CN113264039B - Vehicle driving method and device based on road side sensing device and vehicle-road cooperative system - Google Patents

Abstract

In accordance with embodiments of the present disclosure, methods, devices, media, and systems are provided for assisting in driving a vehicle. A method of assisting driving of a vehicle, comprising: obtaining, from a sensing device, sensing information about an environment in which the vehicle is located, the sensing information including data associated with each of a plurality of objects in the environment, the sensing device being located in the environment and independent of the vehicle; identifying data of a first object of the plurality of objects included in the perception information as data of the vehicle based at least on the positioning information of the vehicle; and in response to identifying the data of the first object as data of the vehicle, estimating a collision risk of the vehicle with one or more second objects different from the first object based on the data of the first object and the data of one or more second objects of the plurality of objects included in the perception information. Aspects of the present disclosure may provide accurate collision risk prediction for a vehicle at relatively low cost using roadside equipment in an environment.

Description

Vehicle driving method, device and vehicle-road coordination system based on roadside sensing device

This application is a divisional application of the invention patent application with application number 201910356185.2 and titled "Method, device, medium and system for assisting vehicle driving" submitted on April 29, 2019.

technical field

Embodiments of the present disclosure generally relate to the field of out-of-vehicle interaction, and more specifically, relate to a method, device, device, computer-readable storage medium, and assisted driving system for assisting vehicle driving.

Background technique

Compared with vehicle passive safety technology, vehicle active safety technology can provide drivers with timely early warning before danger occurs, thereby avoiding traffic accidents such as collisions, and effectively reducing property damage and casualties.

In the current active safety technology, the realization of vehicle collision warning is mainly based on V2V communication (vehicle-to-vehicle communication), in which the main vehicle receives the location information of other vehicles and performs collision warning for vehicles within a certain distance. However, this requires both the own vehicle and other vehicles to be able to communicate wirelessly, and in order to be able to accurately predict whether a collision will occur, the positioning equipment of all vehicles needs to have high accuracy. However, even if the requirements of communication and positioning are met, due to the limitation of V2V technology, the detection range for collision warning is still relatively limited.

Contents of the invention

Based on the above problems, according to an exemplary embodiment of the present disclosure, a solution for assisting driving of a vehicle is provided.

In a first aspect of the present disclosure, a method of assisting driving of a vehicle is provided. The method includes: obtaining from a sensing device perceptual information related to an environment in which the vehicle is located, the sensing information including data associated with each of a plurality of objects in the environment, the sensing device being located in the environment and independent of the vehicle; at least based on the vehicle In response to identifying the data of the first object as vehicle data, based on the data of the first object and the perception information The included data of one or more second objects different from the first object is used to estimate the collision risk of the vehicle with the one or more second objects.

In a second aspect of the present disclosure, an apparatus for assisting driving of a vehicle is provided. The device includes: a communication module configured to obtain sensing information related to the environment in which the vehicle is located from a sensing device, the sensing information includes data associated with each of a plurality of objects in the environment, the sensing device is located in the environment and independently The identification module is configured to identify the data of the first object among the plurality of objects included in the perception information as the data of the vehicle based at least on the positioning information of the vehicle; and the estimation module is configured to respond to the first The data of the object is identified as the data of the vehicle, and based on the data of the first object and the data of one or more second objects different from the first object among the plurality of objects included in the sensory information, the relationship between the vehicle and the one or more second objects is estimated. The collision risk of two objects.

In a third aspect of the present disclosure, there is provided an apparatus comprising one or more processors; and storage means for storing one or more programs, when the one or more programs are executed by the one or more processors , causing one or more processors to implement the method according to the first aspect of the present disclosure.

In a fourth aspect of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, the program implements the method according to the first aspect of the present disclosure when executed by a processor.

In a fifth aspect of the present disclosure, a driving assistance system is provided. The system includes: a vehicle side auxiliary device including the device according to the second aspect; and a roadside sensing device arranged in the environment and configured to sense objects in the environment and send the sensed information to the vehicle side auxiliary device .

It should be understood that what is described in the Summary of the Invention is not intended to define key or critical features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be readily understood through the following description.

