenarios.

[0184] In another case, the distance threshold is determined based on a root mean square error reflecting a distance measurement error, the first angle value is determined based on a root mean square error reflecting an azimuth angle measurement error, and the second angle value is determined based on a root mean square error reflecting a pitch angle measurement error.

[0185] In another case, the distance threshold, the first angle value, and / or the second angle value can also be preset values, for example, the distance threshold can be 3 meters, 5 meters, 8 meters, 10 meters, or other values, etc.; the first angle value can be 15 degrees, 18 degrees, 20 degrees, 25 degrees, or other values, etc.; the second angle value can be 12 degrees, 15 degrees, 18 degrees, 20 degrees, or other values, etc., which can be determined in combination with actual application scenarios.

[0186] Exemplarily, step 702 can include: after determining the target measurement data, the first device can filter a plurality of positions included in the map according to the target measurement data and a preset condition, and each first position after filtering needs to satisfy the aforementioned preset condition.

[0187] 703. Determine a first parameter value for indicating an object occupancy of the first position according to a first distance in the target measurement data.

[0188] 704. Determine an angle adjustment factor corresponding to the first position according to a first angle in the target measurement data.

[0189] 705. determining a second parameter value of the first position according to the first parameter value and the angle adjustment factor.

[0190] In the embodiments of the present application, the first device can implement the specific implementation of steps 703 to 705 according to the above description. Figure 2 The description of steps 202 to 204 in the corresponding embodiments is not repeated here.

[0191] For example, the first device can repeatedly implement steps 703 to 705 to determine the second parameter value of each first position corresponding to the target measurement data by using the target measurement data.

[0192] The first device repeatedly implements steps 702 to 705 multiple times to determine the second parameter value of each first position corresponding to each measurement data in the at least one first position, and further determines the object occupancy of each position in the above map.

[0193] For example, if a second position in the map corresponds to one second occupancy probability, the second occupancy probability can be determined as the final occupancy probability, and the occupancy probability represents the probability that the position is occupied by an object.

[0194] Alternatively, if a second position in the map corresponds to at least two second occupancy probabilities, the final occupancy probability of the second position can be determined according to the at least two second occupancy probabilities of the second position. Alternatively, the at least two second occupancy probabilities can be subjected to binary Bayesian filtering or weighted averaging to obtain the final occupancy probability of the second position; or weighted summation can be used instead of averaging, which is not limited here.

[0195] Alternatively, summing the log-odd corresponding to the at least two second occupancy probabilities of the second position can determine the log-odd value corresponding to the final occupancy probability of the second position.

[0196] Alternatively, if a third position in the map does not correspond to any second occupancy probability, the occupancy probability of the third position can be determined as 0.5 or the log-odd value corresponding to the occupancy probability of the third position can be determined as 0.

[0197] It should be noted that after the final occupancy probability or the log-odd value of each position in the map is determined, the final occupancy probability can also be deformed, and the deformation manner can refer to the above description. Figure 8 The description of step 204 in the corresponding embodiments is not repeated here.

[0198] The map described above is based on multiple measurement data collected by the sensor at one time. Optionally, the first device can acquire measurement data multiple times through the sensor and continuously update the map using the acquired measurement data.

[0199] This application provides a specific application scenario for the location occupancy based on measurement data, thereby improving the integration of this solution with specific application scenarios.

[0200] Optionally, the first device may also predict the trajectory of objects in the environment based on the latest obtained map; and / or, the first device may also determine the drivable area of ​​the first device based on the latest obtained map; and / or, the first device may also determine the driving strategy of the first device based on the latest obtained map; and / or, the first device may also perform path planning for the first device based on the latest obtained map, etc. The specific use of the map can be determined in combination with the actual application scenario, and no limitation is made here.

[0201] II. Multiple measurement data are used to obtain the object occupancy status at a specific first position.

[0202] For example, please refer to Figure 8 , Figure 8 Another schematic diagram illustrating the method for determining the occupancy of a location provided in the embodiments of this application, as shown below. Figure 2 As shown, the method for determining the occupancy status of a location provided in this application may include:

[0203] 801. Acquire at least one first measurement data corresponding to the first position, each first measurement data is obtained through a sensor, each measurement data includes a first distance and a first angle, the first angle includes a first azimuth angle and / or a first pitch angle.

[0204] For example, after determining a specific first position, the first device can acquire multiple measurement data through a sensor, and then determine at least one first measurement data corresponding to the aforementioned specific first position from the multiple measurement data acquired by the sensor.

[0205] In one scenario, at least one first measurement data corresponding to a specific first position may include all measurement data acquired by the sensor. That is, in subsequent steps, the first device needs to determine a second parameter value for the first position based on each measurement data acquired by the sensor.

