CN114676900A - Road danger prediction and path planning method, device, equipment and medium - Google Patents
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Abstract
The application discloses a road risk prediction method, which comprises the following steps: acquiring target object information on a target road, wherein the target object information comprises target object motion information; generating a target object abnormal event according to the target object information; and predicting the danger level of the target road according to the target object abnormal event.
Description
Technical Field
The application relates to the technical field of traffic, in particular to a road danger prediction and path planning method, device, equipment and medium.
Background
In recent years, traffic has rapidly developed, and road traffic construction is being updated every day. Due to different geographical environments, road construction also presents diversity, and when vehicles run on different roads or different scenes, actions of drivers during driving are different, for example, many vehicles are braked emergently (quickly decelerated in short distance) at the exit or the junction of a high-speed ramp or in front of the junction photographed by a red light; in the area with large pedestrian flow at the door of a school or a factory, more pedestrians and low-speed vehicles cross; in the road section of taking a picture without limiting the speed, the vehicles overspeed more, and in conclusion, different traffic events can be generated in different traffic environments. Especially, when a driver is unfamiliar with a driving area, some wrong judgments are made on vehicle driving because some special driving scenes are not known, and a safety accident is caused.
Disclosure of Invention
The embodiment of the specification provides a road risk prediction and path planning method, device, equipment and medium, and is used for solving the problem that the road risk level cannot be accurately predicted in the prior art.
The embodiment of the specification adopts the following technical scheme:
in a first aspect, an embodiment of the present specification provides a road risk prediction method, including the following steps:
acquiring target object information on a target road, wherein the target object information comprises target object motion information;
generating a target object abnormal event according to the target object information;
predicting the danger level of the target road according to the target object abnormal event
In a second aspect, an embodiment of the present specification provides a path planning method based on a road risk, where the method includes:
acquiring global path planning request information sent by a vehicle, and determining a candidate path according to the global path planning request information;
predicting, for any of the candidate routes, a road hazard level of the candidate route according to the method of the first aspect;
sending global path planning information to the vehicle, and determining and displaying recommended paths according to the road danger levels of the candidate paths; or the like, or, alternatively,
Acquiring global path planning request information sent by a vehicle, and determining a candidate path according to the global path planning request information;
sending global path planning information to the vehicle so that the vehicle displays each candidate path according to the global path planning information;
acquiring a target path, and predicting the road danger level of the target path according to the method provided by the first aspect, wherein the target path is a candidate path selected by a vehicle;
and transmitting the road danger level information of the target path to the vehicle so that the vehicle displays the road danger level of the target path.
In a third aspect, an embodiment of the present specification provides a road risk level prediction apparatus, including:
the data acquisition module is used for acquiring target object information on a target road, wherein the target object information comprises at least one of target object speed, target object type, target object longitude and latitude, target object course angle and target object acceleration;
the event generating module is used for generating an abnormal event of the target object according to the information of the target object;
and the danger level prediction module is used for predicting the danger level of the target road according to the target object abnormal event.
In a fourth aspect, an embodiment of the present specification further provides a path planning apparatus based on road risk, including:
the route planning module is used for acquiring global route planning request information sent by a vehicle and determining a candidate route according to the global route planning request information;
a method processing module, configured to predict, for any of the candidate routes, a road risk level of the candidate route according to the method provided in the first aspect;
the pushing module is used for sending global path planning information to the vehicle and determining and displaying recommended paths according to the road danger levels of the candidate paths; or the like, or a combination thereof,
the path planning module is further used for acquiring global path planning request information sent by a vehicle and determining a candidate path according to the global path planning request information;
the pushing module is further configured to send global path planning information to the vehicle, so that the vehicle displays each candidate path according to the global path planning information;
the method processing module is further configured to acquire a target path, and predict a road risk level of the target path according to the method provided by the first aspect, where the target path is a candidate path selected by a vehicle;
The pushing module is further configured to send the road danger level information of the target route to the vehicle, so that the vehicle displays the road danger level of the target route.