Description of drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent with reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, identical or similar reference numerals denote identical or similar elements, wherein:

FIG. 1 shows a schematic diagram of an example traffic environment 100 in which embodiments of the present disclosure may be implemented;

FIG. 2 shows a schematic diagram of communication between the environment sensing device 120 and the vehicle 110;

FIG. 3 shows a flowchart of a method 300 for assisting driving of a vehicle according to an embodiment of the present disclosure;

FIG. 4 shows a flowchart of a method 400 for identifying vehicle data by the identification module 142 of the driving assistance device 140 in some embodiments; and

Fig. 5 shows a schematic block diagram of an example device 500 that may be used to implement embodiments of the present disclosure.

Detailed ways

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein; A more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only, and are not intended to limit the protection scope of the present disclosure.

In the description of the embodiments of the present disclosure, the term "comprising" and its similar expressions should be interpreted as an open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be read as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same object. Other definitions, both express and implied, may also be included below.

As mentioned above, vehicle collision warning based on V2V technology has many shortcomings. With the development of V2X (Vehicle to Everything) technology, vehicles can obtain environmental information from more external devices. For example, in a vehicle-road coordination system, vehicles can obtain information that is helpful for vehicle driving from roadside equipment. However, there is still a lack of mature and effective solutions for vehicle collision warning using V2X technology.

According to an embodiment of the present disclosure, a solution for assisting driving of a vehicle is proposed. In this solution, the sensing device arranged in the environment can sense multiple objects in the environment where the vehicle is located, and send the sensing information to the vehicle. After acquiring the sensing information, the vehicle-side device on the vehicle may identify an object corresponding to the vehicle among multiple objects sensed by the sensing information based on the positioning information of the vehicle itself. Subsequently, the vehicle can estimate whether the vehicle has a collision risk based on the data of the identified object and the data of other objects in the perception information, so as to provide the driver with a timely collision warning. Since a sensing device such as a roadside sensing system is used to obtain information about the environment in which the vehicle is located, it is no longer required that all vehicles in the environment have wireless communication capabilities, and these vehicles are prevented from using expensive positioning equipment with high positioning accuracy. At the same time, vehicles can also obtain a larger detection range through sensing devices such as roadside sensing systems.

Embodiments of the present disclosure will be specifically described below with reference to the accompanying drawings.

FIG. 1 shows a schematic diagram of an example traffic environment 100 in which embodiments of the present disclosure may be implemented. Some typical objects are schematically shown in this example traffic environment 100 . It should be understood that the facilities and objects shown are only examples, and the objects that may appear in different traffic environments will vary according to actual conditions. The scope of the present disclosure is not limited in this respect.

As shown in FIG. 1 , host vehicle 110 is traveling on road 130 . The vehicle 110 may be any type of vehicle that can carry people and/or objects and move through a power system such as an engine, including but not limited to cars, trucks, buses, electric vehicles, motorcycles, RVs, trains, and the like. Vehicle 110 may be a vehicle driven by a person. In some other embodiments, the vehicle 110 may also be a vehicle with certain automatic driving capabilities.

FIG. 1 also exemplarily shows the road surface of the road 130 and some other objects that may appear on the roadside. For the vehicle 110 , other objects in the environment 100 may be considered obstacles, which may become potential collision objects for the vehicle 110 . In Fig. 1, other objects on the road surface of road 130 include other motor vehicles 101-1 and 101-2, non-motor vehicles such as bicycle 102, and pedestrians 103, and the roadside of road 130 may also include a traffic facilities such as traffic lights 104 . However, it can be understood that the road surface of the road 130 and other objects on the roadside are not limited to the objects listed above, but may be any objects existing around the road 130 . For example, obstacles that may appear on the road surface and roadside of the road 130 may also include plants, animals, road facilities, or any unknown items around the road.

In the example traffic environment 100, the roadside of the road 130 also has environment sensing devices 120-1 and 120-2 (collectively referred to as environment sensing devices 120). The environment sensing device 120 may include one or more sensing devices. The environment sensing device 120 may also include various types of sensing devices. The sensing device of the environment sensing device 120 can perceive any object in the environment 100 and collect information of the corresponding object, such information includes but not limited to location information, size information, shape information or color information and the like. The sensing device of the environment sensing device 120 can be suspended at a position with a certain height by means of roadside facilities or special facilities, so as to be able to sense multiple obstacles in the environment 100 in a wider range.

