Vehicle driving strategy recommendation method, device, medium and electronic equipment

Abstract

Embodiments of the present application provide a vehicle driving strategy recommendation method and device, a computer readable medium and an electronic device. The method comprises: obtaining an environmental parameter of a road section where a target vehicle is located and driving parameters of each vehicle in the road section, the road section comprising at least two lanes, and the driving parameters comprising vehicle positioning information; determining, according to the vehicle positioning information of each vehicle in the road section, a reference vehicle driving in front of and closest to the target vehicle in each of the at least two lanes; calculating, based on the driving parameters of the target vehicle and the reference vehicle and the environmental parameter, a driving risk value of the target vehicle in each of the at least two lanes through a driving risk model; and recommending a driving strategy of the target vehicle according to a size relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold. The technical solution of the embodiments of the present application can improve the safety of vehicle driving.

Description

Technical Field

[0001] This application relates to the field of computer and safety-assisted driving technology, and more specifically, to a method, apparatus, computer-readable medium, and electronic device for recommending vehicle driving strategies. Background Technology

[0002] In typical traffic scenarios, such as driving on multi-vehicle roads, drivers usually refer to the driving conditions of surrounding vehicles and rely on their own driving experience to judge and choose driving strategies such as whether to overtake or slow down. However, how to improve the safety of vehicle driving is a technical problem that urgently needs to be solved. Summary of the Invention

[0003] Embodiments of this application provide a method, apparatus, computer-readable medium, and electronic device for recommending vehicle driving strategies, which can at least to some extent improve the safety of vehicle driving.

[0004] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0005] According to one aspect of the embodiments of this application, a method for recommending vehicle driving strategies is provided, comprising: acquiring environmental parameters of a road segment where a target vehicle is located, and driving parameters of each vehicle in the road segment, wherein the road segment includes at least two lanes, and the driving parameters include vehicle positioning information; determining, based on the vehicle positioning information of each vehicle in the road segment, a reference vehicle that is driving in front of the target vehicle and closest to the target vehicle in each of the at least two lanes; calculating, based on the driving parameters of the target vehicle, the driving parameters of the reference vehicles, and the environmental parameters, a driving risk value of the target vehicle in each of the at least two lanes using a driving risk model; and recommending a driving strategy for the target vehicle based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold.

[0006] According to one aspect of the embodiments of this application, a vehicle driving strategy recommendation device is provided, comprising: an acquisition unit, configured to acquire environmental parameters of a road segment where a target vehicle is located, and driving parameters of each vehicle in the road segment, the road segment including at least two lanes, the driving parameters including vehicle positioning information; a determination unit, configured to determine, based on the vehicle positioning information of each vehicle in the road segment, a reference vehicle traveling in front of the target vehicle and closest to the target vehicle in each of the at least two lanes; a calculation unit, configured to calculate, based on the driving parameters of the target vehicle, the driving parameters of the reference vehicles, and the environmental parameters, a driving risk value of the target vehicle in each of the at least two lanes using a driving risk model; and a recommendation unit, configured to recommend a driving strategy for the target vehicle based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold.

[0007] In some embodiments of this application, based on the foregoing scheme, the at least two lanes include a target lane and an adjacent lane, the target vehicle travels in the target lane, and the adjacent lane is the lane adjacent to the target lane.

[0008] In some embodiments of this application, based on the foregoing scheme, the calculation unit includes: a prediction unit, used to predict the predicted driving parameters of the target vehicle and the reference vehicle when the target vehicle changes lanes to an adjacent lane line, based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, wherein the adjacent lane line is the lane dividing line between the target lane and the adjacent lane; a first input unit, used to input the predicted driving parameters of the target vehicle, the predicted driving 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 a second input unit, used to input 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.

[0009] In some embodiments of this application, based on the foregoing scheme, the prediction unit is configured to: determine the lane change time required for the target vehicle to change lanes from the target lane to the adjacent lane; and, based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, and the lane change time, predict the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lanes to the adjacent lane.