[0206] In another case, a preset condition needs to be met between the first position and the first measurement data, the preset condition including at least one of the following: an absolute value of a difference between a second distance corresponding to the first position and the first distance is less than or equal to a distance threshold value, and / or an absolute value of a difference between a second azimuth angle corresponding to the first position and the first azimuth angle is less than or equal to a first angle value, and / or an absolute value of a difference between a second elevation angle corresponding to the first position and the first elevation angle is less than or equal to a second angle value. The second distance corresponding to the first position can be a distance between the first position and an origin of a sensor coordinate system or a carrier coordinate system, the second azimuth angle corresponding to the first position can be an azimuth angle of the first position relative to the sensor coordinate system or the carrier coordinate system, and the second elevation angle corresponding to the first position can be an elevation angle of the first position relative to the sensor coordinate system or the carrier coordinate system. It should be noted that the first distance, the second distance, the first azimuth angle, the second azimuth angle, the first elevation angle, and the second elevation angle are all relative to data in the same coordinate system.

[0207] After determining a specific first position, the first device can filter at least one first measurement data meeting the preset condition from all the measurement data obtained by the sensor, and then determine a second parameter value of the first position based on each of the at least one first measurement data corresponding to the specific first position in a subsequent step.

[0208] 802. Determine a first parameter value indicating an object occupancy of the first position according to the first distance in the first measurement data.

[0209] 803. Determine an angle adjustment factor corresponding to the first position according to the first angle in the first measurement data.

[0210] 804. Determine a second parameter value of the first position according to the first parameter value and the angle adjustment factor.

[0211] In the embodiments of the present application, the specific implementation of the first device performing steps 802 to 804 can refer to the above description of steps 202 to 204. Figures 1 to 8 The description of steps 202 to 204 in the corresponding embodiments will not be repeated here.

[0212] Exemplarily, after the first device repeatedly performs steps 802 to 804 at least once, a second occupancy probability of the first position can be determined according to each of the at least one first measurement data corresponding to the specific first position, i.e., at least one second occupancy probability corresponding to the at least one first measurement data is obtained; and the first device determines a final occupancy probability of the first position according to the at least one second occupancy probability. Alternatively, if the first device obtains at least two second occupancy probabilities corresponding to the specific first position, the at least two second occupancy probabilities can be averaged to obtain the final occupancy probability of the specific first position; or, the averaging can be replaced by weighted summation, etc., which is not limited herein.

[0213] Since a specific measurement data is generated based on an object at a specific position in the surrounding environment, theoretically, it is meaningful to determine the object occupancy of the specific position based on the specific measurement data, and it is meaningless to determine the object occupancy of positions other than the specific position based on the specific measurement data. In the present application, a preset condition is set, so that a specific measurement data in the present application is only used to determine the object occupancy of the first position that meets the preset condition with the measurement data. Compared with each measurement data being used to determine the object occupancy of each position, the present application greatly reduces meaningless calculations, thereby effectively reducing the computing power requirement of the present application, and is beneficial to improve the effective utilization rate of computer resources.

[0214] Further, the distance, azimuth angle and / or elevation angle in the measurement data are used for screening, which provides a simple and easy-to-operate screening method. In addition, considering that the distance, azimuth angle and / or elevation angle in the measurement data may have errors, so that there may be errors between the position of the object of the measurement data and the position pointed by the measurement data, the distance threshold value, the first angle value and / or the second angle value are set in the preset condition. Compared with setting the preset condition as “the distance corresponding to the first position is equal to the first distance, and / or, the azimuth angle corresponding to the first position is equal to the first azimuth angle, and / or, the elevation angle corresponding to the first position is equal to the first elevation angle”, the object occupancy of the position where the specific measurement data is generated can be determined while the computer resources are not wasted as much as possible.

[0215] In addition, the standard deviation or root mean square error reflecting the measurement distance error can indicate the error of the distance in the measurement data, the standard deviation or root mean square error reflecting the measurement azimuth angle error can indicate the error of the azimuth angle in the measurement data, the standard deviation or root mean square error reflecting the measurement elevation angle error can indicate the error of the elevation angle in the measurement data, the distance threshold value in the present application is determined based on the standard deviation or root mean square error reflecting the measurement distance error, the first angle value is determined based on the standard deviation or root mean square error reflecting the measurement azimuth angle error, and the second angle value is determined based on the standard deviation or root mean square error reflecting the measurement elevation angle error, thereby facilitating the compatibility of sensors with different error specifications and facilitating the expansion of the application scenarios of the present application.