Examples of sensing devices of the environment sensing device 120 may include, but are not limited to: image sensors (such as cameras), laser radars, millimeter-wave radars, infrared sensors, positioning sensors, light sensors, pressure sensors, temperature sensors, humidity sensors, and wind speed sensors. , wind direction sensor, air quality sensor, etc. The image sensor can collect image information related to the environment 100; the laser radar and the millimeter-wave radar can collect laser point cloud data related to the environment 100; the infrared sensor can use infrared rays to detect the environmental conditions in the environment 100; The location information of the object related to 100; the light sensor can collect the measurement value indicating the light intensity in the environment 100; the pressure, temperature and humidity sensors can collect the measurement value indicating the pressure, temperature and humidity in the environment 100 respectively; wind speed, wind direction sensor The measurement values used to indicate the wind speed and wind direction in the environment 100 can be collected respectively; the air quality sensor can collect some indicators related to air quality in the environment 100, such as oxygen concentration, carbon dioxide concentration, dust concentration, pollutant concentration, etc. in the air . It should be understood that the above lists only some examples of sensing devices. According to actual needs, there may also be other sensing devices of different types. In some embodiments, different sensing devices may be integrated at a certain location, or may be distributed in an area of the environment 100, for monitoring certain types of roadside perception information.

The environment sensing device 120 may also include a communication module. The communication module can support wired/wireless communication with the sensing device, and is used to obtain collected information from the sensing device. The communications module may also support communications with the vehicle 110, typically wireless communications. The communication between the communication module of the environment sensing device 120 and the vehicle 110 may be based on any communication protocol, and the implementation of the present disclosure is not limited in this respect. In addition, the environment sensing device 120 may also include an information processing module, for preprocessing the information collected by the sensing device before sending the information through the communication module. In some embodiments, the environment sensing device 120 may also not have an information processing module.

In addition, at least one of the communication module and the information processing module may be shared among a plurality of environment sensing devices 120 . For example, the environment sensing devices 120-1 and 120-2 may respectively have independent sensing devices, but may have a common communication module and information processing module.

In order to monitor the environment 100 in all directions, any number of environment sensing devices 120 may be arranged near the road 130 . For example, a plurality of environment sensing devices 120 may be arranged on both sides of the road 130 at certain intervals for monitoring a specific area of the environment 100 . In some examples, in addition to being fixed at a specific location, the environment sensing device 120 may also be set on a movable object, thereby forming a mobile sensing station.

FIG. 2 shows a schematic diagram of communication between the environment sensing device 120 and the vehicle 110 . Although FIG. 2 shows the communication from the sensing device 120 to the vehicle 110 , those skilled in the art can understand that communication from the vehicle 110 to the sensing device 120 can also be performed as required. For example, when the sensing device 120 needs to obtain information on the vehicle side, the sensing device 120 may also receive information from the vehicle 110 . The host vehicle 110 may include a driving assistance device 140 . The auxiliary device 140 of the vehicle 110 may obtain sensing information from the environment sensing device 120 to determine whether the vehicle 110 is at risk of collision.

As shown in FIG. 2 , the auxiliary device 140 of the vehicle 110 may include a communication module 141 , an identification module 142 and an estimation module 143 . The communication module 141 is configured as the environment sensing device 120 , especially the communication module in the environment sensing device 120 is communicatively coupled to receive the environment sensing information. The identification module 142 and the estimation module 143 are configured to perform processing on the perception information to determine whether the vehicle 110 has a collision risk. The driving assistance device 140 may further include an early warning module 144 . If it can be determined that the vehicle 110 has a collision risk, the warning module 144 can provide a video or audio warning in the vehicle to remind the driver to adjust operations to avoid the collision.

The following will describe in detail how the driving assistance device 140 in the vehicle 110 implements the collision warning function.

The communication module 141 of the driving assistance device 140 may obtain from the sensing device 120 perceptual information related to the environment 100 where the vehicle 110 is located. The perceptual information may include data associated with each object in the environment 100. The perceiving device 120 May be located in the environment 100 and independent of the vehicle 100 .

