阅读说明：本技术 车辆定位方法和装置 (Vehicle positioning method and device ) 是由 刘建琴 王兴冰 陈军 于 2020-03-19 设计创作，主要内容包括：一种车辆定位方法和装置,该方法包括：确定车辆在第一时间的第一位置(201)；确定车辆在第二时间的第一设计适用域ODD(202)；根据第一ODD在第一位置的周围采样用于进行粒子滤波的多个粒子,该多个粒子的数目或分布与第一ODD对应(203)；根据该多个粒子确定车辆的第二时间的第二位置(204)。通过对ODD合理的分级,自适应的设置用于定位计算的采样粒子的数目或分布,从而在节省计算资源的同时,提高粒子滤波定位效率,完成自动驾驶车辆的快速定位。(A vehicle locating method and apparatus, the method comprising: determining a first position (201) of the vehicle at a first time; determining a first design applicability, ODD, of the vehicle at a second time (202); sampling a plurality of particles for particle filtering around the first location according to the first ODD, the plurality of particles corresponding in number or distribution to the first ODD (203); a second location of the vehicle at a second time is determined (204) from the plurality of particles. Through reasonable grading of ODD, the number or distribution of sampling particles for positioning calculation is set in a self-adaptive mode, so that the calculation resources are saved, meanwhile, the particle filter positioning efficiency is improved, and the rapid positioning of the automatic driving vehicle is completed.)

1. A vehicle positioning method, characterized by comprising:

determining a first position of the vehicle at a first time;

determining a first design applicability domain, ODD, of the vehicle at a second time;

sampling a plurality of particles for particle filtering around the first location according to the first ODD, the plurality of particles corresponding in number or distribution to the first ODD;

determining a second position of the vehicle at a second time, subsequent to the first time, from the plurality of particles.

2. The method of claim 1, wherein the determining that the vehicle is after the first ODD at the second time, the method further comprises:

determining a target level of the first ODD;

the sampling a plurality of particles for particle filtering around the first location according to the first ODD, the plurality of particles corresponding in number or distribution to the first ODD including:

sampling a plurality of particles for particle filtering around the first location according to the target level, the number or distribution of the plurality of particles corresponding to the target level.

3. The method of claim 2, wherein the number or distribution of the plurality of particles further corresponds to a sensor type of the vehicle.

4. The method according to any one of claims 1-3, further comprising:

determining that the first ODD is switched to a second ODD;

resetting a number or distribution of the plurality of particles according to the second ODD.

5. The method of any of claim 4, further comprising:

determining that a number of valid particles in the plurality of particles is less than a threshold and that no switching has occurred with the first ODD;

deleting part of the invalid particles and copying part of the valid particles.

6. A vehicle positioning device, comprising:

a processing unit for determining a first position of a vehicle at a first time and a first design applicability domain, ODD, of the vehicle at a second time;

an acquisition unit for sampling a plurality of particles for particle filtering around the first location according to the first ODD, the number or distribution of the plurality of particles corresponding to the first ODD;

the processing unit is further configured to determine a second position of the vehicle at a second time, the second time subsequent to the first time, based on the plurality of particles.

7. The apparatus of claim 6, wherein the processing unit is further configured to: determining a target level of a first ODD of the vehicle at a second time after determining the first ODD;

the acquisition unit samples a plurality of particles for particle filtering around the first location according to the first ODD, and the number or distribution of the plurality of particles corresponding to the first ODD includes: the acquisition unit samples a plurality of particles for particle filtering around the first position according to the target level, the number or distribution of the plurality of particles corresponding to the target level.

8. The apparatus of claim 7, wherein the number or distribution of the plurality of particles further corresponds to a sensor type of the vehicle.

9. The apparatus according to any one of claims 6-8, wherein:

the processing unit is further configured to determine that the first ODD switches to a second ODD;

the acquisition unit is further configured to reset a number or distribution of the plurality of particles according to the second ODD.

10. The apparatus of claim 9, wherein the processing unit is further configured to:

determining that a number of valid particles in the plurality of particles is less than a threshold and that no switching has occurred with the first ODD;

deleting part of the invalid particles and copying part of the valid particles.

11. A vehicle positioning device, comprising:

a memory for storing a computer program; and

a processor for executing a computing program stored in the memory to cause the vehicle localization apparatus to perform the method of any of claims 1-5.

12. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, cause the vehicle localization arrangement to perform the method of any one of claims 1-5.

13. A computer program product, characterized in that, when run on a processor, causes the vehicle localization arrangement to perform the method according to any of claims 1-5.

Technical Field

The application relates to the technical field of vehicle networking, in particular to a vehicle positioning method and device.

Background

In an automatic driving system, the positioning of the vehicle itself is an important link. The current positioning method generally adopts a feature matching method, a particle filtering algorithm and the like to complete the positioning of the vehicle according to observation data acquired by different sensors on the vehicle. The sensors on the vehicle may include, for example, a Global Navigation Satellite System (GNSS), an Inertial Measurement Unit (IMU), a light detection and ranging (LiDAR), or a camera, etc.