In a fifth aspect, embodiments of the present specification further provide an electronic device, including at least one processor and a memory, where the memory stores a program and is configured to enable the at least one processor to execute any one of the road risk prediction method and the road risk-based path planning method.
In a sixth aspect, the embodiments of the present specification further provide a computer-readable storage medium storing computer instructions for causing the computer to execute any one of the road risk prediction method and the road risk-based path planning method described in any one of the above.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects: the driving safety grade of the target road is judged according to the abnormal event generated by the target road, different road danger grades are divided, then the danger grades are issued to vehicles for early warning, then the road with higher road danger grade is avoided through global planning, and the driving safety performance is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a schematic flow chart of a road risk prediction method provided in embodiment 1 of the present specification;
fig. 2 is a schematic structural diagram of a road risk level prediction apparatus provided in embodiment 3 of the present specification.
Detailed Description
In the prior art, the traffic field is developed rapidly, and the road traffic construction is updated every day. Because the prior art does not judge the driving safety level of road driving according to abnormal events generated by road traffic, a driver cannot reasonably avoid dangerous roads in the driving process.
Therefore, embodiments of the present disclosure provide a method, an apparatus, a device, and a medium for predicting road risk and planning a route, where a driving safety level of a target road is judged according to an abnormal event generated by the target road, different road risk levels are divided, the risk levels are issued to vehicles for early warning, and then roads with higher road risk levels are avoided through global planning, so as to improve driving safety performance.
In order to make the objects, technical solutions and advantages of the present application more clear, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present specification and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step are within the scope of the present application.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Example 1
The existing traffic conditions are complex, so that the road construction is diversified, and the actions of a driver during driving are different when the vehicle runs on different roads or different scenes, for example, the number of vehicles in emergency braking is large (short-distance rapid deceleration) at the exit or the junction of a high-speed ramp or in front of the junction with a red light for photographing; in the area with large pedestrian flow at the door of a school or a factory, more pedestrians and low-speed vehicles cross; in the road section of taking a picture without limiting the speed, the vehicles overspeed more, and in conclusion, different traffic events can be generated in different traffic environments. Especially, when a driver is unfamiliar with a driving area, some wrong judgments are made on vehicle driving because some special driving scenes are not known, and a safety accident is caused. Therefore, embodiment 1 provides a road risk prediction method for predicting a road risk level so that a driver can make a reasonable driving strategy according to the road risk level. Referring to fig. 1, fig. 1 is a schematic flow chart of a road risk prediction method according to an embodiment of the present disclosure. The method comprises the following steps:
S101, obtaining target object information on a target road, wherein the target object information comprises target object motion information.
Specifically, the target object includes a target vehicle, a pedestrian and an obstacle, and the target road refers to the road target object motion information in the sensing range of the sensing device, which can be understood as the information of the target vehicle, the information of the pedestrian or the information of other moving objects on the road. The information includes, but is not limited to, object speed information, object type information, object longitude and latitude information, object course angle information, and object acceleration information. The information acquisition mode includes but is not limited to data uploaded to the control end based on a road side sensing system or an intelligent networked vehicle, wherein the intelligent networked vehicle refers to a vehicle capable of interacting data to the control end. The roadside sensing system senses vehicles, non-motor vehicles, pedestrians and the like running on the road through the camera and the millimeter wave radar, and basic data of a target, such as information of target speed, target type, target longitude and latitude, target course angle and the like, are output through the sensing fusion system; the intelligent internet vehicle uploads driving data of the intelligent internet vehicle to the control end, wherein the driving data comprises driving speed, type of the intelligent internet vehicle, longitude and latitude of the intelligent internet vehicle, course angle of the intelligent internet vehicle and other basic information, and the automatic driving vehicle uploads target structured data of surrounding environment sensed by a sensor of the automatic driving vehicle to the cloud end. The control terminal includes, but is not limited to, a server and a terminal server. In this embodiment, a server is adopted, and the specific application can be selected according to needs, which is not limited herein.