As discussed above, a plurality of sensing devices 120 are arranged along the road 130, and sensing devices of the sensing devices 120 may be suspended at a position having a certain height. Therefore, the sensing device 120 can collect a wider range of road condition data with a better perspective in the environment 100 . The information collected by the sensing device 120 may relate to multiple objects in the environment 100 . In other words, the collected information includes data associated with each of the plurality of objects in environment 100 . In some embodiments, the sensing device 120 may include an information processing module, which preprocesses the collected information. For example, the information processing module may identify data associated with a specific object among the plurality of objects in the collected information, and establish a correlation between these data and the specific object. In other embodiments, the sensing device 120 may not have an information processing module, and the corresponding preprocessing tasks may also be completed by the auxiliary device 140 in the vehicle 110 . The communication module of the sensing device 120 may send the sensing information to the vehicle 110 . For example, perception information may be broadcast in environment 100 . Thus, when the vehicle 110 is in the environment 100 , the communication module 141 of the driving assistance device 140 for the vehicle 110 can acquire the sensing information sent by the sensing device 120 .

In an embodiment of the present disclosure, the identification module 142 of the driving assistance device 140 may identify the data of the first object among the plurality of objects included in the perception information as the data of the vehicle 110 based at least on the positioning information of the vehicle 110 .

Specifically, the driving assistance device 140 may obtain the positioning information of the vehicle 110 through a positioning device of the vehicle 110 itself. For example, the driving assistance device 140 can be positioned through a GPS antenna or through an auxiliary device such as a base station. According to the positioning information of the vehicle 110 , the identification module 142 of the auxiliary device 140 can identify which object is the vehicle 110 among the multiple objects involved in the perception information. Subsequently, the identification module 142 may identify the data of the object corresponding to the vehicle 110 in the acquired perception information as the data of the vehicle 110 .

In some embodiments, the data associated with each of the plurality of objects included in the sensory information may include at least positioning data for each object. The positioning data of each object may relate to an absolute position in an earth coordinate system. However, the positioning data for each object may also relate to a relative position with respect to the sensing device 120 .

In some embodiments, the identification module 142 of the auxiliary device 140 may further calculate the distance between the positioning position of the vehicle 110 and the positioning position of each of the multiple objects based on the positioning information of the vehicle 110 and the positioning data of the multiple objects . Then, the identification module 142 may determine objects whose distance from the location location of the vehicle 110 is less than a predetermined threshold among the plurality of objects. In response to only the distance between the location of the first object and the location of the vehicle 110 being less than a predetermined threshold, the identification module 142 identifies the data of the first object included in the sensing information as the data of the vehicle 110 .

In the case where the positioning data of multiple objects in the sensing information is absolute position data, since the positioning obtained by the positioning device of the vehicle 110 itself is usually also an absolute position, it is possible to directly compare the positioning of multiple objects in the sensing information with the vehicle 110's own positioning to determine the distance between them.

In the case where the positioning data of multiple objects in the sensing information is relative to the relative position data of the sensing device 120, the absolute position of the sensing device 120 can be used to determine the position of each object involved in the sensing information in the earth coordinate system. The absolute position is then determined as a distance between the location of the plurality of objects in the sensory information and the location of the vehicle 110 itself. Or, in the case where the positioning data of multiple objects in the sensing information is relative position data relative to the sensing device 120, the positioning obtained by the positioning device of the vehicle 110 itself may also be converted into a relative position relative to the sensing device 120. position, and then determine the distance between the location of the plurality of objects in the sensory information and the location of the vehicle 110 itself.

Then, the identification module 142 may determine which objects among the plurality of objects involved in the perception information are located at a distance from the vehicle 110 that is less than a predetermined threshold. For example, the predetermined threshold may be set as the vehicle length or vehicle width of the vehicle 110, or any other suitable value. In addition, the predetermined threshold can also be set according to the positioning accuracy of the positioning device of the vehicle 110, for example, if the positioning accuracy is high, the predetermined threshold can be set lower, and if the positioning accuracy is low, the predetermined threshold can be set higher.

If the location of only one of the multiple objects involved in the perception information is smaller than the location of the vehicle 110, the identification module 142 can confirm that the object is the vehicle 110, and will identify the data of the object in the perception information as the data of the vehicle , for subsequent processing.

In some embodiments, the data associated with each of the plurality of objects included in the perception information may further include at least size data of each object. For example, dimensional data may be the volume of an object.

In some embodiments, if the location of more than one object among the plurality of objects involved in the perception information is less than a predetermined threshold with the location of the vehicle 110, the identification module 142 may identify each of the more than one objects The size data of the vehicle is compared with the pre-stored size information of the vehicle. Subsequently, the identification module 142 may determine the object whose size is closest to the vehicle 110 as the vehicle 110 , and identify the data of the object as the data of the vehicle 110 . For example, if the volume data of an object in the perception information is closest to the volume data of the vehicle 110 , then this object may be determined as the vehicle 110 .

