CN111605555A - Recommendation method, device, medium and electronic equipment for vehicle driving strategy - Google Patents

Recommendation method, device, medium and electronic equipment for vehicle driving strategy Download PDF

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Publication number
CN111605555A
CN111605555A CN202010414595.0A CN202010414595A CN111605555A CN 111605555 A CN111605555 A CN 111605555A CN 202010414595 A CN202010414595 A CN 202010414595A CN 111605555 A CN111605555 A CN 111605555A
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China
Prior art keywords
vehicle
driving
target vehicle
driving risk
target
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CN202010414595.0A
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Chinese (zh)
Inventor
侯琛
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Tencent Technology Shenzhen Co Ltd
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Tencent Technology Shenzhen Co Ltd
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Priority to CN202010414595.0A priority Critical patent/CN111605555A/en
Publication of CN111605555A publication Critical patent/CN111605555A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions

Abstract

The embodiment of the application provides a recommendation method and device of a vehicle driving strategy, a computer readable medium and electronic equipment. The method comprises the following steps: acquiring environmental parameters of a road section where a target vehicle is located and driving parameters of each vehicle in the road section, wherein the road section comprises at least two lanes, and the driving parameters comprise vehicle positioning information; according to the vehicle positioning information of each vehicle in the road section, determining a reference vehicle which runs in front of the target vehicle and is closest to the target vehicle in the at least two lanes; calculating driving risk values of the target vehicle in the at least two lanes respectively through a driving risk model based on the driving parameters of the target vehicle and the reference vehicle and the environmental parameters; and recommending the driving strategy of the target vehicle according to the magnitude relation between the driving risk values of the target vehicle in the at least two lanes and the preset driving risk threshold values. The technical scheme of the embodiment of the application can improve the safety of vehicle driving.

Description

Recommendation method, device, medium and electronic equipment for vehicle driving strategy
Technical Field
The application relates to the technical field of computers and safe auxiliary driving, in particular to a recommendation method and device of a vehicle driving strategy, a computer readable medium and electronic equipment.
Background
In a general traffic scene, for example, in a vehicle driving scene of a multi-vehicle road section, a driver generally refers to the driving conditions of surrounding vehicles and determines and selects a driving strategy such as whether to overtake or slow down the vehicle according to the driving experience of the driver.
Disclosure of Invention
The embodiment of the application provides a recommendation method and device of a vehicle driving strategy, a computer readable medium and electronic equipment, so that the safety of vehicle driving can be improved at least to a certain extent.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to an aspect of an embodiment of the present application, there is provided a recommendation method of a vehicle driving strategy, including: the method comprises the steps of obtaining environmental parameters of a road section where a target vehicle is located and driving parameters of each vehicle in the road section, wherein the road section comprises at least two lanes, and the driving parameters comprise vehicle positioning information; according to the vehicle positioning information of each vehicle in the road section, determining a reference vehicle which runs in front of the target vehicle and is closest to the target vehicle in the at least two lanes respectively; calculating driving risk values of the target vehicle in the at least two lanes respectively through a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle and the environmental parameters; and recommending the driving strategy of the target vehicle according to the magnitude relation between the driving risk values of the target vehicle in the at least two lanes and the preset driving risk threshold value.
According to an aspect of an embodiment of the present application, there is provided a recommendation apparatus for a vehicle driving strategy, including: the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring environmental parameters of a road section where a target vehicle is located and driving parameters of each vehicle in the road section, the road section comprises at least two lanes, and the driving parameters comprise vehicle positioning information; a determining unit, configured to determine, according to vehicle positioning information of each vehicle in the road segment, reference vehicles that are traveling ahead of and closest to the target vehicle in the at least two lanes, respectively; a calculation unit configured to calculate driving risk values of the target vehicle in the at least two lanes, respectively, through a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters; and the recommending unit is used for recommending the driving strategy of the target vehicle according to the magnitude relation between the driving risk values of the target vehicle in the at least two lanes and the preset driving risk threshold values.
In some embodiments of the present application, based on the foregoing solution, the at least two lanes include a target lane and an adjacent lane, and the target vehicle travels in the target lane, wherein the adjacent lane is a lane adjacent to the target lane.
In some embodiments of the present application, based on the foregoing solution, the calculation unit includes: a prediction unit configured to predict a predicted travel parameter of the target vehicle and a predicted travel parameter of the reference vehicle when the target vehicle changes lane to an adjacent lane line, which is a lane dividing line between the target lane and the adjacent lane, based on the travel parameter of the target vehicle and the travel parameter of the reference vehicle; a first input unit, which is used for inputting the predicted running parameters of the target vehicle, the predicted running parameters of the reference vehicle and the environment parameters into a driving risk model to obtain the driving risk value of the target vehicle in the adjacent lane; and the second input unit is used for inputting the running parameters of the target vehicle, the running parameters of the reference vehicle in the target lane and the environment parameters into a driving risk model to obtain a first driving risk value of the target vehicle in the target lane.