[0010] In some embodiments of this application, based on the foregoing scheme, the first input unit is configured to: determine the probability of debris and debris movement parameters, wherein the probability of debris is the probability of debris from a truck appearing in the target lane, and the debris movement parameters include the mass of debris, which is the average mass of debris in historical traffic accidents caused by truck debris; input the driving parameters of the target vehicle, the debris movement parameters of a reference vehicle in the target lane, and the environmental parameters into a driving risk model to obtain the driving risk value of the target vehicle from the truck debris in the target lane; input the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane, and the environmental parameters into the driving risk model to obtain the driving risk value of the target vehicle from the reference vehicle in the target lane; and calculate the first driving risk value of the target vehicle in the target lane based on the driving risk value from the truck debris, the probability of debris, and the driving risk value from the reference vehicle.

[0011] In some embodiments of this application, based on the foregoing scheme, 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.

[0012] In some embodiments of this application, based on the foregoing scheme, the recommendation unit is configured as follows: when the first, second, and third driving risk values ​​are all greater than the driving risk threshold, the target vehicle is recommended to reduce its speed; 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; when the first driving risk value is less than the driving risk threshold, the target vehicle is recommended not to overtake.

[0013] In some embodiments of this application, based on the foregoing scheme, the recommendation unit is configured to: recommend the target vehicle to overtake from the left adjacent lane when the second driving risk value is less than the third driving risk value; and recommend the target vehicle to overtake from the right adjacent lane when the third driving risk value is less than the second driving risk value.

[0014] In some embodiments of this application, based on the foregoing scheme, the driving risk threshold includes at least two sub-driving risk thresholds, and the recommendation unit is configured to recommend a driving strategy for the target vehicle based on the driving risk values ​​of the target vehicle in the at least two lanes and the magnitude relationship between the at least two sub-driving risk thresholds.

[0015] In some embodiments of this application, based on the foregoing scheme, the environmental parameters of the road segment include one or more of the following: road viscosity, road curvature, road humidity, road slope, road visibility, and road friction coefficient.

[0016] In some embodiments of this application, based on the aforementioned scheme, the driving parameters of each vehicle in the road segment also include vehicle mass, vehicle vector velocity, and vehicle acceleration.

[0017] In some embodiments of this 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.

[0018] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for recommending vehicle driving strategies as described in the above embodiments.

[0019] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and 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 for recommending vehicle driving strategies as described in the above embodiments.

[0020] In some embodiments of this application, the technical solutions first obtain environmental parameters of the road segment where the target vehicle is located, as well as the driving parameters of each vehicle in the road segment. Then, based on the vehicle positioning information in the driving parameters, reference vehicles that are driving in front of and closest to the target vehicle in at least two lanes are determined. A driving risk model is then used to calculate the driving risk value of the target vehicle in each of the at least two lanes. Finally, based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold, a driving strategy for the target vehicle is recommended. Since the driving risk value of the target vehicle in a lane reflects its driving risk in that lane, a scientifically reasonable driving strategy can be recommended by referring to the relationship between the driving risk value of the target vehicle in a lane and the predetermined driving risk threshold. Therefore, the technical solutions provided in some embodiments of this application can improve vehicle driving safety.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0023] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solutions of the embodiments of this application can be applied is shown;

[0024] Figure 2 An application scenario diagram of a recommended method for implementing a vehicle driving strategy according to an embodiment of this application is shown;

[0025] Figure 3 A flowchart illustrating a method for recommending vehicle driving strategies according to an embodiment of this application is shown;

[0026] Figure 4 A detailed flowchart illustrating the calculation of driving risk values ​​of the target vehicle in the at least two lanes, according to an embodiment of this application, is shown.

[0027] Figure 5 A detailed flowchart illustrating the prediction of the target vehicle's predicted driving parameters and the reference vehicle's predicted driving parameters according to one embodiment of this application is shown.

[0028] Figure 6 This illustration shows a scenario where the target vehicle changes lanes to the adjacent lane according to one embodiment of the present application.

[0029] Figure 7 A detailed flowchart is shown, illustrating how a first driving risk value of the target vehicle in the target lane is obtained when the reference vehicle in the target lane is a freight vehicle, according to an embodiment of this application.

[0030] Figure 8 A detailed flowchart of a recommended driving strategy for the target vehicle according to one embodiment of this application is shown;

[0031] Figure 9 A detailed flowchart of a recommended driving strategy for the target vehicle according to one embodiment of this application is shown;

[0032] Figure 10 A schematic diagram illustrating a cloud-based recommendation of vehicle driving strategies according to an embodiment of this application is shown;

[0033] Figure 11 A block diagram of a vehicle driving strategy recommendation device according to an embodiment of this application is shown;

[0034] Figure 12 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0035] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0036] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0039] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solutions of the embodiments of this application can be applied is shown.