In some embodiments, if the position of no object among the plurality of objects involved in the sensing information is smaller than the threshold of the position of the vehicle 110, the auxiliary device 140 can determine whether the distance from the vehicle 110 to the sensing device 120 is less than the maximum detection of the sensing device 120 distance. If the judgment result is yes, then the auxiliary device 140 can report an abnormality to remind the system that an error has occurred; Collision warning cannot be provided.

According to an embodiment of the present disclosure, in response to identifying the data of the first object as the data of the vehicle 110 , the estimation module 143 of the driving assistance device 140 may be based on the data of the first object and the perception information among the plurality of objects included in the first object. The collision risk of the vehicle 110 with the one or more second objects is estimated by using the data of the one or more second objects that are different from the objects.

Specifically, the identification module 142 has identified the first object as the vehicle 110, and the estimation module 143 of the auxiliary device 140 can use the data of the first object in the perception information as the data of the vehicle, so that the collision risk of the first object can be estimated to determine the collision risk of the vehicle 110 . Wherein, the estimation module 143 may estimate the collision risk according to the data of the first object and the data of one or more objects different from the first object among the plurality of objects.

In some embodiments, the estimation module 143 may convert the data of one or more second objects in the plurality of objects from a coordinate system referenced to the sensing device 120 or the earth to coordinates referenced to the first object based on the data of the first object Tie. Then, the estimation module 143 estimates the collision risk between the vehicle 110 and the one or more second objects based on the data of the one or more second objects after the coordinate system transformation.

Since the auxiliary device 140 in the vehicle 110 obtains the sensing information from the sensing device 120 , all data in the sensing information is data obtained using the sensing device 120 or the earth as a coordinate system. The estimation module 143 may perform coordinate system transformation on the data in the perception information, so as to transform it into a coordinate system with the first object (which corresponds to the vehicle 110 ) as a reference object. When performing collision prediction in the coordinate system with the first object as the reference object, it is not necessary to consider the trajectory of the first object, but only consider the trajectory of other objects in the plurality of objects, thereby greatly reducing the amount of computation.

In some embodiments, the estimation module 143 may further predict the trajectory of the one or more second objects relative to the first object based on the data of the one or more second objects after the coordinate system is transformed. If the distance between the one or more second objects and the first object in the predicted motion trajectory is smaller than the safety threshold, the estimation module 143 may determine that there is a collision risk between the vehicle 110 and the one or more second objects. If the distance between the one or more second objects and the first object in the predicted motion trajectory is greater than the safety threshold, it may be determined that there is no collision risk between the vehicle 110 and the one or more second objects.

In some embodiments, the driving assistance device 140 of the vehicle 110 may further include an early warning module 144 . If it is determined that there is a risk of collision between the vehicle 110 and one or more second objects, the warning module 144 may issue a video or audio warning in the vehicle 110 to remind the driver to adjust operations to avoid traffic accidents.

In such an implementation, the vehicle 110 can use the roadside equipment in the environment to obtain a wider range of environmental conditions, and can accurately predict the collision of the vehicle itself without the need for other vehicles in the environment 100 to install wireless communication and positioning equipment risk. In the solution of the present disclosure, even if the positioning accuracy of the vehicle 110 itself is limited, the identification module 142 can use a relatively simple algorithm to identify the object corresponding to the vehicle in the perception information. In addition, although the present disclosure requires the use of roadside equipment, the roadside equipment usually does not perform any calculation processing on collision warning, and the main calculation processing is completed by the equipment on the vehicle side, which also effectively reduces the number of roadside equipment. construction cost.

FIG. 3 shows a flowchart of a method 300 for assisting driving of a vehicle according to an embodiment of the present disclosure. The method 300 can be implemented by the driving assistance device 140 in FIG. 2 . In block 301, the driving assistance device 140 may obtain from the sensing device 120 perceptual information related to the environment 100 where the vehicle 110 is located, the perceptual information may include data associated with each of a plurality of objects in the environment 100, the perceiving device 120 May be located in environment 100 and independent of vehicle 110 . In block 302 , the driving assistance device 140 may identify data of a first object among the plurality of objects included in the perception information as data of the vehicle 110 based at least on the positioning information of the vehicle 110 . In block 303, in response to identifying the data of the first object as the data of the vehicle 110, the driving assistance device 140 may, based on the data of the first object and one or more of the plurality of objects included in the sensory information, be different from the first object second object data to estimate the collision risk of the vehicle 110 with one or more second objects.