In some embodiments of the present application, based on the foregoing scheme, the prediction unit is configured to: determining lane change time required for the target vehicle to change lane from the target lane to the adjacent lane line; and predicting the predicted running parameters of the target vehicle and the predicted running parameters of the reference vehicle when the target vehicle changes the lane to the adjacent lane line based on the running parameters of the target vehicle and the running parameters of the reference vehicle and the lane change time.
In some embodiments of the present application, based on the foregoing solution, the first input unit is configured to: determining a scattered object probability and a scattered object motion parameter, wherein the scattered object probability is the probability of occurrence of truck scattered objects in the target lane, the scattered object motion parameter comprises scattered object quality, and the scattered object quality is the average quality of the scattered objects in historical traffic accidents caused by the truck scattered objects; inputting the running parameters of the target vehicle, the scatterer motion parameters of the reference vehicle in the target lane and the environment parameters into a driving risk model to obtain a driving risk value of the target vehicle from the truck scatterer in the target lane; inputting the running parameters of the target vehicle, the running parameters of the reference vehicle in the target lane and the environmental parameters into a driving risk model to obtain a driving risk value of the target vehicle from the reference vehicle in the target lane; calculating a first driving risk value of the target vehicle in the target lane based on the driving risk value from the truck spill, the spill probability, and the driving risk value from the reference vehicle.
In some embodiments of the application, based on the aforementioned solution, the adjacent lanes include a left adjacent lane and a right adjacent lane, and the driving risk value of the target vehicle in the adjacent lanes includes a second driving risk value of the target vehicle in the left adjacent lane and a third driving risk value of the target vehicle in the right adjacent lane.
In some embodiments of the present application, based on the foregoing solution, the recommendation unit is configured to: when the first driving risk value, the second driving risk value and the third driving risk value are all larger than the driving risk threshold value, recommending the target vehicle to reduce the speed; when the first driving risk value is larger than the driving risk threshold value, and the second or third driving risk value is smaller than the driving risk threshold value, recommending that the target vehicle overtake; and when the first driving risk value is smaller than the driving risk threshold value, recommending that the target vehicle does not overtake.
In some embodiments of the present application, based on the foregoing solution, the recommendation unit is configured to: when the second driving risk value is smaller than the third driving risk value, recommending that the target vehicle overtake the left adjacent lane; and when the third driving risk value is smaller than the second driving risk value, recommending that the target vehicle overtake the right adjacent lane.
In some embodiments of the present application, based on the foregoing, the driving risk threshold includes at least two sub-driving risk thresholds, and the recommending unit is configured to: and recommending the driving strategy of the target vehicle according to the driving risk values of the target vehicle in the at least two lanes and the magnitude relation between the at least two sub-driving risk thresholds.
In some embodiments of the present application, based on the foregoing, the environmental parameter of the road segment includes one or more of road viscosity, road camber, road humidity, road grade, road visibility, and road friction coefficient.
In some embodiments of the present application, based on the foregoing scheme, the driving parameters of each vehicle in the road segment further include vehicle mass, vehicle vector speed, and vehicle acceleration.
In some embodiments of the present application, based on the foregoing scheme, the vehicle positioning information includes any one of vehicle GPS positioning information, vehicle beidou satellite positioning information, and vehicle two-dimensional coordinate positioning information.
According to an aspect of embodiments of the present application, there is provided a computer readable medium having stored thereon a computer program which, when executed by a processor, implements a method of recommending a vehicle driving strategy as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method of recommending a vehicle driving strategy as described in the above embodiments.
In the technical solutions provided in some embodiments of the present application, first, an environmental parameter of a road segment where a target vehicle is located and a driving parameter of each vehicle in the road segment are obtained, then, according to vehicle positioning information in the driving parameter, a reference vehicle which is driven in front of the target vehicle and is closest to the target vehicle is determined in the at least two lanes, driving risk values of the target vehicle in the at least two lanes are calculated through a driving risk model, and finally, a driving strategy of the target vehicle is recommended according to a magnitude relation between the driving risk values of the target vehicle in the at least two lanes and a predetermined driving risk threshold. Since the driving risk value of the target vehicle in the lane can embody the driving risk of the target vehicle in the lane, a scientific and reasonable vehicle driving strategy can be recommended by referring to the magnitude relation between the driving risk value of the target vehicle in the lane and the preset driving risk threshold value, so that the technical scheme provided by some embodiments of the application can improve the safety of vehicle driving.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
FIG. 1 shows a schematic diagram of an exemplary system architecture to which aspects of embodiments of the present application may be applied;
FIG. 2 illustrates an application scenario diagram of a recommendation method for implementing a vehicle driving strategy according to an embodiment of the present application;
FIG. 3 shows a flow chart of a method of recommendation of vehicle driving strategies according to one embodiment of the present application;
FIG. 4 shows a detailed flowchart for calculating driving risk values of the target vehicle in the at least two lanes, respectively, according to an embodiment of the present application;
FIG. 5 illustrates a detailed flow diagram for predicting predicted travel parameters of the target vehicle and the reference vehicle according to one embodiment of the present application;
FIG. 6 illustrates a schematic view of a scenario in which the target vehicle changes lane to the adjacent lane line according to one embodiment of the present application;
FIG. 7 illustrates a detailed flow chart for deriving a first driving risk value for the target vehicle in the target lane when the reference vehicle in the target lane is a freight vehicle, according to an embodiment of the present application;
FIG. 8 illustrates a detailed flow diagram for recommending a driving strategy for the target vehicle according to one embodiment of the present application;
FIG. 9 shows a detailed flow diagram of recommending a driving strategy for the target vehicle according to one embodiment of the present application;
FIG. 10 illustrates a schematic diagram of cloud-based recommendation of vehicle driving strategies according to an embodiment of the present application;
FIG. 11 shows a block diagram of a recommendation device for vehicle driving strategies according to an embodiment of the present application;
FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
Fig. 1 shows a schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied.