[0216] In Figure 9 Based on the corresponding embodiments, in order to better implement the above-mentioned scheme of the embodiments of the present application, the related equipment for implementing the above-mentioned scheme is also provided below. For details, please refer to Figure 9 , Figures 1 to 8 A structural schematic diagram of the position occupancy determination apparatus provided by the embodiments of the present application is shown in FIG. 9. The position occupancy determination apparatus 900 comprises: an acquisition module 901 configured to acquire measurement data, the measurement data comprising a first distance and an angle, the angle comprising a first azimuth angle and / or a first elevation angle; a determination module 902 configured to determine, according to the first distance, a first parameter value used to indicate the object occupancy of the first position; the determination module 902 is further configured to determine, according to the angle, an angle adjustment factor corresponding to the first position; and the determination module 902 is further configured to determine, according to the first parameter value and the angle adjustment factor, a second parameter value corresponding to the first position, the second parameter value indicating the object occupancy of the first position.

[0217] Optionally, the first angle adjustment factor is determined based on the first azimuth angle and first quality information, the first quality information being used to reflect the error of the first azimuth angle in the measurement data; and / or the second angle adjustment factor is determined based on the first elevation angle and second quality information, the second quality information being used to reflect the error of the first elevation angle in the measurement data, wherein the first angle adjustment factor and / or the second angle adjustment factor are used to determine the angle adjustment factor.

[0218] Optionally, the first angle adjustment factor is determined based on a first function according to the first azimuth angle, a second azimuth angle and the first quality information, wherein the first function is a Gaussian function, or the first function is obtained based on an error erf function, or the first function is obtained based on an activation sigmoid function; and / or the second angle adjustment factor is determined based on a second function according to the first elevation angle, a second elevation angle and the second quality information, wherein the second function is a Gaussian function, or the second function is obtained based on an error erf function, or the second function is obtained based on an activation sigmoid function.

[0219] Optionally, the first parameter value is a first occupancy probability, and the second parameter value is a second occupancy probability, and the second occupancy probability satisfies the following relationship with the first occupancy probability and the angle adjustment factor:

[0220] p r,a (r,a)=c0+(p r (r)-c 10 )·F(a)

[0221] wherein p r,a (r,a) is the second occupancy probability, p r (r) is the first occupancy probability, F(a) is the angle adjustment factor, and the values of c0 and c 10 are preset values.

[0222] Optionally, c0 and c 10 are both 0.5.

[0223] Optionally, the first position and the measurement data satisfy a preset condition, and the preset condition comprises at least one of the following: an absolute value of a difference between a distance corresponding to the first position and the first distance is less than or equal to a distance threshold value, and / or an absolute value of a difference between an azimuth angle corresponding to the first position and the first azimuth angle is less than or equal to a first angle value, and / or an absolute value of a difference between a pitch angle corresponding to the first position and the first pitch angle is less than or equal to a second angle value.

[0224] Optionally, the distance threshold value is determined based on a standard deviation or a root mean square error reflecting a distance measurement error, the first angle value is determined based on a standard deviation or a root mean square error reflecting an azimuth angle measurement error, and the second angle value is determined based on a standard deviation or a root mean square error reflecting a pitch angle measurement error.

[0225] Optionally, the first parameter value is obtained based on the first distance and third quality information, and the third quality information is used to reflect an error of the first distance in the measurement data.

[0226] Optionally, the at least one measurement data is obtained by a sensor, and the at least one measurement data is used to obtain a map corresponding to a field of view range of the sensor, and each measurement data is used to obtain the second parameter value of one or more first positions in the map.

[0227] It should be noted that the information interaction and execution process between the modules / units in the position occupancy determination apparatus 900 are the same as those of the method embodiments of the present application. Figure 10 The corresponding method embodiments are based on the same concept, and specific contents can be referred to the descriptions in the foregoing method embodiments of the present application, which will not be described here.

[0228] Next, a device provided in an embodiment of the present application is introduced. In the case of the device specifically representing an execution device, please refer toFigure 10 , Figure 10 Figure 1 shows a schematic diagram of an apparatus according to an embodiment of the present application. The apparatus 1000 includes a receiver 1001, a transmitter 1002, a processor 1003 and a memory 1004. The processor 1003 can include an application processor 10031 and a communication processor 10032. The receiver 1001, the transmitter 1002, the processor 1003 and the memory 1004 can be connected by a bus or other means. Figures 1 to 8 The processor 1003 controls the operation of the apparatus. In particular applications, the various components of the apparatus are coupled by a bus system, which can include a data bus, a power bus, a control bus and a state signal bus, etc. However, in order to clearly illustrate the apparatus, all the buses are referred to as a bus system in the figure.