Currently, the particle filter algorithm is a process of obtaining a minimum variance estimate of a system state by finding a group of random samples propagated in a state space to approximately represent a probability density function and substituting an integral operation with a sample mean value. The automatic driving scene has various design applicable domains (ODD), and in the prior art, when the particle filtering is applied to vehicle positioning, the conditions that different vehicle sensors have different observation noises and state transition process noises under different ODDs are not considered, so that not only is the calculation space wasted, but also the problems that the positioning performance of the vehicle cannot be guaranteed and the positioning result deviation is large are caused.

Disclosure of Invention

The application provides a vehicle positioning method and device, which are used for solving the problem that the positioning performance of a vehicle cannot be guaranteed in the prior art.

In a first aspect, the present application provides a vehicle positioning method, which may be performed by a vehicle positioning module, or may also be performed by a positioning server on an automatic driving system network side. Alternatively, the method may be performed by other devices having a positioning function. In the method, a first position of the vehicle at a first time may be determined, where the first position may be obtained by receiving a message, or obtained by observation, for example, an approximate position obtained by positioning with a positioning module in the vehicle, such as a GPS module, or an approximate position where the vehicle is located and obtained by positioning with a positioning server based on a cell positioning manner; a first design applicability ODD for the vehicle at the second time is then determined, which may be determined in a variety of ways, such as from collected observation data, user input, the first location, or received indications of other devices. Wherein the first ODD is indicative of an operating condition of the vehicle or an environmental condition in which the vehicle is located. And further sampling a plurality of particles for particle filtering around the first location according to the first ODD. Wherein the number and/or distribution of the plurality of particles corresponds to the first ODD; and finally, determining a second position of the vehicle at a second time according to the plurality of sampled particles. It is understood that the second time is after the first time. According to the embodiment of the invention, the number or distribution of the sampling particles for positioning calculation is set in a self-adaptive manner according to the ODD, so that the collected particles are distributed near the real position of the vehicle more densely, particularly in the iterative process of particle filtering, the positioning result of the particles can finish the self-positioning of the vehicle more accurately, and the accuracy and the efficiency of the automatic driving vehicle positioning are effectively improved.

Based on the scheme, when the vehicle is positioned by adopting a particle filtering algorithm, the first ODD of the vehicle can be determined, and the plurality of particles for particle filtering can be sampled around the first position according to the first ODD, wherein the number and/or distribution of the plurality of particles can correspond to the first ODD, so that the plurality of sampled particles can accord with the running condition of the vehicle, and the positioning performance of the vehicle can be better improved when the vehicle is positioned based on the plurality of sampled particles.

In one possible implementation, the autopilot system may also determine a target level for the first ODD for the vehicle at the second time after determining the first ODD. An autopilot system may collect a plurality of particles for particle filtering around the first location based on the target level. In particular, the number and/or distribution of the plurality of particles corresponds to the target level. In one example, the number of the plurality of particles may be positively correlated with the target level, and the higher the level of the first ODD, the greater the number of particles. The positive correlation may be achieved by an exponential function, a direct proportional function, or a look-up table mapping with respect to the target level.

Based on the scheme, the number and/or distribution of the sampled plurality of particles may correspond to the target level of the first ODD, and a plurality of particles more conforming to the operating condition of the vehicle, or a plurality of particles having a distribution density or distribution area more conforming to the operating condition of the vehicle may be determined according to the target level of the first ODD, so that the positioning performance of the vehicle may be improved, and the positioning result may be more accurate.

In one possible implementation, the number or distribution of the plurality of particles may also correspond to a sensor type of the vehicle.

Based on the scheme, the number or distribution of the sampled plurality of particles may also correspond to the target level of the first ODD and the sensor type of the vehicle, so that the sampled plurality of particles may better conform to the operating conditions of the vehicle, and the positioning result may be more accurate.

In a possible implementation, after obtaining the second position of the vehicle at the second time, the second position may also be determined as a new first position, and the vehicle is iteratively located according to the above method of the embodiment of the present application. In iteratively locating a vehicle, if it is determined that the vehicle switches from a first ODD to a second ODD, a plurality of particles for particle filtering may be sampled around the new first location according to the second ODD. Wherein the number and/or distribution of the plurality of particles collected again corresponds to the second ODD; and finally, re-determining a second position of the vehicle at a third time after the first time according to the plurality of particles obtained by re-sampling.

Based on the scheme, when the ODD of the vehicle changes, the plurality of particles for particle filtering can be resampled around the first position of the vehicle according to the transformed ODD, the number or distribution of the plurality of particles for particle filtering can be reset according to the change of the running condition of the vehicle, and the positioning result can be more accurate along with the change of the current environment and the running condition of the vehicle.