As shown in fig. 1, the system architecture may include a terminal device (such as one or more of the smart phone 101, the tablet computer 102, and the portable computer 103 shown in fig. 1, but may also be, but is not limited to, a desktop computer, a smart speaker, a smart watch, etc.), a network 104, and a server 105. The network 104 serves as a medium for providing communication links between terminal devices and the server 105. Network 104 may include various types of connections, such as wired communication links, wireless communication links, and so forth, which are not limiting in this application. It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, server 105 may be a server cluster comprised of multiple servers, or the like.
In one embodiment of the present application, the terminal devices shown in fig. 1 may be all target vehicles described in the present application, wherein the terminal devices may send a request for recommending a target vehicle driving strategy to the server, the server 105, after receiving the request, obtains an environmental parameter of a road segment where the target vehicle is located and a driving parameter of each vehicle in the road segment, then determines a reference vehicle which is driven in front of and closest to the target vehicle in the at least two lanes according to vehicle positioning information in the driving parameters, respectively, and calculates driving risk values of the target vehicle in the at least two lanes through a driving risk model, and finally calculates a magnitude relationship between the driving risk values of the target vehicle in the at least two lanes and a predetermined driving risk threshold according to a magnitude relationship between the driving risk values of the target vehicle in the at least two lanes, and determining the driving strategy of the target vehicle, and recommending the driving strategy to the target vehicle.
It should be noted that the recommendation method for the vehicle driving strategy provided in the embodiment of the present application is generally executed by the server 105, and accordingly, the recommendation device for the vehicle driving strategy is generally disposed in the server 105. However, in other embodiments of the present application, the terminal device may also have a similar function as the server, so as to execute the recommendation scheme of the vehicle driving strategy provided by the embodiments of the present application.
The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services.
Specifically, cloud computing (cloud computing) as described above is a computing model that distributes computing tasks over a resource pool formed by a large number of computers, enabling various application systems to acquire computing power, storage space, and information services as needed. The network that provides the resources is referred to as the "cloud". Resources in the cloud can be infinitely expanded to users, and can be acquired at any time, used as required and expanded at any time. The cloud computing resource pool mainly comprises computing equipment (which is a virtualization machine and comprises an operating system), storage equipment and network equipment.
In one embodiment of the present application, an application scenario in which the recommendation method of the vehicle driving strategy is implemented may be an application scenario diagram as shown in fig. 2.
Referring to fig. 2, an application scenario diagram of a recommendation method for implementing a vehicle driving strategy according to an embodiment of the present application is shown.
Specifically, in the road segment 200 shown in fig. 2, 4 vehicles A, B, C, D are included, wherein a vehicle is a target vehicle, B, C and D vehicles are reference vehicles, during the driving process, the driving strategy of the target vehicle a is influenced by the reference vehicle A, B, C, during the implementation of the vehicle driving strategy recommendation method, the environmental parameters of the road segment 200 and the driving parameters of the vehicles A, B, C, D can be obtained, as can be seen from the figure, the vehicles B, C, D are respectively reference vehicles which are driven in front of the vehicle a and are closest to the vehicle a, further, the driving risk values of the vehicle a in the three lanes shown in the figure can be calculated through the environmental parameters of the road segment 200 and the driving parameters of the vehicles A, B, C, D, and finally, the driving risk values of the vehicle a in the three lanes shown in the figure are calculated according to the driving risk values of the vehicle a, And a predetermined driving risk threshold, determining the driving strategy of the vehicle a.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
referring to FIG. 3, a flow chart of a method of recommendation of a vehicle driving strategy according to one embodiment of the present application is shown. The recommendation method of the vehicle driving strategy may be executed by a device having a computing processing function, such as the server 105 shown in fig. 1, the terminal device shown in fig. 1, or a cloud server having a cloud computing function. As shown in fig. 3, the recommendation method for vehicle driving strategy at least includes steps 310 to 370:
in step 310, an environmental parameter of a road section where a target vehicle is located and a driving parameter of each vehicle in the road section are obtained, wherein the road section comprises at least two lanes, and the driving parameter comprises vehicle positioning information.
In one embodiment of the present application, the at least two lanes may include a target lane and an adjacent lane, the target vehicle traveling in the target lane, wherein the adjacent lane is a lane adjacent to the target lane.