[0229] The memory 1004 can include read-only memory and random access memory, and provide instructions and data to the processor 1003. A part of the memory 1004 can also include a non-volatile random access memory (NVRAM). The memory 1004 stores processor and operation instructions, executable modules or data structures, or subsets thereof, or expanded sets thereof, wherein the operation instructions can include various operation instructions for implementing various operations.

[0230] The processor 1003 controls the operation of the apparatus. In particular applications, the various components of the apparatus are coupled by a bus system, which can include a data bus, a power bus, a control bus and a state signal bus, etc. However, in order to clearly illustrate the apparatus, all the buses are referred to as a bus system in the figure.

[0231] The method disclosed in the embodiments of the present application can be applied to the processor 1003 or implemented by the processor 1003. The processor 1003 can be an integrated circuit chip with a signal processing capability. In the implementation process, the steps of the method can be completed by using an integrated logic circuit or an instruction in the form of software in the processor 1003. The processor 1003 can be a general-purpose processor, a digital signal processor (DSP), a microprocessor or a microcontroller. The processor 1003 can further include an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The processor 1003 can implement or execute the methods, steps and logical block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as a hardware code processor to execute, or a combination of hardware and software modules in the code processor to execute. The software module can be located in a storage medium in the art such as a random access memory, a flash memory, a read only memory, a programmable read only memory or an electrically erasable programmable memory, a register, or the like. The storage medium is located in the storage 1004, and the processor 1003 reads information in the storage 1004 and combines the hardware to complete the steps of the method.

[0232] The receiver 1001 can be configured to receive input digital or character information, and generate signal input related to the settings and function control of the device. The transmitter 1002 can be configured to output digital or character information through the first interface; the transmitter 1002 can also be configured to send instructions to the disk group through the first interface to modify the data in the disk group; the transmitter 1002 can also include a display device such as a display screen.

[0233] In the embodiments of the present application, the processor 1003 is configured to execute Figures 1 to 8 the method executed by the first device in the corresponding embodiments. It should be noted that the application processor 10031 in the processor 1003 executes the specific manner of each step as described above, and the specific manner of each step as described above is the same as the method disclosed in the embodiments of the present application. Figures 1 to 8 The corresponding method embodiments are based on the same concept, and the technical effects brought by the corresponding method embodiments are the same as the method disclosed in the embodiments of the present application. Figure 11 The corresponding method embodiments are based on the same concept, and the technical effects brought by the corresponding method embodiments are the same as the method disclosed in the embodiments of the present application.

[0234] The embodiments of the present application also provide a vehicle, please refer to Figure 11 ,Figure 11 A schematic diagram of a vehicle is provided for embodiments of the present application. The vehicle 100 is configured to operate in a fully or partially autonomous mode. For example, the vehicle 100 can control itself while in an autonomous mode and can determine a current state of the vehicle and its surroundings, determine a likely behavior of at least one other vehicle in the surroundings, and determine a confidence level corresponding to a likelihood that the other vehicle will perform the likely behavior based on the determined information, and control the vehicle 100 based on the determined information. The vehicle 100 can also be configured to operate without human interaction while in the autonomous mode.

[0235] The vehicle 100 can include various subsystems, such as a propulsion system 102, a sensor system 104, a control system 106, one or more peripheral devices 108, a power source 110, a computer system 112, and a user interface 116. Alternatively, the vehicle 100 can include more or fewer subsystems, and each subsystem can include multiple components. Additionally, each subsystem and component of the vehicle 100 can be interconnected by wires or wirelessly.

[0236] The propulsion system 102 can include components that provide powered movement for the vehicle 100. In one embodiment, the propulsion system 102 can include an engine 118, an energy source 119, a transmission 120, and wheels / tires 121.

[0237] The engine 118 can be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine that includes a gasoline engine and an electric motor, or a hybrid engine that includes an internal combustion engine and an air compression engine. The engine 118 converts the energy source 119 into mechanical energy. Examples of the energy source 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electrical power. The energy source 119 can also provide energy for other systems of the vehicle 100. The transmission 120 can transmit mechanical power from the engine 118 to the wheels 121. The transmission 120 can include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 120 can also include other devices, such as a clutch. The drive shaft can include one or more shafts that can be coupled to one or more wheels 121.

[0238] The sensor system 104 can include several sensors that sense information about the environment surrounding the vehicle 100. For example, the sensor system 104 can include a positioning system 122 (which can be a global positioning GPS system, a Beidou system, or other positioning system), an inertial measurement unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. The sensor system 104 can also include sensors that monitor internal systems of the vehicle 100 (e.g., in-vehicle air quality monitors, fuel gauges, oil temperature gauges, etc.). Sensor data from one or more of these sensors can be used to detect objects and their respective characteristics (location, shape, direction, velocity, etc.). Such detection and identification is a critical function for the safe operation of the autonomous vehicle 100.