In a possible implementation manner, after obtaining the second position of the vehicle at the second time, the second position may also be determined as a new first position, and the vehicle may be continuously located according to the above method of the embodiment of the present application. When the vehicle is continuously located, if the number of the collected valid particles in the plurality of particles is less than the threshold value and the first ODD is not switched, an operation of deleting a part of invalid particles and copying a part of valid particles may be performed first, and after the operations of deleting and copying the particles are performed, a third position of the vehicle at a third time after the first time may be determined based on the plurality of particles obtained after the operation. In terms of how to determine whether a particle is a valid particle, for example, each particle in the collected multiple particles is configured with a corresponding weight value, the weight values of different particles may be the same or different, and a particle with a weight value greater than a certain threshold may be used as a valid particle, or a particle with a weight value less than a certain threshold may be used as an invalid particle.

Based on the scheme, when the ODD of the vehicle is not changed and the number of the effective particles is smaller than the threshold value, the operation of copying the particles can be continuously performed by adopting part or all of the effective particles, so that the number of the copied particles meets the requirement of the ODD of the vehicle, and the positioning of the vehicle is more accurate.

In a second aspect, embodiments of the present application further provide a vehicle positioning apparatus, which may be used to perform the operations in the first aspect and any possible implementation manner of the first aspect. For example, the positioning apparatus may comprise means or elements for performing the respective operations in the first aspect or any possible implementation manner of the first aspect. For example comprising a processing unit and an acquisition unit.

In a third aspect, an embodiment of the present application provides a vehicle positioning apparatus, including a processor, and optionally a memory; wherein the memory stores a computer program and the processor is configured to retrieve and run the computer program from the memory, such that the vehicle localization apparatus performs any of the methods of the first aspect or any possible implementation manner of the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes: computer program code which, when run by a communication unit, a processing unit or a transceiver, a processor of a vehicle localization apparatus, causes the vehicle localization apparatus to perform any of the methods of the first aspect or any possible implementation manner of the first aspect described above.

In a fifth aspect, the present application provides a computer-readable storage medium storing a program, where the program causes a vehicle positioning apparatus to execute any one of the above-mentioned methods of the first aspect or any possible implementation manner of the first aspect.

For technical effects that can be achieved by any one of the second aspect to the fifth aspect or any one of the designs of any one of the second aspect to the fifth aspect, reference may be made to the technical effects that can be achieved by any one of the designs of the first aspect or the first aspect, and repeated description thereof is omitted here.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

FIG. 1 is a diagram illustrating an exemplary application scenario according to an embodiment of the present application;

FIG. 2 is an exemplary flow chart of a vehicle locating method provided by an embodiment of the present application;

FIG. 3 is a schematic diagram of particle distributions under different ODDs in an embodiment of the present application;

FIG. 4 is a schematic diagram of particle sampling under different ODDs in the embodiment of the present application;

FIG. 5 is a schematic illustration of an effective particle distribution in an embodiment of the present application;

FIG. 6 is a schematic diagram of an efficient particle replication operation in an embodiment of the present application;

fig. 7 is a block diagram of a vehicle positioning device according to an embodiment of the present disclosure;

fig. 8 is a block diagram of another vehicle positioning device according to an embodiment of the present disclosure.

Detailed Description

In order to understand the technical solutions provided in the embodiments of the present application, the terms in the embodiments of the present application are explained first:

1) an Operational Design Domain (ODD) refers to the condition or range of applicability under which the autonomous driving system is designed to function, including but not limited to vehicle speed, geographic location, traffic conditions, road type, weather, time, environment, country or local traffic laws, etc. Also commonly referred to as a run design domain, a design run domain, and the like.

2) The particles refer to random samples propagated in a state space and can be used as reference points for determining the position of a vehicle in an automatic driving system, and a large number of particles can simulate the motion state and the motion track of the vehicle and are used for carrying out particle filtering.

The technical solution of the present application will be described below with reference to the accompanying drawings.

The technical scheme provided by the embodiment of the application can be applied to various automatic driving systems, such as an automatic driving system (AD) system, an advanced automatic driving system (HAD) system, an assisted driving system (ADAS), a future automatic driving system and the like.

This application is intended to present various aspects, embodiments or features around a system that may include a number of devices, components, modules, and the like. It is to be understood and appreciated that the various systems may include additional devices, components, modules, etc. and/or may not include all of the devices, components, modules etc. discussed in connection with the figures. Furthermore, a combination of these schemes may also be used.

In addition, in the embodiments of the present application, the word "exemplary" is used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the term using examples is intended to present concepts in a concrete fashion.

The network architecture and the service scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not form a limitation on the technical solution provided in the embodiment of the present application, and as a person of ordinary skill in the art knows that along with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

In the embodiment of the present application, information (information), signal (signal), message (message), channel (channel) may be mixed, and it should be noted that the intended meanings are consistent when the differences are not emphasized. "of", "corresponding", and "corresponding" may sometimes be used in combination, it being noted that the intended meaning is consistent when no distinction is made.