In this application, the positioning information may include position coordinates of each vehicle in the road segment, where the position coordinates may be two-dimensional coordinates, longitude and latitude coordinates, or three-dimensional coordinates, for example, any one of vehicle GPS positioning information, vehicle beidou satellite positioning information, and vehicle two-dimensional coordinate positioning information.
In the present application, the environmental parameter of the road segment may include one or more of road viscosity, road camber, road humidity, road grade, road visibility, and road coefficient of friction.
In the present application, the driving parameters of each vehicle in the road section may further include vehicle mass, vehicle vector speed, and vehicle acceleration.
With continued reference to fig. 3, in step 330, a reference vehicle traveling ahead of and closest to the target vehicle is determined in the at least two lanes according to the vehicle positioning information of each vehicle in the road segment, respectively.
Specifically, in the present application, according to the vehicle positioning information of each vehicle in the road section, the distance between each vehicle and the direction of each vehicle relative to the target vehicle can be determined, and then the reference vehicle which runs in front of the target vehicle and is closest to the target vehicle can be determined in the at least two lanes.
With continued reference to fig. 3, in step 350, driving risk values of the target vehicle in the at least two lanes, respectively, are calculated by a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters.
In one embodiment of the present application, calculating driving risk values of the target vehicle in the at least two lanes respectively through a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters may be performed according to the steps shown in fig. 4.
Referring to fig. 4, a detailed flowchart of calculating driving risk values of the target vehicle in the at least two lanes, respectively, according to an embodiment of the present application is shown. Specifically, the method comprises steps 351 to 353:
in step 351, the predicted travel parameter of the target vehicle and the predicted travel parameter of the reference vehicle are predicted when the target vehicle changes lane to an adjacent lane line, which is a lane dividing line between the target lane and the adjacent lane, based on the travel parameter of the target vehicle and the travel parameter of the reference vehicle.
In a specific implementation of one embodiment, when the target vehicle is predicted to change to the adjacent lane line based on the traveling parameters of the target vehicle and the reference vehicle, the predicted traveling parameters of the target vehicle and the predicted traveling parameters of the reference vehicle may be performed according to the steps shown in fig. 5.
Referring to fig. 5, a detailed flow diagram of predicting predicted travel parameters of the target vehicle and the reference vehicle according to one embodiment of the present application is shown. Specifically, the method comprises steps 3511-3512:
step 3511, determining lane change time required for the target vehicle to change lane from the target lane to the adjacent lane line.
Step 3512, predicting the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lane to the adjacent lane line based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle and the lane change time.
In order to make the prediction process of the predicted running parameters of the target vehicle and the reference vehicle better understood by those skilled in the art, the following description is given with reference to fig. 6:
referring to fig. 6, a schematic view of a scenario in which the target vehicle changes lane to the adjacent lane line according to an embodiment of the present application is shown. In the figure, a1 or a2 is the position when vehicle a changes lane to an adjacent lane line, B1 is the position when vehicle B travels to position a1, and C1 is the position when vehicle C travels to position a2, respectively.
In the concrete calculation, the time taken for the vehicle a to travel to the position a1 or a2 may be determined based on the travel parameters of the vehicle a, so that the travel positions of the vehicle B and the vehicle D (B1 and D1) when the vehicle a travels to the position a1 or a2 may be predicted based on the travel parameters of the vehicle B and the vehicle D, and the predicted travel parameters of the vehicle a, the vehicle B, and the vehicle D (the predicted travel positions of the vehicle a, the vehicle B, and the vehicle D are actually predicted) may be obtained.
With continued reference to fig. 4, in step 352, the predicted travel parameters of the target vehicle, the predicted travel parameters of the reference vehicle, and the environmental parameters are input into a driving risk model, so as to obtain a driving risk value of the target vehicle in the adjacent lane.
In step 353, the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane, and the environmental parameters are input into a driving risk model, so as to obtain a first driving risk value of the target vehicle in the target lane.
In a specific implementation of an embodiment, when the reference vehicle in the target lane is a freight vehicle, the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane, and the environmental parameters are input into a driving risk model to obtain a first driving risk value of the target vehicle in the target lane, which may be performed according to the steps shown in fig. 7.
Referring to fig. 7, a detailed flowchart of obtaining a first driving risk value of the target vehicle in the target lane when the reference vehicle in the target lane is a freight vehicle according to an embodiment of the present application is shown. Specifically, the method comprises steps 3531-3534:
step 3531, determining a scattered object probability and a scattered object motion parameter, wherein the scattered object probability is the probability of occurrence of truck scattered objects in the target lane, the scattered object motion parameter comprises scattered object quality, and the scattered object quality is the average quality of scattered objects in historical traffic accidents caused by the truck scattered objects.
It should be noted that, because the scatterers are stationary relative to the truck before scattering, some of the motion parameters of the scatterers are the same as some of the driving parameters of the reference vehicle, such as vector velocity, acceleration, etc.