In some embodiments, the data associated with each of the plurality of objects included in the sensory information may include at least positioning data for each object.

In some embodiments, the data associated with each of the plurality of objects included in the perception information may further include at least size data of each object.

FIG. 4 shows a flowchart of a method 400 for identifying vehicle data by the identification module 142 of the driving assistance device 140 in some embodiments. In block 401 , the driving assistance device 140 may calculate the distance between the positioning position of the vehicle 110 and the positioning position of each of the plurality of objects based on the positioning information of the vehicle 110 and the positioning data of the plurality of objects. In block 402 , the driving assistance device 140 may determine whether there is an object whose distance from the localization position of the vehicle 110 is less than a predetermined threshold among the plurality of objects. In block 403 , if there is an object whose distance from the localization position of the vehicle 110 is less than a predetermined threshold, the driving assistance device 140 may determine whether there is only one object whose distance from the localization position of the vehicle 110 is less than the predetermined threshold. In block 404 , if only the distance between the location of the first object and the location of the vehicle 110 is less than a predetermined threshold, the driving assistance device 140 may identify the data of the first object included in the perception information as the data of the vehicle 110 .

In block 405, if there is more than one object among the plurality of objects whose positioning position is less than the predetermined threshold value from the positioning position of the vehicle 110, the driving assistance device 140 may set the size of each of the more than one objects to The data is compared with pre-stored vehicle 110 dimensional information. In block 406 , if it is determined that the size data of the first object is closest to the size information of the vehicle 110 , the driving assistance device 140 may identify the data of the first object included in the perception information as the data of the vehicle 110 .

In some embodiments, the driving assistance device 140 may convert the data of one or more second objects among the plurality of objects from the coordinate system of the sensing device 120 or the earth to the coordinate system of the first object based on the data of the first object. Coordinate System. The driving assistance device 140 may estimate the collision risk between the vehicle 110 and the one or more second objects based on the data of the one or more second objects transformed from the coordinate system.

In some embodiments, the driving assistance device 140 may predict the trajectory of the one or more second objects relative to the first object based on the data of the one or more second objects after the coordinate system is transformed. If the distance between the one or more second objects and the first object in the predicted motion trajectory is smaller than the safety threshold, the driving assistance device 140 determines that there is a collision risk between the vehicle 110 and the one or more second objects.

In some embodiments, if it is determined that there is a risk of collision between the vehicle 110 and one or more second objects, the driving assistance device 140 issues a visual or audio warning in the vehicle 110 .

Fig. 5 shows a schematic block diagram of an example device 500 that may be used to implement embodiments of the present disclosure. The device 500 can be used to realize the driving assistance device 140 of FIG. 2 . As shown, the device 500 includes a computing unit 501 that can perform computer program instructions stored in a read-only memory (ROM) 502 or loaded from a storage unit 508 into a random-access memory (RAM) 503 in accordance with computer program instructions Various appropriate actions and processes are performed. In the RAM 503, various programs and data necessary for the operation of the device 500 can also be stored. The computing unit 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to the bus 504 .

Multiple components in the device 500 are connected to the I/O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disk, etc. ; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 509 allows the device 500 to exchange information/data with other devices over a computer network such as the Internet and/or various telecommunication networks.

The computing unit 501 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of computing units 501 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 can execute various methods and processes described above, such as the process 300 . For example, in some embodiments, process 300 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 508 . In some embodiments, part or all of the computer program may be loaded and/or installed on the device 500 via the ROM 502 and/or the communication unit 509. When a computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of process 300 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to execute the process 300 in any other suitable manner (eg, by means of firmware).

The functions described herein above may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate array (FPGA), application specific integrated circuit (ASIC), application specific standard product (ASSP), system on a chip (SOC), load programmable logic device (CPLD), etc.