According to the embodiment, the data of the target object information on the target road is uploaded to the server through the road side sensing system, the intelligent internet vehicle and the automatic driving vehicle.
It should be understood that the specific references listed above are for illustrative purposes only and should not be construed as limiting the invention in any way.
And S103, generating a target object abnormal event according to the target object information.
Specifically, the server performs a series of target fusion processes through data uploaded by the roadside sensing system and the intelligent networked vehicle through the fusion system, and the server can fully acquire information of a target object on a target road, including information such as target speed, target type, target longitude and latitude, target course angle and target acceleration. An abnormal event is generated based on the data of the target object information.
For example: an abnormal parking event, wherein when the speed of the target vehicle is continuously lower than a preset speed within a period of time, the abnormal parking event of the target object is generated;
specifically, the server generates an abnormal parking event when the speed of the vehicle is lower than a certain value (e.g., 0-3km/h) for a certain period of time according to the speed of the target vehicle.
For another example: an abnormal low-speed event, wherein when the speed of the target vehicle is continuously lower than the average speed of the target road within a period of time, the target object abnormal low-speed event is generated;
For another example: an emergency braking event, wherein when the acceleration of the target vehicle is continuously lower than a preset acceleration within a period of time, an abnormal emergency braking event of the target object is generated;
specifically, the server determines the acceleration of the target vehicle when the acceleration of the target vehicle is below a threshold value for a period of time (e.g., the acceleration is below-4 m/s for 5 consecutive frames)2) The server generates an emergency braking event.
For another example: an overspeed event, wherein when the speed of the target vehicle is continuously higher than the speed limit of the target road within a period of event, an abnormal overspeed event of the target object is generated;
for another example: a pedestrian crossing event is generated when the longitude and latitude position of the pedestrian is in the range of the motor lane of the target road
It should be understood that the specific references listed above are for illustrative purposes only and should not be construed as limiting the invention in any way.
And S105, predicting the danger level of the target road according to the target object abnormal event.
Specifically, event parameters of the target object abnormal event are obtained, wherein the event parameters include but are not limited to event positions, event numbers and event generation time; and predicting the danger level of the target road according to the event parameters.
For example, the risk level of the event position on the target road is predicted based on the event position and the number of events corresponding to the event position, and the risk level of the event position on the target road is higher if the number of events corresponding to the event position is larger, and the risk level of the event position on the target road is lower if the number of events corresponding to the event position is smaller.
Specifically, the road danger level may be divided into several levels, such as first level, second level, third level, fourth level and fifth level, each level is set to a range, such as first level from 1-5, second level from 6-10, and so on. If 20 abnormal events occurred in the area a, the risk rating of the area a was evaluated as four.
According to the driving safety level judging method and device, the driving safety level of the target road is judged according to the abnormal event generated by the target road, different road danger levels are divided, then the danger levels are issued to vehicles for early warning, then the road with the higher road danger level is avoided through global planning, and the driving safety performance is improved.
Further preferably, a time period corresponding to the position of the event is preset; predicting the danger level of the event position in the target road in the time period according to the event number generated in the time period, wherein if the event number in a certain time period is larger, the danger level of the event position in the target road in the time period is higher, and otherwise, the danger level of the event position in the target road in the time period is lower.
For example, a hierarchy of risk levels, such as first, second, third, fourth, and fifth levels, is preset for each day as a time period, each level defining a range, such as 1-5 for first level, 6-10 for second level, and so on. If 20 abnormal events occurred in the cycle of the area a, the risk level of the area a is evaluated as four. Thirty days per month, daily risk rating evaluation can be obtained, and drivers can adopt different driving strategies according to the daily evaluation.