For the convenience of understanding the embodiments of the present application, the communication system according to the embodiments of the present application will be described in detail by taking the communication system shown in fig. 1 as an example. Fig. 1 shows a schematic diagram of a communication system suitable for use in the vehicle positioning method of the embodiment of the present application. As shown in fig. 1, the communication system 100 includes a network device 101, a location server 103 on the network side, a vehicle 102, and a control management device 104 on the vehicle 102. The network device 101 may be configured with multiple antennas, and the vehicle 102 may also be configured with multiple antennas. Optionally, the communication system may further include the network device 105, and the network device 105 may also be configured with multiple antennas.

It should be understood that network device 101 or network device 105 may also include a number of components associated with signal transmission and reception (e.g., processors, modulators, multiplexers, demodulators, demultiplexers, etc.).

The technical solution provided in the embodiment of the present application may be executed by a positioning module having a positioning function in the control management device 104 or may also be executed by the positioning server 103 on the network side in fig. 1.

The network device may be a device with a wireless transceiving function or a chip provided to the device, and the device includes but is not limited to: evolved Node B (eNB), Radio Network Controller (RNC), Node B (NB), Base Station Controller (BSC), Base Transceiver Station (BTS), home base station (e.g., home evolved Node B, or home Node B, HNB), baseband unit (BBU), wireless fidelity (WIFI) system Access Point (AP), wireless relay Node, wireless backhaul Node, transmission point (TRP or transmission point, TP), etc., and may also be 5G, such as NR, a gbb in the system, or a transmission point (TRP or TP), a set (including multiple antennas) of a base station in the 5G system, or a panel of a base station (including multiple antennas, or a BBU) in the 5G system, or a Distributed Unit (DU), etc.

If the network device is a base station, different base stations in this embodiment may be base stations with different identifiers, or base stations with the same identifier and deployed in different geographic locations. Since the base station does not know whether the base station relates to the application scenario of the embodiment of the present application before the base station is deployed, the base station or the baseband chip should support the method provided by the embodiment of the present application before the base station is deployed. It is to be understood that the aforementioned base stations with different identities may be base station identities, cell identities, or other identities.

In some deployments, the gNB may include a Centralized Unit (CU) and a DU. The gNB may also include a Radio Unit (RU). The CU implements part of the function of the gNB, and the DU implements part of the function of the gNB, for example, the CU implements Radio Resource Control (RRC) and Packet Data Convergence Protocol (PDCP) layers, and the DU implements Radio Link Control (RLC), Medium Access Control (MAC) and Physical (PHY) layers. Since the information of the RRC layer is eventually converted into or from the information of the PHY layer, the higher layer signaling, such as RRC layer signaling or PDCP layer signaling, can also be considered to be sent by the DU or the DU + CU under this architecture. It is to be understood that the network device may be a CU node, or a DU node, or a device including a CU node and a DU node. In addition, the CU may be divided into network devices in the access network RAN, or may be divided into network devices in the core network CN, which is not limited herein.

In this communication system 100, each of the network device 101 and the network device 105 can communicate with a plurality of vehicles (for example, the vehicle 102 shown in the figure). It should be understood that network device 101 and network device 105 may each communicate with one or more vehicles similar to vehicle 102, and that vehicle 102 may implement autonomous driving (also referred to as unmanned) functionality by communicating with different network devices. It should be understood that the vehicle communicating with network device 101 and the vehicle communicating with network device 105 may be the same or different. Vehicle 102 shown in fig. 1 may communicate with both network device 101 and network device 105, but this is merely illustrative of one possible scenario, and in some scenarios, vehicle 102 may only communicate with network device 101 or network device 105, and the present application is not limited thereto.

It should be understood that fig. 1 is a simplified schematic diagram of an example for ease of understanding only, and that other network devices or other vehicles, not shown in fig. 1, may also be included in the communication system.

In the system architecture shown in fig. 1, during the driving process of the vehicle 102, information such as road information on a lane where the vehicle 102 is located, obstacle information, and a position of the vehicle 102 on the lane needs to be collected in detail, so as to control the driving direction and speed of the vehicle 102 according to the collected information. In contrast to ordinary vehicles, the accuracy of recognition required in the field of automated driving is not only "what road the vehicle 102 is traveling on", but "which lane the vehicle 102 is traveling on". Usually, the width of a lane is only 2.7m-4.6m, the allowable error is very small, and therefore, the realization of automatic driving does not have the requirement of high-precision positioning of the vehicle.

Based on fig. 1 or other system architectures similar to the system architecture shown in fig. 1, fig. 2 is an exemplary flowchart of a vehicle positioning method provided in the embodiment of the present application, and may include the following processes:

step 201: a first position of the vehicle at a first time is determined.