Program codes for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

In addition, while operations are depicted in a particular order, this should be understood to require that such operations be performed in the particular order shown, or in sequential order, or that all illustrated operations should be performed to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while the above discussion contains several specific implementation details, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims (9)

1. A vehicle driving method based on a roadside sensing device, comprising: Perceptual information related to the environment in which the vehicle is located is obtained from a roadside sensing device, the sensing information including data associated with each of a plurality of objects in the environment, the roadside sensing device being located at the environment and independently of the vehicle; identifying data of a first object of the plurality of objects included in the perception information as data of the vehicle based at least on vehicle positioning information, wherein the vehicle positioning information is absolute position information with reference to the earth And when the positioning data of the plurality of objects in the sensing information refers to the relative position information of the roadside sensing device, use the absolute position of the roadside sensing device itself with reference to the earth to map the sensing converting the relative position information of the plurality of objects in the information into absolute position information with reference to the earth, or converting the absolute position information of the vehicle into relative position information with reference to the roadside sensing device; and In response to identifying the data of the first object as the data of the vehicle, one or more of the plurality of objects included in the sensory information based on the data of the first object and the sensory information is different from the first object data of a plurality of second objects to estimate the collision risk of the vehicle with the one or more second objects, Wherein, based on the data of the first object and the data of the one or more second objects in the plurality of objects, estimating the collision risk between the vehicle and the one or more second objects includes: based on the data of the first object, transforming the data of the one or more second objects in the plurality of objects from a coordinate system referenced to the roadside sensing device or the earth to a coordinate frame referenced to the first object coordinate system; and Estimating the risk of collision between the vehicle and the one or more second objects based on the data of the one or more second objects after coordinate system transformation. 2. The method according to claim 1, wherein estimating the collision risk of the vehicle with the one or more second objects based on the data of the one or more second objects transformed into a coordinate system comprises: Predicting the trajectory of the one or more second objects relative to the first object based on the data of the one or more second objects after the coordinate system is transformed; In response to the distance between the one or more second objects and the first object in the predicted motion trajectory being less than a safety threshold, it is determined that there is a collision risk between the vehicle and the one or more second objects. 3. The method of claim 2, further comprising: A visual or audio warning is issued within the vehicle in response to determining that the vehicle is at risk of collision with the one or more second objects. 4. A vehicle driving device based on a roadside sensing device, comprising: a communication module, configured to obtain, from a roadside sensing device, sensing information related to the environment in which the vehicle is located, the sensing information including data associated with each of a plurality of objects in the environment, the road a side sensing device located in the environment and independent of the vehicle; An identification module configured to identify the data of the first object among the plurality of objects included in the perception information as the data of the vehicle based at least on the positioning information of the vehicle, wherein the positioning information of the vehicle is In the case of referring to the absolute position information of the earth and the positioning data of the plurality of objects in the sensing information refers to the relative position information of the roadside sensing device, using the absolute position information of the roadside sensing device itself with reference to the earth convert the relative position information of the plurality of objects in the perception information into absolute position information referring to the earth, or convert the absolute position information of the vehicle into reference to the roadside sensing device relative location information; and an estimation module configured to, in response to identifying the data of the first object as the data of the vehicle, based on the data of the first object and the correlation between the plurality of objects included in the sensory information data of one or more second objects different from the one object, to estimate the collision risk of the vehicle with the one or more second objects, Wherein the estimation module is further configured as: based on the data of the first object, transforming the data of the one or more second objects in the plurality of objects from a coordinate system referenced to the roadside sensing device or the earth to a coordinate frame referenced to the first object coordinate system; and Estimating the risk of collision between the vehicle and the one or more second objects based on the data of the one or more second objects after coordinate system transformation. 5. The apparatus of claim 4, wherein the estimation module is further configured to: Predicting the trajectory of the one or more second objects relative to the first object based on the data of the one or more second objects after the coordinate system is transformed; In response to the distance between the one or more second objects and the first object in the predicted motion trajectory being less than a safety threshold, it is determined that there is a collision risk between the vehicle and the one or more second objects. 6. The apparatus of claim 5, further comprising: An early warning module configured to issue a visual or audio early warning within the vehicle in response to determining that the vehicle is at risk of colliding with the one or more second objects. 7. A device, said device comprising: one or more processors; and A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors implement any one of claims 1-3 method described in the item. 8. A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented. 9. A vehicle-road coordination system, comprising: A vehicle side auxiliary device comprising a device according to any one of claims 4 to 6; and A roadside sensing device is arranged in the environment and is configured to sense objects in the environment and send the sensed information to the vehicle side auxiliary device.

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