Step 3532, inputting the driving parameters of the target vehicle, the scatters motion parameters of the reference vehicle in the target lane and the environment parameters into a driving risk model to obtain a driving risk value of the target vehicle from the truck scatters in the target lane.
Step 3533, inputting the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane and the environmental parameters into a driving risk model to obtain a driving risk value of the target vehicle from the reference vehicle in the target lane.
Step 3534, calculating a first driving risk of the target vehicle in the target lane based on the driving risk value from the truck spill, the spill probability, and the driving risk value from the reference vehicle.
Specifically, in step 3534, the first driving risk of the target vehicle in the target lane may be calculated by the following formula:
E1＝Et+P×Eo
wherein E is1A first driving risk value of the target vehicle in the target lane; etRepresenting a driving risk value from the reference vehicle; eoRepresenting a driving risk value from the truck spill; p represents the scatter probability.
With continued reference to fig. 3, in step 370, a driving strategy of the target vehicle is recommended according to a magnitude relationship between the driving risk values of the target vehicle in the at least two lanes, respectively, and a predetermined driving risk threshold.
In one embodiment of the present application, the adjacent lanes may include a left adjacent lane and a right adjacent lane, the driving risk value of the target vehicle in the adjacent lanes including a second driving risk value of the target vehicle in the left adjacent lane and a third driving risk value of the target vehicle in the right adjacent lane.
In a specific implementation of an embodiment, recommending a driving strategy of the target vehicle according to a magnitude relationship between the driving risk values of the target vehicle in the at least two lanes and the predetermined driving risk threshold value may be performed according to the steps shown in fig. 8.
Referring to FIG. 8, a detailed flow diagram of recommending a driving strategy for the target vehicle is shown, according to one embodiment of the present application. Specifically, the method comprises steps 371 to 373:
step 371, recommending the target vehicle to reduce the vehicle speed when the first, second, and third driving risk values are all greater than the driving risk threshold.
And 372, recommending the target vehicle to overtake when the first driving risk value is larger than the driving risk threshold and the second or third driving risk value is smaller than the driving risk threshold.
Further, when the first driving risk value is greater than the driving risk threshold and the second or third driving risk value is less than the driving risk threshold, the target vehicle is recommended to overtake, which may be performed according to the steps shown in fig. 9.
Referring to FIG. 9, a detailed flow diagram of recommending a driving strategy for the target vehicle is shown, according to one embodiment of the present application. Specifically, the method comprises steps 3721 to 3722:
step 3721, when the second driving risk value is smaller than the third driving risk value, recommending that the target vehicle overtake the left adjacent lane.
Step 3722, when the third driving risk value is smaller than the second driving risk value, recommending the target vehicle to overtake from the right adjacent lane.
Step 373, recommending that the target vehicle not overtake when the first driving risk value is less than the driving risk threshold.
In a particular implementation of an embodiment, the driving risk threshold may further include at least two sub-driving risk thresholds.
Specifically, the recommending the driving strategy of the target vehicle according to the magnitude relationship between the driving risk values of the target vehicle in the at least two lanes and the predetermined driving risk threshold may be recommending the driving strategy of the target vehicle according to the magnitude relationship between the driving risk values of the target vehicle in the at least two lanes and the at least two sub-driving risk thresholds.
Specifically, for example, the at least two sub-driving risk thresholds include a first sub-driving risk threshold and a second sub-driving risk threshold, where the first sub-driving risk threshold is greater than the second sub-driving risk threshold.
And when the first driving risk value, the second driving risk value and the third driving risk value are all larger than the first sub-driving risk threshold value, recommending the target vehicle to reduce the vehicle speed.
And when the first driving risk value, the second driving risk value and the third driving risk value are all larger than the second sub-driving risk threshold and are all smaller than the first sub-driving risk threshold, recommending that the target vehicle overtake.
And when the first driving risk value, the second driving risk value and the third driving risk value are all smaller than the second sub-driving risk threshold value, recommending that the target vehicle does not overtake.
It should be understood to those skilled in the art that the driving strategy for recommending the target vehicle may also have other specific implementations according to the magnitude relationship between the driving risk values of the target vehicle in the at least two lanes and the predetermined driving risk threshold, and is not limited to the two types listed above.
In order to make the present application more understandable to those skilled in the art, a model in the prior art will be briefly described below.
The calculation formula of the driving risk value between two moving vehicles (moving objects) is as follows:
Figure BDA0002494485270000131
wherein SPEV_abRepresents a driving risk value between the vehicle (object) a and the vehicle (object) b; g is a constant (similar to the gravitational constant); raThe road condition parameters representing the road surface on which the vehicle (object) a is located are used for comprehensively measuring the viscosity, humidity, gradient and temperature of the road surface, generally RbEqual; rbRepresenting road condition parameters of the road surface on which the vehicle (object) b is located, the road condition parameters being used for comprehensively measuring the viscosity, humidity, gradient and temperature of the road surface, generally RaEqual; maRepresenting the mass of the vehicle a; mbRepresents the mass of the vehicle b; k is a radical of3Is a constant (equal to the speed of light); k is a radical of1Is constant (typically 3 in air);
Figure BDA0002494485270000132
represents the linear distance of the vehicle a from the vehicle b;
Figure BDA0002494485270000133
representing the relative speed of vehicle a and vehicle b; thetaaWhich represents the angle between the direction of travel of vehicle a and the direction of travel of vehicle j.