For another example, on a weekly basis, a hierarchy of risk levels, such as first, second, third, fourth, and fifth levels, is preset, each level defining a range, such as 1-5 for first level, 6-10 for second level, and so on. If 20 abnormal events occurred in the cycle of the area a, the risk level of the area a is evaluated as four. Four weeks a month, a weekly assessment of the risk level can be obtained and the driver can adopt different driving strategies based on the weekly assessment.
According to the embodiment of the disclosure, the time period is set, the number of events in the time period is obtained, and the position and time prediction of the risk level can be further improved.
Further preferably, each of the time periods is divided into a plurality of time segments; and predicting the danger level of the event position in the target road in the time period according to the event number generated in each time period, wherein if the event number in a certain time period is larger, the danger level of the event position in the target road in the time period is higher, and otherwise, the danger level of the event position in the target road in the time period is lower.
For example, the time period is one day, one day is 24 hours, and the 24 hours are divided into four time periods, such as 0-6, 7-12, 13-18, 19-24 (0). At the event location, a road hazard level is predicted for the event location based on the number of events generated within each time period. If 20 abnormal events occurred in the third cycle of the area a during the period of 7-12, the risk rating of the area a during the period of 7-12 in the third cycle was evaluated as four. The driver may adopt different driving strategies depending on the evaluation.
According to the embodiment of the disclosure, road danger levels of different road areas in different time periods in one day can be obtained. The driver can go out in different time periods according to the specific division of road grades, and the selection of the trip and the safety rate of the trip are improved.
It should be understood that the specific references listed above are for illustrative purposes only and should not be construed as limiting the invention in any way.
Example 2
On the basis of the embodiment 1, the embodiment 2 provides a path planning method based on road dangerousness, and the method includes:
acquiring global path planning request information sent by a vehicle, and determining a candidate path according to the global path planning request information;
Predicting the road risk level of any candidate route according to the method in the embodiment 1;
and sending global path planning information to the vehicle, and determining and displaying recommended paths according to the road danger levels of the candidate paths. Specifically, when the vehicle sends a global path planning request to the server, the server determines a plurality of candidate paths according to the global path request, and the server performs road risk level prediction on the selected candidate paths according to the method in embodiment 1, and performs one-to-one prediction on the road risk level of each candidate path; and then sending global planning information to the vehicle, namely sending the determined candidate paths and road danger levels corresponding to the candidate paths to the vehicle, and recommending and displaying paths with lower road danger levels to the vehicle.
According to the embodiment, the danger level evaluation can be performed on the road in advance, the efficiency of path planning is improved, meanwhile, the candidate paths with higher safety in the road safety level are pushed to the vehicle, and the driving safety of the vehicle is improved.
Preferably, if the road risk level of the candidate route is predicted, the predicting the road risk level of the candidate route includes: and predicting the road danger levels of the candidate paths in different time periods. Please refer to example 1 for a specific prediction method, which is not repeated here. The road safety grade of the candidate route can be specified to a certain time period, and the convenience and the safety of vehicle driving are improved.
Example 3
On the basis of the embodiment 1, the embodiment 3 provides a path planning method based on road dangerousness, and the method includes:
acquiring global path planning request information sent by a vehicle, and determining a candidate path according to the global path planning request information;
sending global path planning information to the vehicle so that the vehicle displays each candidate path according to the global path planning information;
acquiring a target path, and predicting the road danger level of the target path according to the method in the embodiment 1, wherein the target path is a candidate path selected by a vehicle;
and transmitting the road danger level information of the target path to the vehicle so that the vehicle displays the road danger level of the target path.
Specifically, when the vehicle sends a global path planning request to the server, the server determines a plurality of candidate paths according to the global path request, and after the vehicle selects one candidate path, the server predicts the road risk level of the candidate path selected by the vehicle according to the method in embodiment 1. Then, the server sends the road danger level of the candidate route to the vehicle, so that the vehicle improves the driving alertness of the driver according to the road danger level of the selected candidate route.