In one possible implementation, the first position of the vehicle may be determined by a control management device installed in the vehicle; for example, the control management device of the vehicle may perform a positioning operation on the position of the vehicle at the first time by controlling a component having a positioning function, such as a Global Positioning System (GPS), a Beidou satellite navigation system (BDS), and the like, which is installed in the vehicle itself or a communication device carried in the vehicle, so as to obtain the first position of the vehicle at the first time. Alternatively, the first position may be an approximate position observed by a control management device of the vehicle by controlling a sensor mounted on the vehicle itself, and the sensor may be a sensor such as a camera, a Global Navigation Satellite System (GNSS), or the like. In practice, the control and management device may calculate the first position by observing data obtained by a sensor of the vehicle. For example, the GNSS obtains distance information between the vehicle and the satellite at the first time, reports the distance information to the control management device, and calculates the first position of the vehicle at the first time according to the distance information.

In another possible implementation, the first position of the vehicle may also be determined by a positioning server on the network side of the autonomous driving system. The location server may identify a first location of the vehicle at a first time based on cell information in which the vehicle was located at the first time.

It should be noted that the first position here may be a positioning result obtained by the last calculation based on the iterative operation of the present scheme. Based on the iterative operation, the positioning result can be continuously updated according to the change of the environment and the state. The first position may also be an approximate initial position of the vehicle, for example, the first position may be an approximate fuzzy position of the vehicle, for example, the first position may indicate which area or which road the vehicle is in at the first time, and may not give information such as the specific latitude and longitude of the vehicle, that is, the first position is position information with relatively low precision, which does not meet the requirement of positioning the vehicle in the current automatic driving system. Therefore, in order to meet the requirement of the automatic driving system for high-precision positioning of the vehicle, it is necessary to continuously determine the high-precision position of the vehicle according to the first position, and the position of the vehicle with higher precision than the first position is referred to as a second position in this application.

In the following description of the present application, a plurality of reference particles for determining second position information are determined around a first position based on a first position of a vehicle at a first time, which is roughly determined preliminarily, in combination with current ODD information of the vehicle, and a second position with higher accuracy is determined by a particle filter algorithm according to the first position and the determined plurality of reference particles. Because more information is collected and used when the second position is obtained through calculation, iteration is performed through a particle filtering algorithm, and the accuracy of the positioning result is higher.

Step 202: a first design applicability ODD for the vehicle at a second time is determined.

The first ODD of the vehicle may be determined based on observations of sensors of the vehicle. The operating conditions of the vehicle may include road type, weather, time, traffic characteristics, vehicle speed, local traffic laws, and the like. The traffic characteristic here can be a participant of the traffic, for example a pedestrian, a bicycle, a traffic light, etc.

In the embodiment of the present application, the corresponding relationship between different ODDs and different observation data may be stored in advance. In this way, after the sensor of the vehicle acquires the observation data of the vehicle at the second time point, the first ODD corresponding to the vehicle at the second time point can be determined according to the corresponding relationship.

Step 203: according to the first ODD, a plurality of particles for particle filtering are sampled around a first location, the number or distribution of the plurality of particles corresponding to the first ODD.

In one embodiment, a number of the plurality of particles may be determined based on the first ODD, and the sampling may be performed around the first location according to a predetermined distribution of the plurality of particles. Alternatively, the distribution of the plurality of particles may also be determined based on the first ODD and sampled around the first location according to a predetermined number of the plurality of particles. Alternatively, the number and distribution of the plurality of particles may be determined according to the first ODD, and the sampling may be performed around the first position, which is not particularly limited in the embodiment of the present application.

Embodiments of determining the number or distribution of the plurality of particles according to the first ODD are described below, respectively.

First, the number of the plurality of particles is determined according to the first ODD.

In an embodiment of the present application, the number of the plurality of particles may be related to a characteristic of the first ODD. In the following, a brief description will be given, taking the type of road on which the vehicle is located as an example, without loss of generality.

When the vehicle runs on the expressway, the first ODD is used for representing the expressway, and the characteristics of the first ODD can include high speed, sparse obstacles, simple traffic characteristics and the like. Thus, the noise covariance of the localization process and the observation noise covariance are small, and then a smaller number of particles may be collected at the periphery of the first location according to the first ODD.

When the vehicle runs on the urban block road, the first ODD is used for representing the urban block road, and the characteristics of the first ODD comprise low vehicle speed, dense obstacles, complex traffic characteristics and the like. Thus, the noise covariance of the positioning process and the observation noise covariance are larger, and then a larger number of particles may be collected at the periphery of the first location according to the first ODD.

In one possible implementation, a corresponding relationship between different ODDs and the number of particles to be collected may be pre-established, so that when the ODD where the vehicle is currently located is determined, how many particles need to be collected around the first position of the vehicle may be determined according to the corresponding relationship.

In another possible implementation, the determination of how many particles need to be collected around the first position of the vehicle may also be determined according to the ODD where the vehicle is currently located, as a function of the ODD and the number of particles that need to be collected.