[0239] The positioning system 122 can be used to estimate the geographic location of the vehicle 100. The IMU 124 is used to perceive changes in the position and orientation of the vehicle 100 based on inertial acceleration. In one embodiment, the IMU 124 can be a combination of an accelerometer and a gyroscope. The radar 126 can utilize radio signals to perceive objects within the environment surrounding the vehicle 100, which can be manifested as a millimeter wave radar or a laser radar. In some embodiments, in addition to perceiving objects, the radar 126 can also be used to perceive the velocity and / or heading of the objects. The laser rangefinder 128 can utilize laser light to perceive objects in the environment in which the vehicle 100 is located. In some embodiments, the laser rangefinder 128 can include one or more laser sources, a laser scanner, and one or more detectors, among other system components. The camera 130 can be used to capture multiple images of the environment surrounding the vehicle 100. The camera 130 can be a still camera or a video camera.

[0240] The control system 106 controls the operation of the vehicle 100 and its components. The control system 106 can include various components, including a steering system 132, a throttle 134, a braking unit 136, a computer vision system 140, a line control system 142, and an obstacle avoidance system 144.

[0241] The steering system 132 is operable to adjust the heading of the vehicle 100. In one embodiment, the steering system 132 can be a steering wheel system. The throttle 134 is used to control the speed of the engine 118 and, in turn, the speed of the vehicle 100. The braking unit 136 is used to control the deceleration of the vehicle 100. The braking unit 136 can use friction to slow the wheels 121. In other embodiments, the braking unit 136 can convert the kinetic energy of the wheels 121 into electrical current. The braking unit 136 can also take other forms to slow the wheels 121 and, in turn, control the speed of the vehicle 100. The computer vision system 140 is operable to process and analyze images captured by the cameras 130 to identify objects and / or features in the environment surrounding the vehicle 100. The objects and / or features can include traffic signals, road boundaries, and obstacles. The computer vision system 140 can use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 140 can be used to map the environment, track objects, estimate the speed of objects, and the like. The route control system 142 is used to determine the route and speed of travel of the vehicle 100. In some embodiments, the route control system 142 can include a lateral planning module 1421 and a longitudinal planning module 1422 that are used to determine the route and speed of travel of the vehicle 100 in conjunction with data from the obstacle avoidance system 144, the GPS 122, and one or more predetermined maps, respectively. The obstacle avoidance system 144 is used to identify, evaluate, and avoid or otherwise navigate around obstacles in the environment of the vehicle 100, which can be actual obstacles and virtual moving obstacles that can collide with the vehicle 100. In one instance, the control system 106 can include additional components in addition to those shown and described, or some of the components shown can be reduced.

[0242] The vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users through the peripherals 108. The peripherals 108 can include a wireless communication system 146, an on-board computer 148, a microphone 150, and / or a speaker 152. In some embodiments, the peripherals 108 provide a means for a user of the vehicle 100 to interact with the user interface 116. For example, the on-board computer 148 can provide information to a user of the vehicle 100. The user interface 116 can also operate the on-board computer 148 to receive input from the user. The on-board computer 148 can be operated through a touch screen. In other cases, the peripherals 108 can provide a means for the vehicle 100 to communicate with other devices located within the vehicle. For example, the microphone 150 can receive audio (e.g., voice commands or other audio input) from a user of the vehicle 100. Similarly, the speaker 152 can output audio to a user of the vehicle 100. The wireless communication system 146 can wirelessly communicate with one or more devices, either directly or via a communication network. For example, the wireless communication system 146 can use 3G cellular communication, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communication, such as LTE. Or 5G cellular communication. The wireless communication system 146 can utilize wireless local area network (WLAN) communication. In some embodiments, the wireless communication system 146 can utilize an infrared link, Bluetooth, or ZigBee to communicate directly with devices. Other wireless protocols, such as various vehicle communication systems, for example, the wireless communication system 146 can include one or more dedicated short range communications (DSRC) devices, which can include public and / or private data communication between vehicles and / or roadside stations.

[0243] The power supply 110 can provide power to various components of the vehicle 100. In one embodiment, the power supply 110 can be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such a battery can be configured to power the various components of the vehicle 100. In some embodiments, the power supply 110 and the energy source 119 can be implemented together, such as in some all-electric vehicles.