In the embodiment of recommending the vehicle driving strategy in a vehicle driving scene of a plurality of vehicle road sections, a fused vehicle cloud, a regional cloud and a marginal cloud can be further built to realize that the driving strategy is recommended for each vehicle in the internet of vehicles through a cloud vehicle system, as shown in fig. 10, a schematic diagram of recommending the vehicle driving strategy based on the cloud according to one embodiment of the application is shown. The system consists of a cloud and a vehicle network. Wherein, all calculation functions of this scheme can be realized on the car cloud, and the vehicle can obtain the parameter of going of vehicle self in real time to upload to the car high in the clouds in real time.
Specifically, the automobile cloud firstly obtains an environmental parameter of a road section where a target vehicle is located and a driving parameter of each vehicle in the road section, then determines a reference vehicle which is driven in front of the target vehicle and is closest to the target vehicle in the at least two lanes according to vehicle positioning information in the driving parameter, calculates driving risk values of the target vehicle in the at least two lanes through a driving risk model, and finally recommends a driving strategy of the target vehicle according to a magnitude relation between the driving risk values of the target vehicle in the at least two lanes and a preset driving risk threshold.
In the technical solutions provided in some embodiments of the present application, first, an environmental parameter of a road segment where a target vehicle is located and a driving parameter of each vehicle in the road segment are obtained, then, according to vehicle positioning information in the driving parameter, a reference vehicle which is driven in front of the target vehicle and is closest to the target vehicle is determined in the at least two lanes, driving risk values of the target vehicle in the at least two lanes are calculated through a driving risk model, and finally, a driving strategy of the target vehicle is recommended according to a magnitude relation between the driving risk values of the target vehicle in the at least two lanes and a predetermined driving risk threshold. Since the driving risk value of the target vehicle in the lane can embody the driving risk of the target vehicle in the lane, a scientific and reasonable vehicle driving strategy can be recommended by referring to the magnitude relation between the driving risk value of the target vehicle in the lane and the preset driving risk threshold value, so that the technical scheme provided by some embodiments of the application can improve the safety of vehicle driving.
The following describes embodiments of the apparatus of the present application, which may be used to implement the recommendation method of the vehicle driving strategy in the above-described embodiments of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for recommending a driving strategy of a vehicle described above in the present application.
FIG. 11 shows a block diagram of a recommendation device for vehicle driving strategies according to one embodiment of the present application.
Referring to fig. 11, a recommendation apparatus 1100 for a vehicle driving strategy according to an embodiment of the present application includes: an acquisition unit 1101, a determination unit 1102, a calculation unit 1103, and a recommendation unit 1104.
The acquiring unit 1101 is used for acquiring an environmental parameter of a road section where a target vehicle is located, and a driving parameter of each vehicle in the road section, wherein the road section comprises at least two lanes, and the driving parameter comprises vehicle positioning information; a determining unit 1102, configured to determine, according to vehicle positioning information of each vehicle in the road segment, reference vehicles that are traveling ahead of and closest to the target vehicle in the at least two lanes, respectively; a calculating unit 1103 configured to calculate driving risk values of the target vehicle in the at least two lanes, respectively, through a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters; a recommending unit 1104, configured to recommend a driving strategy of the target vehicle according to magnitude relationships between driving risk values of the target vehicle in the at least two lanes respectively and predetermined driving risk thresholds.
In some embodiments of the present application, based on the foregoing solution, the at least two lanes include a target lane and an adjacent lane, and the target vehicle travels in the target lane, wherein the adjacent lane is a lane adjacent to the target lane.
In some embodiments of the present application, based on the foregoing solution, the calculating unit 1103 includes: a prediction unit configured to predict a predicted travel parameter of the target vehicle and a predicted travel parameter of the reference vehicle when the target vehicle changes lane to an adjacent lane line, which is a lane dividing line between the target lane and the adjacent lane, based on the travel parameter of the target vehicle and the travel parameter of the reference vehicle; a first input unit, which is used for inputting the predicted running parameters of the target vehicle, the predicted running parameters of the reference vehicle and the environment parameters into a driving risk model to obtain the driving risk value of the target vehicle in the adjacent lane; and the second input unit is used for inputting the running parameters of the target vehicle, the running parameters of the reference vehicle in the target lane and the environment parameters into a driving risk model to obtain a first driving risk value of the target vehicle in the target lane.
In some embodiments of the present application, based on the foregoing scheme, the prediction unit is configured to: determining lane change time required for the target vehicle to change lane from the target lane to the adjacent lane line; and predicting the predicted running parameters of the target vehicle and the predicted running parameters of the reference vehicle when the target vehicle changes the lane to the adjacent lane line based on the running parameters of the target vehicle and the running parameters of the reference vehicle and the lane change time.