The road safety grade prediction method and the road safety grade prediction device can predict the road safety grade of the candidate route selected by the vehicle, so that the vehicle can conveniently select a better route, and the driving safety of the vehicle is improved.
Preferably, after the road risk level is obtained, the candidate route is highlighted on a map, and the displaying method includes: when the time period is that the road danger level is higher, highlighting the candidate route, such as displaying striking red; when the time period is that the road danger level is general, the candidate route is displayed in a bold mode, and yellow is displayed; and when the road danger level progression is lower in the time period, normally displaying. Thereby improving the driving alertness of the driver.
Further preferably, if the road risk level of the target route is predicted, the predicting the road risk level of the target route includes:
predicting road danger levels of the target path in different time periods.
Please refer to example 1 for a specific prediction method, which is not repeated here.
It should be understood that the specific references listed above are for illustrative purposes only and should not be construed as limiting the application in any way.
Example 4
Embodiment 4 provides a road risk level prediction device, please refer to fig. 2, fig. 2 is a schematic structural diagram of a road risk level prediction device provided in embodiment 3, the device includes:
the data acquiring module 201 is configured to acquire target object information on a target road, where the target object information includes target object motion information.
And the event generating module 203 is used for generating the target object abnormal event according to the target object information.
And the danger level prediction module 205 is used for predicting the danger level of the target road according to the target object abnormal event.
The judging module is used for generating an abnormal parking event of the target object when the speed of the target vehicle is continuously lower than a preset speed within a period of time; when the speed of the target vehicle is continuously lower than the average speed of the target road within a period of time, generating an abnormal low-speed event of the target object; when the acceleration of the target vehicle is continuously lower than the preset acceleration within a period of time, generating an abnormal emergency braking event of the target object; when the speed of the target vehicle is continuously higher than the speed limit of the target road within a period of event, generating an abnormal overspeed event of the target object; and when the longitude and latitude positions of the pedestrians are within the range of the motor vehicle lane of the target road, generating a target object crossing road event.
The data acquisition module is further used for acquiring event parameters of the target object abnormal event, wherein the event parameters comprise event positions, event numbers and event generation time.
And the danger level prediction module is also used for predicting the danger level of the target road according to the event parameters.
The judging module is further configured to predict a danger level of the event location in the target road according to the event location and the event number corresponding to the event location, where if the event number corresponding to the event location is larger, the danger level of the event location in the target road is higher, and if the event number corresponding to the event location is smaller, the danger level of the event location in the target road is lower.
And the presetting module is used for presetting a time period corresponding to the event at the position of the event.
And the judging module is further configured to predict a risk level of the event position in the target road in the time period according to the number of events generated in the time period, where if the number of events in a certain time period is larger, the risk level of the event position in the target road in the time period is higher, and otherwise, the risk level of the event position in the target road in the time period is lower.
And the presetting module is also used for dividing each time period into a plurality of time periods.
The judging module is further configured to predict a risk level of the event position in the target road in the time period according to the event number generated in each time period, where if the event number in a certain time period is larger, the risk level of the event position in the target road in the time period is higher, and otherwise, the risk level of the event position in the target road in the time period is lower.
Example 5
Embodiment 5 provides a path planning device based on road danger, includes:
the route planning module is used for acquiring global route planning request information sent by a vehicle and determining a candidate route according to the global route planning request information;
a method processing module, configured to predict, for any of the candidate routes, a road risk level of the candidate route according to the method described in embodiment 1;
the pushing module is used for sending global path planning information to the vehicle and determining and displaying recommended paths according to the road danger levels of the candidate paths;
the path planning module is further used for acquiring global path planning request information sent by a vehicle and determining a candidate path according to the global path planning request information;
The pushing module is further configured to send global path planning information to the vehicle, so that the vehicle displays each candidate path according to the global path planning information;
the method processing module is further configured to acquire a target route, and predict a road risk level of the target route according to the method described in embodiment 1, where the target route is a candidate route selected by a vehicle;
the pushing module is further configured to send the road danger level information of the target route to the vehicle, so that the vehicle displays the road danger level of the target route.