[0244] Some or all of the functionality of the vehicle 100 is controlled by a computer system 112. The computer system 112 can include at least one processor 113 that executes instructions 115 stored in a non-transitory computer readable medium such as a memory 114. The computer system 112 can also be a plurality of computing devices that control individual components or subsystems of the vehicle 100 in a distributed manner. The processor 113 can be any conventional processor such as a commercially available central processing unit (CPU). Alternatively, the processor 113 can be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware-based processor. Although Figure 11 Although the processor and the memory are functionally illustrated as being in the same block, it will be understood by those of ordinary skill in the art that the processor or the memory can actually comprise multiple processors or memories that are not stored in the same physical housing. For example, the memory 114 can be a hard drive or other storage medium located in a different housing than the computer system 112. Thus, references to the processor 113 or the memory 114 will be understood to include references to a collection of processors or memories that can or can not operate in parallel. Rather than using a single processor to perform the steps described herein, some components such as the steering assembly and the deceleration assembly can each have their own processor that only performs calculations related to the functionality specific to the component.

[0245] In various aspects described herein, the processor 113 can be located remotely from the vehicle 100 and in wireless communication with the vehicle 100. In other aspects, some of the processes described herein are performed on the processor 113 disposed within the vehicle 100 while others are performed by a remote processor 113, including taking the necessary steps to perform a single maneuver.

[0246] In some embodiments, the memory 114 can include instructions 115 (e.g., program logic) that can be executed by the processor 113 to perform various functions of the vehicle 100, including those described above. The memory 114 can also include additional instructions, including instructions to send data to, receive data from, interact with, and / or control one or more of the travel system 102, the sensor system 104, the control system 106, and the peripherals 108. In addition to the instructions 115, the memory 114 can also store data, such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, and other information. Such information can be used by the vehicle 100 and the computer system 112 during operation of the vehicle 100 in autonomous, semi-autonomous, and / or manual modes. The user interface 116 is used to provide information to or receive information from a user of the vehicle 100. Optionally, the user interface 116 can include one or more input / output devices within the set of peripherals 108, such as the wireless communication system 146, the on-board computer 148, the microphone 150, and the speaker 152.

[0247] The computer system 112 can control the functions of the vehicle 100 based on inputs received from various subsystems (e.g., the travel system 102, the sensor system 104, and the control system 106), as well as from the user interface 116. For example, the computer system 112 can utilize inputs from the control system 106 in order to control the steering system 132 to avoid obstacles detected by the sensor system 104 and the obstacle avoidance system 144. In some embodiments, the computer system 112 can be operable to provide control over many aspects of the vehicle 100 and its subsystems.

[0248] Optionally, one or more of the above-described components can be installed separately from or associated with the vehicle 100. For example, the memory 114 can exist partially or entirely separately from the vehicle 100. The above-described components can be communicatively coupled together in a wired and / or wireless manner.

[0249] Optionally, the above-described components are just one example, and in actual applications, components in each of the above-described modules can be added or deleted according to actual needs, Figure 11 This should not be construed as limiting the embodiments of the present application. A vehicle that travels on a road, such as the vehicle 100 above, can identify objects within its surrounding environment to determine an adjustment to a current speed. The objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and based on respective characteristics of the object, such as its current speed, acceleration, spacing from the vehicle, etc., can be used to determine a speed at which the vehicle is to adjust.

[0250] Optionally, the vehicle 100 or a computing device associated with the vehicle 100, such as the computer system 112, canFigures 1 to 8 The computer system 112, the computer vision system 140, the memory 114 can predict the behavior of the identified object based on the characteristics of the identified object and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of the other, so all of the identified objects can also be considered together to predict the behavior of a single identified object. The vehicle 100 can adjust its speed based on the predicted behavior of the identified object. In other words, the vehicle 100 can determine what steady state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the object. In this process, other factors can also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 in the road it is traveling on, the curvature of the road, the proximity of static and dynamic objects, etc. In addition to providing instructions to adjust the speed of the vehicle, the computing device can also provide instructions to modify the steering angle of the vehicle 100 to cause the vehicle 100 to follow a given trajectory and / or maintain a safe lateral and longitudinal distance from objects near the vehicle 100 (e.g., a car in the adjacent lane on the road).

[0251] In the embodiments of the present application, the processor 113 in the vehicle 100 is configured to perform the method Figures 1 to 8 corresponding to the method performed by the first device. It should be noted that the specific manner in which the processor 113 performs the various steps described above is the same as that described above in the present application Figures 1 to 8 The corresponding method embodiments are based on the same concept, and the resulting technical effects are the same as those described above in the present application Figures 1 to 8 The corresponding method embodiments are based on the same concept, and the resulting technical effects are the same as those described above in the present application

[0252] In the embodiments of the present application, a computer readable storage medium is also provided, which stores a program, and when the program is run on a computer, the computer performs the steps performed by the first device in the method described in the above Figures 1 to 8 embodiments.