In some embodiments of the present application, based on the foregoing solution, the first input unit is configured to: determining a scattered object probability and a scattered object motion parameter, wherein the scattered object probability is the probability of occurrence of truck scattered objects in the target lane, the scattered object motion parameter comprises scattered object quality, and the scattered object quality is the average quality of the scattered objects in historical traffic accidents caused by the truck scattered objects; inputting the running parameters of the target vehicle, the scatterer motion parameters of the reference vehicle in the target lane and the environment parameters into a driving risk model to obtain a driving risk value of the target vehicle from the truck scatterer in the target lane; inputting the running parameters of the target vehicle, the running parameters of the reference vehicle in the target lane and the environmental parameters into a driving risk model to obtain a driving risk value of the target vehicle from the reference vehicle in the target lane; calculating a first driving risk of the target vehicle in the target lane based on the driving risk value from the truck spill, the spill probability, and the driving risk value from the reference vehicle.
In some embodiments of the application, based on the aforementioned solution, the adjacent lanes include a left adjacent lane and a right adjacent lane, and the driving risk value of the target vehicle in the adjacent lanes includes a second driving risk value of the target vehicle in the left adjacent lane and a third driving risk value of the target vehicle in the right adjacent lane.
In some embodiments of the present application, based on the foregoing solution, the recommending unit 1104 is configured to: when the first driving risk value, the second driving risk value and the third driving risk value are all larger than the driving risk threshold value, recommending the target vehicle to reduce the speed; when the first driving risk value is larger than the driving risk threshold value, and the second or third driving risk value is smaller than the driving risk threshold value, recommending that the target vehicle overtake; and when the first driving risk value is smaller than the driving risk threshold value, recommending that the target vehicle does not overtake.
In some embodiments of the present application, based on the foregoing solution, the recommending unit 1104 is configured to: when the second driving risk value is smaller than the third driving risk value, recommending that the target vehicle overtake the left adjacent lane; and when the third driving risk value is smaller than the second driving risk value, recommending that the target vehicle overtake the right adjacent lane.
In some embodiments of the present application, based on the above scheme, the driving risk threshold includes at least two sub-driving risk thresholds, and the recommendation unit 1104 is configured to: and recommending the driving strategy of the target vehicle according to the driving risk values of the target vehicle in the at least two lanes and the magnitude relation between the at least two sub-driving risk thresholds.
In some embodiments of the present application, based on the foregoing, the environmental parameter of the road segment includes one or more of road viscosity, road camber, road humidity, road grade, road visibility, and road friction coefficient.
In some embodiments of the present application, based on the foregoing scheme, the driving parameters of each vehicle in the road segment further include vehicle mass, vehicle vector speed, and vehicle acceleration.
In some embodiments of the present application, based on the foregoing scheme, the vehicle positioning information includes any one of vehicle GPS positioning information, vehicle beidou satellite positioning information, and vehicle two-dimensional coordinate positioning information.
FIG. 12 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
It should be noted that the computer system 1200 of the electronic device shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 12, the computer system 1200 includes a Central Processing Unit (CPU)1201, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. In the RAM 1203, various programs and data necessary for system operation are also stored. The CPU 1201, ROM 1202, and RAM 1203 are connected to each other by a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output section 1207 including a Display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a network interface card such as a LAN (Local area network) card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1209, and/or installed from the removable medium 1211. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 1201.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (15)

1. A method of recommending a vehicle driving strategy, the method comprising:
the method comprises the steps of obtaining environmental parameters of a road section where a target vehicle is located and driving parameters of each vehicle in the road section, wherein the road section comprises at least two lanes, and the driving parameters comprise vehicle positioning information;
according to the vehicle positioning information of each vehicle in the road section, determining a reference vehicle which runs in front of the target vehicle and is closest to the target vehicle in the at least two lanes respectively;
calculating driving risk values of the target vehicle in the at least two lanes respectively through a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle and the environmental parameters;
and recommending the driving strategy of the target vehicle according to the magnitude relation between the driving risk values of the target vehicle in the at least two lanes and the preset driving risk threshold value.
2. The method of claim 1, wherein the at least two lanes include a target lane and an adjacent lane, the target vehicle traveling in the target lane, wherein the adjacent lane is a lane adjacent to the target lane.
3. The method according to claim 2, wherein the calculating driving risk values of the target vehicle in the at least two lanes, respectively, by a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters comprises:
predicting a predicted travel parameter of the target vehicle and a predicted travel parameter of the reference vehicle when the target vehicle changes lanes to an adjacent lane line, which is a lane dividing line between the target lane and the adjacent lane, based on the travel parameter of the target vehicle and the travel parameter of the reference vehicle;
inputting the predicted running parameters of the target vehicle, the predicted running parameters of the reference vehicle and the environmental parameters into a driving risk model to obtain a driving risk value of the target vehicle in the adjacent lane;
and inputting the running parameters of the target vehicle, the running parameters of the reference vehicle in the target lane and the environmental parameters into a driving risk model to obtain a first driving risk value of the target vehicle in the target lane.