Example 6
Embodiment 6 provides an electronic device, which includes at least one processor and a memory, where the memory stores a program and is configured to enable the at least one processor to execute a road risk prediction method or a road risk-based path planning method according to any one of the foregoing embodiments.
Example 7
Embodiment 7 provides a computer-readable storage medium storing computer instructions for causing a computer to execute a road risk prediction method or a road risk-based path planning method according to any one of the preceding embodiments.
In the 90's of the 20 th century, improvements to a technology could clearly distinguish between improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements to process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain a corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical blocks. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is an integrated circuit whose Logic functions are determined by a user programming the Device. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making integrated circuit chips, such Programming is often implemented by "logic compiler" software, which is similar to the software compiler used in program development and writing, but the original code before compiling is written in a specific Programming Language, which is called Hardware Description Language (HDL), and the HDL is not just one kind but many kinds, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), confluency, CUPL (computer universal Programming Language), HDCal, dl (Java Hardware Description Language), laval, Lola, mylar, HDL, PALASM, and harddl (runtime Description Language).
The most commonly used are VHDL (Very-High-speed Integrated Circuit Hardware Description Language) and Verilog. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, apparatuses, modules or units illustrated in the above-described embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various modules or units by function, respectively. Of course, the functionality of the modules or units may be implemented in the same one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process flow such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory (NVM), such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the system embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The above description is only an example of the specification of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A road risk prediction method, characterized by comprising the steps of:
acquiring target object information on a target road, wherein the target object information comprises target object motion information;
generating a target object abnormal event according to the target object information;
and predicting the danger level of the target road according to the target object abnormal event.
2. The method according to claim 1, wherein the target includes a target vehicle, and the generating of the target abnormal event based on the target information includes:
When the speed of the target vehicle is continuously lower than a preset speed within a period of time, generating an abnormal parking event of the target object; and/or the presence of a gas in the atmosphere,
when the speed of the target vehicle is continuously lower than the average speed of the target road within a period of time, generating an abnormal low-speed event of a target object; and/or the presence of a gas in the atmosphere,
when the acceleration of the target vehicle is continuously lower than the preset acceleration within a period of time, generating an abnormal emergency braking event of a target object; and/or the presence of a gas in the atmosphere,
when the speed of the target vehicle is continuously higher than the speed limit of the target road within a period of event, generating an abnormal overspeed event of the target object; and/or the presence of a gas in the atmosphere,
the target object comprises a pedestrian, and when the longitude and latitude position of the pedestrian is within the range of a motor vehicle lane of the target road, a target object crossing road event is generated.
3. The road risk prediction method according to claim 1, wherein the predicting the risk level of the target road according to the target object abnormal event comprises:
acquiring event parameters of the target object abnormal event;
and predicting the danger level of the target road according to the event parameters.
4. The method according to claim 3, wherein the event parameters include an event location and an event number, and the predicting the risk level of the target road according to the event parameters includes:
Predicting the danger level of the event position in the target road according to the event position and the event number corresponding to the event position, wherein if the event number corresponding to the event position is larger, the danger level of the event position in the target road is higher; and/or the presence of a gas in the atmosphere,
the event parameters also comprise the time for generating the event, and a time period corresponding to the position of the event is preset; judging the number of events generated in the time period according to the time of generating the events corresponding to the abnormal events of the target object, predicting the danger level of the event position in the target road in the time period, wherein if the number of the events in the time period is larger, the danger level of the event position in the target road in the time period is higher; and/or the presence of a gas in the gas,
dividing each of the time periods into a number of time segments;
judging the number of events generated in each time period according to the time of generating the events corresponding to the abnormal events of the target object, predicting the danger level of the event position in the target road in the time period, wherein if the number of the events in a certain time period is larger, the danger level of the event position in the target road in the time period is higher; and/or the presence of a gas in the gas,
If the number of the events corresponding to the event positions is smaller, the danger level of the event positions in the target road is lower; and/or the presence of a gas in the atmosphere,
if the number of the events in the time period is smaller, the risk level of the event position in the target road in the time period is lower; and/or the presence of a gas in the atmosphere,
and if the number of the events in a certain time period is smaller, the risk level of the event position in the target road in the time period is lower.