[0253] In the embodiments of the present application, a computer program product is also provided, which includes a program, and when the program is run on a computer, the computer performs the steps performed by the first device in the method described in the above Figures 1 to 8 embodiments.

[0254] In the embodiments of the present application, a circuit system is also provided, which includes processing circuitry configured to perform the steps performed by the first device in the method described in the above Figures 1 to 8 embodiments.

[0255] The first device or the determination apparatus of the occupancy of the location provided by the embodiments of the present application can be a chip, which includes a processing unit, for example, a processor, and a communication unit, for example, an input / output interface, a pin, or a circuit, etc. The processing unit can execute computer execution instructions stored in a storage unit, so that the chip executes the method described in the above embodiments. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, etc. The storage unit can also be a storage unit outside the chip in the wireless access device, such as a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), etc. Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures 1 to 8 Figures

[0256] The processor mentioned in any of the above can be a general central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the method of the first aspect.

[0257] In addition, it should be noted that the apparatus embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments. In addition, the connection relationship between the modules in the apparatus embodiments provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines.

[0258] ​Those skilled in the art can clearly understand that the application can be implemented by means of software plus necessary universal hardware, and of course can also be implemented by means of dedicated hardware including special integrated circuit, special CLU, special memory, special component, etc. Generally, any function completed by computer program can be easily implemented by corresponding hardware, and the specific hardware structure for implementing the same function can also be various, such as analog circuit, digital circuit or special circuit, etc. However, for the application, the software program implementation is a better embodiment. Based on such understanding, the technical solution of the application or the part of the application which makes contribution to the prior art can be embodied in the form of software product, which is stored in readable storage medium, such as floppy disk, U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a plurality of instructions for making a computer device (which can be personal computer, server or network device, etc.) execute the method described in various embodiments of the application.

[0259] In the above embodiments, the implementation can be achieved by software, hardware, firmware or any combination thereof, entirely or partially. When implemented by software, the implementation can be in the form of computer program product entirely or partially.

[0260] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the flow or function described in the embodiments of the application is entirely or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be stored by the computer or a data storage device such as server, data center, etc. integrated with one or more available media. The available medium can be magnetic medium (such as floppy disk, hard disk, magnetic tape), optical medium (such as DVD) or semiconductor medium (such as solid state disk (SSD)) etc.

Claims

1. A method of determining occupancy of a location, characterized by, The method comprises: obtaining measurement data, the measurement data comprising a first distance and an angle, the angle comprising a first azimuth angle and / or a first elevation angle; determining, according to the first distance, a first parameter value for indicating an object occupancy of a first position; determining, according to the angle, an angle adjustment factor corresponding to the first position; determining, according to the first parameter value and the angle adjustment factor, a second parameter value corresponding to the first position, the second parameter value indicating the object occupancy of the first position.

2. The method of claim 1, wherein, The first angle adjustment factor is determined based on the first azimuth angle and first quality information, the first quality information being used to reflect an error of the first azimuth angle in the measurement data; and / or, The second angle adjustment factor is determined based on the first elevation angle and second quality information, the second quality information being used to reflect an error of the first elevation angle in the measurement data, wherein the first angle adjustment factor and / or the second angle adjustment factor are used to determine the angle adjustment factor.

3. The method of claim 2, wherein: The first angle adjustment factor is determined based on a first function according to the first azimuth angle, a second azimuth angle and the first quality information, wherein the first function is a Gaussian function, or the first function is obtained based on an error function erf, or the first function is obtained based on an activation function sigmoid; and / or The second angle adjustment factor is determined based on a second function according to the first elevation angle, a second elevation angle and the second quality information, wherein the second function is a Gaussian function, or the second function is obtained based on an error function erf, or the second function is obtained based on an activation function sigmoid.

4. The method according to any one of claims 1 to 3, characterized in that, The first parameter value is a first occupancy probability, and the second parameter value is a second occupancy probability, and the second occupancy probability and the first occupancy probability and the angle adjustment factor satisfy the following relationship: p r,a (r,a) = c0+ (p r (r)-c1) · F(a) where p r,a (r,a) is the second occupancy probability, p r (r) is the first occupancy probability, F(a) is an angle adjustment factor, and the values of c0 and c1 are preset values.

5. The method of claim 4, wherein, c0 and c1 are both 0.

5.

6. The method according to any one of claims 1 to 3, characterized in that, The first position and the measurement data satisfy a preset condition, and the preset condition comprises at least one of the following: an absolute value of a difference between a distance corresponding to the first position and the first distance is less than or equal to a distance threshold value, and / or an absolute value of a difference between an azimuth angle corresponding to the first position and the first azimuth angle is less than or equal to a first angle value, and / or an absolute value of a difference between an elevation angle corresponding to the first position and the first elevation angle is less than or equal to a second angle value.