In one example, each ODD may be divided into a plurality of levels, i.e., one ODD includes a plurality of levels. The plurality of levels included in one ODD respectively correspond to the number of the plurality of particles to be collected. For example, the number of the plurality of particles to be collected may be positively correlated with one level under the corresponding ODD, i.e., the higher the one level under the ODD, the larger the number of the plurality of particles to be collected. For example, positive correlation may be expressed exponentially and in a proportional manner.

Specifically, a level under the ODD and the number of particles that need to be collected may conform to the following equation (1) or equation (2).

Formula (1) of where

Wherein, N is the number of a plurality of particles to be collected, a is a preset integer greater than 1, f is a preset positive integer, and i is a grade under ODD.

N ═ f ═ i formula (2)

Alternatively, the number of the plurality of particles to be collected may also be inversely related to a level under the corresponding ODD, i.e. the higher the level under the ODD, the smaller the number of the plurality of particles to be collected. It should be understood that when the number of the particles to be collected is positively related to the next level of the ODD, the division of the plurality of levels of the ODD may be different from that when the number of the particles to be collected is negatively related to the next level of the OD.

Based on the scheme, the specific target grade of the vehicle under the first ODD can be determined according to the information such as the current environment of the vehicle, and then the number of the plurality of particles needing to be collected around the first position of the vehicle is determined according to the specific target grade of the vehicle under the first ODD and the mode, so that the number of the sampled particles can better meet the running condition of the vehicle, and the positioning performance of the vehicle can be improved.

In yet another possible implementation, the number of the plurality of particles that need to be collected may also correspond to the sensor type of the vehicle and the specific target class of the vehicle under the first ODD. In the embodiment of the present application, the candidate ranges of the number of the plurality of particles may be configured for the sensor type of the vehicle and the plurality of levels included under the first ODD to which the vehicle belongs. At the time of sampling, a number may be randomly selected among the candidate ranges. Hereinafter, the above-described manner of determining the number of the plurality of particles will be described by taking the first ODD as an expressway and the first ODD as a city block road, respectively, as an example.

Combining the determination manners of the above equations (1) and (2), and the determination manner of the candidate range, a relationship table as shown in table 1 may be given, for example, when the first ODD is used to characterize an expressway, levels 1 to 3 may be included in the first ODD, and the number of the plurality of particles as shown in table 1 may be determined by the above plurality of methods for each level.

Combining the determination methods of the above equations (1) and (2) and the determination method of the candidate range, a relationship table shown in table 2 can be given, for example, when the first ODD is used to characterize a city block road, the first ODD may include levels 1 to 3, and the number of the plurality of particles shown in table 2 can be determined by the above methods for each level.

Based on the method, the number of the plurality of particles used for particle filtering can be determined according to the first ODD, so that when the first position is iterated through a particle filtering algorithm, the adopted information is closer to the running condition of the vehicle, the positioning performance of the vehicle can be improved, and the positioning result (the second position in the embodiment of the application) of the vehicle can be more accurate.

Second, the distribution of the plurality of particles is determined according to the first ODD.

In an embodiment of the present application, the distribution of the plurality of particles may be related to the characteristics of the first ODD. In the following, a brief description will be given, taking the type of road on which the vehicle is located as an example, without loss of generality.

When the vehicle runs on the expressway, the first ODD is used for representing the expressway, and the characteristics of the first ODD can include high speed, sparse obstacles, simple traffic characteristics and the like. Thus, the noise covariance and the observation noise covariance of the localization process are small, and the plurality of particles collected around the first location may exhibit an aggregated distribution pattern.

When the vehicle runs on the urban block road, the first ODD is used for representing the urban block road, and the characteristics of the first ODD comprise low vehicle speed, dense obstacles, complex traffic characteristics and the like. Thus, the noise covariance and the observation noise covariance of the localization process are large, and the plurality of particles collected around the first location may exhibit a discrete distribution pattern.

As shown in fig. 3, (1) is a schematic diagram of a distribution of a plurality of particles collected around a first location of a vehicle when the first ODD is used to characterize an expressway, and (2) is a schematic diagram of a distribution of a plurality of particles collected around a location of a vehicle when the first ODD is used to characterize a city block road.

Hereinafter, a method of determining the distribution of a plurality of particles in the embodiments of the present application will be described in detail. Wherein the distribution of the plurality of particles may be determined according to a distribution variance of the plurality of particles or a particle interval of the plurality of particles.

In an example, in the embodiment of the present application, each ODD may be divided into a plurality of levels, that is, a plurality of levels are included in one ODD. The plurality of levels included in one ODD respectively correspond to the distribution variance of the particles to be collected. For example, the distribution variance of the particles to be collected may be positively correlated with a level under the corresponding ODD, that is, the higher the level under the ODD, the larger the distribution variance of the particles to be collected. Specifically, the distribution variance may conform to the following formula (3):

o' ═ f i formula (3)

Where o' is the distribution variance of the plurality of particles.

In another example, a plurality of levels included in one ODD respectively correspond to particle intervals of particles to be collected. In the embodiment of the present application, the corresponding particle interval can be determined according to the current target level of the first ODD of the vehicle.