5. A path planning method based on road dangerousness is characterized by comprising the following steps:
acquiring global path planning request information sent by a vehicle, and determining a candidate path according to the global path planning request information;
predicting, for any of the candidate routes, a road risk level of the candidate route according to the method of any one of claims 1 to 4;
sending global path planning information to the vehicle, and determining and displaying recommended paths according to the road danger levels of the candidate paths; or the like, or a combination thereof,
acquiring global path planning request information sent by a vehicle, and determining a candidate path according to the global path planning request information;
sending global path planning information to the vehicle so that the vehicle displays each candidate path according to the global path planning information;
Acquiring a target path, and predicting the road danger level of the target path according to the method of any one of claims 1 to 4, wherein the target path is a candidate path selected by a vehicle;
and sending the road danger level information of the target path to the vehicle so as to enable the vehicle to display the road danger level of the target path.
6. The method of claim 5, wherein if the road risk level of the candidate route is predicted, predicting the road risk level of the candidate route comprises: predicting road danger levels of the candidate path in different time periods;
or the like, or, alternatively,
if the road danger level of the target path is predicted, predicting the road danger level of the target path comprises:
predicting road danger levels of the target path in different time periods.
7. A road risk prediction device, comprising:
the data acquisition module is used for acquiring target object information on a target road, wherein the target object information comprises target object motion information;
the event generating module is used for generating an abnormal event of the target object according to the information of the target object;
And the danger level prediction module is used for predicting the danger level of the target road according to the target object abnormal event.
8. A road danger-based path planning device is characterized by comprising:
the route planning module is used for acquiring global route planning request information sent by a vehicle and determining a candidate route according to the global route planning request information;
a method processing module for predicting, for any of the candidate routes, a road hazard level for the candidate route according to the method of any of claims 1 to 4;
the pushing module is used for sending global path planning information to the vehicle and determining and displaying recommended paths according to the road danger levels of the candidate paths;
the path planning module is further used for acquiring global path planning request information sent by a vehicle and determining a candidate path according to the global path planning request information;
the pushing module is further configured to send global path planning information to the vehicle, so that the vehicle displays each candidate path according to the global path planning information;
the method processing module is further used for acquiring a target path, and predicting the road danger level of the target path according to the method of any one of claims 1 to 4, wherein the target path is a candidate path selected by a vehicle;
The pushing module is further configured to send the road danger level information of the target route to the vehicle, so that the vehicle displays the road danger level of the target route.
9. An electronic device comprising at least one processor and a memory, the memory storing a program and being configured to the at least one processor to execute a road risk prediction method according to any one of claims 1-4 or a road risk-based path planning method according to claim 5 or 6.
10. A computer-readable storage medium storing computer instructions for causing a computer to execute a road risk prediction method according to any one of claims 1 to 4 or a road risk-based path planning method according to claim 5 or 6.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115092158A (en) * | 2022-07-30 | 2022-09-23 | 重庆长安汽车股份有限公司 | Road form danger level generation method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115092158A (en) * | 2022-07-30 | 2022-09-23 | 重庆长安汽车股份有限公司 | Road form danger level generation method, device, equipment and storage medium |
| CN115092158B (en) * | 2022-07-30 | 2024-11-05 | 重庆长安汽车股份有限公司 | Road form danger level generation method, device, equipment and storage medium |
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