7. The method of claim 6, wherein, The distance threshold value is determined based on a standard deviation or a root mean square error reflecting a distance measurement error, the first angle value is determined based on a standard deviation or a root mean square error reflecting an azimuth angle measurement error, and the second angle value is determined based on a standard deviation or a root mean square error reflecting an elevation angle measurement error.

8. The method according to any one of claims 1 to 3, characterized in that, The first parameter value is obtained based on the first distance and third quality information, and the third quality information is used to reflect an error of the first distance in the measurement data.

9. The method according to any one of claims 1 to 3, characterized in that, The at least one measurement data is obtained by a sensor, and the at least one measurement data is used to obtain a map corresponding to a field of view of the sensor, and each measurement data is used to obtain a second parameter value of one or more first positions in the map.

10. An arrangement for determining occupancy of a location, characterized by The device comprises: an acquisition module configured to acquire measurement data, the measurement data comprising a first distance and an angle, the angle comprising a first azimuth angle and / or a first elevation angle; a determination module configured to determine, according to the first distance, a first parameter value used to indicate an object occupancy of a first position; the determination module is further configured to determine, according to the angle, an angle adjustment factor corresponding to the first position; the determination module is further configured to determine, according to the first parameter value and the angle adjustment factor, a second parameter value corresponding to the first position, the second parameter value being used to indicate the object occupancy of the first position.

11. The apparatus of claim 10, wherein, the first angle adjustment factor is determined based on the first azimuth angle and first quality information, the first quality information being used to reflect an error of the first azimuth angle in the measurement data; and / or the second angle adjustment factor is determined based on the first elevation angle and second quality information, the second quality information being used to reflect an error of the first elevation angle in the measurement data, wherein the first angle adjustment factor and / or the second angle adjustment factor are used to determine the angle adjustment factor.

12. The device of claim 11, wherein the first angle adjustment factor is determined based on a first function according to the first azimuth angle, a second azimuth angle and the first quality information, wherein the first function is a Gaussian function, or the first function is determined based on an error function erf, or the first function is determined based on an activation function sigmoid; and / or the second angle adjustment factor is determined based on a second function according to the first elevation angle, a second elevation angle and the second quality information, wherein the second function is a Gaussian function, or the second function is determined based on an error function erf, or the second function is determined based on an activation function sigmoid.

13. The apparatus of any one of claims 10 to 12, wherein, the first parameter value is a first occupancy probability, and the second parameter value is a second occupancy probability, and the second occupancy probability satisfies the following relationship with the first occupancy probability and the angle adjustment factor: p r,a (r,a) = c0+ (p r (r)-c 10 ) · F(a) where p r,a (r,a) is the second occupancy probability, p r (r) is the first occupancy probability, F(a) is an angle adjustment factor, c0 and c 10 are preset values.

14. The apparatus of claim 13, wherein, c0and c 10 are both 0.

5.

15. The apparatus of any one of claims 10 to 12, wherein, the first position and the measurement data satisfy a preset condition, and the preset condition comprises at least one of the following: an absolute value of a difference between a distance corresponding to the first position and the first distance is less than or equal to a distance threshold value, and / or an absolute value of a difference between an azimuth angle corresponding to the first position and the first azimuth angle is less than or equal to a first angle value, and / or an absolute value of a difference between an elevation angle corresponding to the first position and the first elevation angle is less than or equal to a second angle value.

16. The apparatus of claim 15, wherein, The distance threshold is determined based on a standard deviation or a root mean square error reflecting an error of the distance measurement, the first angle value is determined based on a standard deviation or a root mean square error reflecting an error of the azimuth angle measurement, and the second angle value is determined based on a standard deviation or a root mean square error reflecting an error of the elevation angle measurement.

17. The apparatus of any one of claims 10 to 12, wherein, The first parameter value is obtained based on the first distance and third quality information reflecting an error of the first distance in the measurement data.

18. The apparatus of any one of claims 10 to 12, wherein, At least one of the measurement data is obtained by a sensor, and each of the measurement data is used to obtain a second parameter value of one or more of the first positions in a map corresponding to a field of view range of the sensor.

19. An apparatus, comprising: A computer program product includes a program, and when the program is run on a computer, the computer executes the method of any one of claims 1 to 9.

20. A vehicle characterized by A computer program product includes a program, and when the program is run on a computer, the computer executes the method of any one of claims 1 to 9.

21. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a program, and when the program is run on a computer, the computer executes the method of any one of claims 1 to 9.

22. A computer program product, characterised in that, The computer program product includes a program, and when the program is run on a computer, the computer executes the method of any one of claims 1 to 9.

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