In the embodiment of the present application, after the number and the particle interval of the plurality of particles are determined, uniform sampling is performed around the first position of the vehicle in accordance with the number and the particle interval. Alternatively, the centralized sampling may be performed at the first position of the vehicle according to the number and the inter-particle distance, and the centralized sampling may be performed at a specific area of the first position. The concentrated sampling may be uniform sampling, such as the same spacing between particles.

As shown in fig. 4, (1) and (2) are schematic views of uniform sampling around the first position of the vehicle, and (3) is a schematic view of concentrated sampling at the first position of the vehicle.

Based on the scheme, the distribution of the plurality of particles can be determined according to the target level of the first ODD, the sampled distribution of the plurality of particles can be made to meet the operating conditions of the vehicle, and the positioning performance of the vehicle can be improved.

Step 204: a second location of the vehicle at a second time is determined based on the plurality of particles.

The second time here is after the aforementioned first time. In an embodiment of the application, after determining the number and distribution of the plurality of particles for particle filtering, a second position of the vehicle at a second time may be determined according to a particle filtering algorithm. Based on the scheme, a plurality of particles can be sampled around the first position according to the first ODD, the number or distribution of the plurality of particles can be made to correspond to the first ODD, and the positioning performance of the vehicle can be improved.

After the second position is obtained, the second position may be determined as a new first position in the embodiment of the present application, and the vehicle is continuously located by the locating method provided in the embodiment of the present application. For example, if the second position a of the vehicle is obtained according to the foregoing steps 201 to 204, the point a may be determined as a new first position, and the vehicle may be continuously located according to the steps 201 to 204 to obtain a new second position.

It should be noted that, after the second location is determined as the new first location, if the first ODD is switched to the second ODD, the number or the distribution of the plurality of particles used for particle filtering may be determined again at the new first location according to the technical solution provided in the embodiment of the present application, and the specific implementation method is the same as the above method, and is not described herein again.

If the ODD of the vehicle is still the first ODD, then the valid particle of the sampled plurality of particles may be determined. Wherein a weight value of each of the plurality of particles may be determined, and a valid particle may be determined according to the weight value. Hereinafter, a method of determining effective particles will be described with reference to fig. 5.

As shown in fig. 5, a vehicle travels on a road with a plurality of traffic participants, such as signal lights 501, road signs 502, buildings 503, etc. in fig. 5. The landmark signpost 501 may be selected in the first position according to a preset rule. The control management apparatus of the vehicle may calculate a first distance of the plurality of particles to the road sign 501 and a second distance of the vehicle to the road sign 501 from data observed by a sensor installed in the vehicle itself. The higher the similarity between the first distance and the second distance is, the larger the weight value of the particle corresponding to the first distance is. When the weight value is less than or equal to the first threshold, the particle corresponding to the first distance may be determined to be an invalid particle. In fig. 5, the black particles are effective particles, and the white particles are ineffective particles.

Alternatively, when the difference between the first distance and the second distance is greater than the second threshold, the particle corresponding to the first distance may be determined to be an invalid particle. The first threshold and the second threshold may be preset according to empirical values, and the present application is not particularly limited.

When the number of the effective particles is smaller than the threshold, the second position at the second time may be determined as a new first position according to the technical scheme provided in the embodiment of the present application, and the number and/or distribution of the plurality of particles used for particle filtering are sampled around the new first position. When the number of valid particles is greater than or equal to the threshold, the second location may be determined as a new first location around which a portion of invalid particles may be deleted and a portion of valid particles may be copied, with which the vehicle may continue to be located using the particle filtering algorithm.

In one embodiment, when a copy valid particle operation is performed, a particle may be sampled at the new first location that is the same as the location of the valid particle. The same position may mean that the distance of the particles from the vehicle is the same, or that the distribution of the particles around the vehicle is the same. As shown in fig. 5, the black particles are valid particles, and the point b is a second position of the vehicle at a second time, i.e., a new first position. According to the technical scheme in the embodiment of the application, all effective particles can be copied for particle filtering. After the particle copy operation, the number and distribution of the particles are shown in fig. 6, and the position of the vehicle at the third time can be determined by using a particle filter algorithm according to the particles shown in fig. 6.

In the embodiment of the present application, a policy for resetting the number or distribution of the plurality of particles may be stored in advance, as shown in table 3:

based on the scheme, when the ODD of the vehicle is not changed and the number of the effective particles is smaller than the threshold value, the operation of copying the particles can be continuously performed by adopting part or all of the effective particles, so that the number of the copied particles meets the requirement of the ODD of the vehicle, and the positioning of the vehicle is more accurate.

The vehicle positioning method according to the embodiment of the present application is described in detail above with reference to fig. 1 to 6. The vehicle positioning device according to the embodiment of the present application will be described in detail below with reference to fig. 7 to 8.