4. The method according to claim 3, wherein the predicting the predicted travel parameter of the target vehicle and the predicted travel parameter of the reference vehicle when the target vehicle is predicted to change lane to the adjacent lane line based on the travel parameter of the target vehicle and the travel parameter of the reference vehicle comprises:
determining lane change time required for the target vehicle to change lane from the target lane to the adjacent lane line;
and predicting the predicted running parameters of the target vehicle and the predicted running parameters of the reference vehicle when the target vehicle changes the lane to the adjacent lane line based on the running parameters of the target vehicle and the running parameters of the reference vehicle and the lane change time.
5. The method of claim 3, wherein when the reference vehicle in the target lane is a freight vehicle, inputting the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane, and the environmental parameters into a driving risk model to obtain a first driving risk value of the target vehicle in the target lane, comprises:
determining a scattered object probability and a scattered object motion parameter, wherein the scattered object probability is the probability of occurrence of truck scattered objects in the target lane, the scattered object motion parameter comprises scattered object quality, and the scattered object quality is the average quality of the scattered objects in historical traffic accidents caused by the truck scattered objects;
inputting the running parameters of the target vehicle, the scatterer motion parameters of the reference vehicle in the target lane and the environment parameters into a driving risk model to obtain a driving risk value of the target vehicle from the truck scatterer in the target lane;
inputting the running parameters of the target vehicle, the running parameters of the reference vehicle in the target lane and the environmental parameters into a driving risk model to obtain a driving risk value of the target vehicle from the reference vehicle in the target lane;
calculating a first driving risk value of the target vehicle in the target lane based on the driving risk value from the truck spill, the spill probability, and the driving risk value from the reference vehicle.
6. The method of claim 3, wherein the adjacent lanes include a left adjacent lane and a right adjacent lane, the driving risk value of the target vehicle in the adjacent lanes including a second driving risk value of the target vehicle in the left adjacent lane and a third driving risk value of the target vehicle in the right adjacent lane.
7. The method of claim 6, wherein recommending the driving strategy of the target vehicle according to the magnitude relationship between the driving risk values of the target vehicle in the at least two lanes and the predetermined driving risk threshold comprises:
when the first driving risk value, the second driving risk value and the third driving risk value are all larger than the driving risk threshold value, recommending the target vehicle to reduce the speed;
when the first driving risk value is larger than the driving risk threshold value, and the second or third driving risk value is smaller than the driving risk threshold value, recommending that the target vehicle overtake;
and when the first driving risk value is smaller than the driving risk threshold value, recommending that the target vehicle does not overtake.
8. The method of claim 7, wherein recommending the target vehicle to cut-in when the first driving risk value is greater than the driving risk threshold and the second or third driving risk value is less than the driving risk threshold comprises:
when the second driving risk value is smaller than the third driving risk value, recommending that the target vehicle overtake the left adjacent lane;
and when the third driving risk value is smaller than the second driving risk value, recommending that the target vehicle overtake the right adjacent lane.
9. The method of claim 6, wherein the driving risk threshold comprises at least two sub-driving risk thresholds, and wherein recommending a driving strategy for the target vehicle based on a magnitude relationship between driving risk values of the target vehicle in the at least two lanes, respectively, and predetermined driving risk thresholds comprises:
and recommending the driving strategy of the target vehicle according to the driving risk values of the target vehicle in the at least two lanes and the magnitude relation between the at least two sub-driving risk thresholds.
10. The method of any one of claims 1 to 9, wherein the environmental parameters of the road segment include one or more of road viscosity, road camber, road humidity, road grade, road visibility, and road coefficient of friction.
11. The method according to any one of claims 1 to 9, wherein the driving parameters of each vehicle in the road segment further comprise vehicle mass, vehicle vector speed, and vehicle acceleration.
12. The method according to any one of claims 1 to 9, wherein the vehicle positioning information comprises any one of vehicle GPS positioning information, vehicle beidou satellite positioning information, vehicle two-dimensional coordinate positioning information.
13. An apparatus for recommending a driving strategy for a vehicle, said apparatus comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring environmental parameters of a road section where a target vehicle is located and driving parameters of each vehicle in the road section, the road section comprises at least two lanes, and the driving parameters comprise vehicle positioning information;
a determining unit, configured to determine, according to vehicle positioning information of each vehicle in the road segment, reference vehicles that are traveling ahead of and closest to the target vehicle in the at least two lanes, respectively;
a calculation unit configured to calculate driving risk values of the target vehicle in the at least two lanes, respectively, through a driving risk model based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters;
and the recommending unit is used for recommending the driving strategy of the target vehicle according to the magnitude relation between the driving risk values of the target vehicle in the at least two lanes and the preset driving risk threshold values.
14. A computer-readable storage medium, on which a computer program is stored, the computer program comprising executable instructions that, when executed by a processor, carry out the method of any one of claims 1 to 12.
15. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is arranged to execute the executable instructions to implement the method of any one of claims 1 to 12.
CN202010414595.0A 2020-05-15 2020-05-15 Recommendation method, device, medium and electronic equipment for vehicle driving strategy Pending CN111605555A (en)

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