The vehicle locating device 700 includes one or more processors 701, and the one or more processors 701 may implement the method of the embodiment shown in fig. 2.

In one possible design, the vehicle localization apparatus 700 includes means (means) for sampling a plurality of particles for particle filtering around the first location according to the first ODD. The number or distribution of the plurality of particles can be found in the description relating to the above method embodiment. The first ODD may be determined, for example, by one or more processors. Optionally, the processor 701 may also implement other functions besides the method of the embodiment shown in fig. 2.

Alternatively, in one design, the processor 701 may execute instructions that cause the vehicle localization apparatus 700 to perform the methods described in the above method embodiments. The instructions may be stored in whole or in part within the processor, or in whole or in part in a memory 702 coupled to the processor.

In yet another possible design, the vehicle localization device 700 may also include circuitry that may implement the functionality of the foregoing method embodiments.

In yet another possible design, the vehicle positioning apparatus 700 may include one or more memories 702 having instructions 704 stored thereon, which are executable on the processor to cause the vehicle positioning apparatus 700 to perform the methods described in the above method embodiments. Optionally, the memory may further store data therein. Instructions and/or data may also be stored in the optional processor. For example, the one or more memories 702 may store the first ODD described in the above embodiments, or the number or distribution of the plurality of particles involved in the above embodiments, or the like. The processor and the memory may be provided separately or may be integrated together.

In yet another possible design, the communication device 700 may further include a transceiver 705 and an antenna 706. The processor 701, which may be referred to as a processing unit, controls the vehicle locating device. The transceiver 705 may be referred to as a transceiver, a transceiver circuit, or a transceiver, etc. for implementing the transceiving function of the vehicle positioning device through the antenna 706.

In one possible implementation, the vehicle positioning apparatus 800 shown in fig. 8 may be used as a positioning server on the management side or a control management device of the vehicle itself according to the above method embodiment, and execute the steps executed by the positioning server or the management control device of the vehicle in the above method embodiment. As shown in fig. 8, the vehicle positioning apparatus 800 may include a processing unit 801 and an acquisition unit 802, and the processing unit 801 and the acquisition unit 802 are coupled to each other. The processing unit 801 may be used to support the vehicle localization apparatus 800 in performing the processing acts in the above-described method embodiments.

In performing the acts performed by the vehicle locating device in the above-described method embodiments, the processing unit 801 may be configured to determine a first position of the vehicle at a first time; alternatively, the method may be further used to determine a first design applicability domain, ODD, of the vehicle at a second time.

The acquisition unit 802 samples a plurality of particles for particle filtering around the first location according to the first ODD. The number or distribution of the plurality of particles may be referred to in the description of the above method embodiments.

In one design, processing unit 801 may also be configured to determine a second position of the vehicle at a second time based on the plurality of particles.

In one design, the processing unit 801 may be further configured to determine a target level of the first ODD after determining the first ODD for the vehicle at the second time, where the target level of the first ODD and how to determine the target level of the first ODD may be as described above in the method embodiment.

In one design, processing unit 801 may also be used to determine that the first ODD switches to a second ODD. The acquisition unit 802 may also be used to reset the number or distribution of the plurality of particles according to the second ODD. How to reset the number or distribution of the plurality of particles according to the second ODD may be referred to the related description in the above method embodiment.

Alternatively, the processing unit 801 may be further configured to determine that a number of valid particles in the plurality of particles is less than a threshold and that the first ODD has not switched; deleting part of the invalid particles and copying part of the valid particles. Wherein, how to determine the number of effective particles and how to copy part of the effective particles can be referred to the related description in the above embodiment of the method. How to perform heartbeat detection on the server to be upgraded can be referred to the related description in the above method embodiment.

Only one or more of the various elements in fig. 8 may be implemented in software, hardware, firmware, or a combination thereof. The software or firmware includes, but is not limited to, computer program instructions or code and may be executed by a hardware processor. The hardware includes, but is not limited to, various integrated circuits such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or an Application Specific Integrated Circuit (ASIC).

It should be noted that the processor in the embodiments of the present application may be an integrated circuit chip having signal processing capability. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

It will be appreciated that the memory in the embodiments of the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), double data rate SDRAM, enhanced SDRAM, SLDRAM, Synchronous Link DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

The embodiment of the application also provides a computer readable medium, on which a computer program is stored, and the computer program is executed by a computer to implement the vehicle positioning method in any one of the above method embodiments.

The embodiment of the present application further provides a computer program product, and when executed by a computer, the computer program product implements the vehicle positioning method according to any of the above method embodiments.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Video Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.

The embodiment of the application also provides a vehicle positioning device, which comprises a processor and an interface; the processor is used for executing the vehicle positioning method in any one of the above method embodiments.

It should be understood that the vehicle positioning device may be a chip, the processor may be implemented by hardware or may be implemented by software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like; when implemented in software, the processor may be a general-purpose processor implemented by reading software code stored in a memory, which may be integrated in the processor, located external to the processor, or stand-alone.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.