[0040] like Figure 1 As shown, the system architecture may include terminal devices (such as...) Figure 1 The device shown includes one or more of the following: smartphone 101, tablet computer 102, and portable computer 103. It can also be a desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The network 104 and server 105 are also shown. The network 104 is used as a medium to provide a communication link between the terminal device and the server 105. The network 104 can include various connection types, such as wired communication links, wireless communication links, etc., which are not limited herein. It should be understood that... Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, networks, and servers. For example, server 105 could be a server cluster composed of multiple servers.

[0041] In one embodiment of this application, such as Figure 1 All terminal devices shown can be used as target vehicles in this application. The terminal device can send a request to the server to recommend a driving strategy for the target vehicle. After receiving the request, the server 105 obtains the environmental parameters of the road segment where the target vehicle is located and the driving parameters of each vehicle in the road segment. Then, based on the vehicle positioning information in the driving parameters, it determines the reference vehicle that is driving in front of the target vehicle and closest to the target vehicle in at least two lanes. It then calculates the driving risk value of the target vehicle in each of the at least two lanes using a driving risk model. Finally, based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold, it determines the driving strategy of the target vehicle and recommends the driving strategy to the target vehicle.

[0042] It should be noted that the vehicle driving strategy recommendation method provided in this application embodiment is generally executed by server 105, and correspondingly, the vehicle driving strategy recommendation device is generally set in server 105. However, in other embodiments of this application, the terminal device may also have similar functions to the server, thereby executing the vehicle driving strategy recommendation scheme provided in this application embodiment.

[0043] It should be noted that a server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0044] Specifically, as mentioned above, cloud computing is a computing model that distributes computing tasks across a resource pool composed of a large number of computers, enabling various application systems to obtain computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, the resources in the "cloud" are infinitely scalable and can be accessed, used on demand, and expanded at any time. By establishing a cloud computing resource pool (referred to as a cloud platform, generally called an IaaS (Infrastructure as a Service) platform), various types of virtual resources are deployed in the resource pool for external customers to choose from. The cloud computing resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, and network devices.

[0045] In one embodiment of this application, the application scenario for the method of recommending vehicle driving strategies can be as follows: Figure 2 The application scenario diagram is shown.

[0046] See Figure 2 The diagram illustrates an application scenario of a recommended method for implementing a vehicle driving strategy according to an embodiment of this application.

[0047] Specifically, in such Figure 2 In the road segment 200 shown, there are four vehicles: A, B, C, and D. Vehicle A is the target vehicle, and vehicles B, C, and D are reference vehicles. During the journey, the driving strategy of the target vehicle A will be influenced by the reference vehicles A, B, and C. In the implementation of the vehicle driving strategy recommendation method, the environmental parameters of road segment 200 and the driving parameters of vehicles A, B, C, and D can be obtained. As shown in the figure, vehicles B, C, and D are the reference vehicles that are driving in front of vehicle A and are closest to vehicle A. Furthermore, the driving risk values ​​of vehicle A in the three lanes shown in the figure can be calculated using the environmental parameters of road segment 200 and the driving parameters of vehicles A, B, C, and D. Finally, the driving strategy of vehicle A is determined based on the relationship between the driving risk values ​​of vehicle A in the three lanes and the predetermined driving risk threshold.

[0048] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0049] See Figure 3 A flowchart illustrating a method for recommending vehicle driving strategies according to an embodiment of this application is shown. This method for recommending vehicle driving strategies can be executed by a device with computational processing capabilities, such as... Figure 1 The server 105 shown can be used to perform this, or it can be executed by... Figure 1 The terminal device shown can execute the command, but it can also be executed by a cloud server with cloud computing capabilities. For example... Figure 3 As shown, the recommended method for this vehicle driving strategy includes at least steps 310 to 370:

[0050] In step 310, environmental parameters of the road segment where the target vehicle is located and driving parameters of each vehicle in the road segment are obtained. The road segment includes at least two lanes, and the driving parameters include vehicle positioning information.

[0051] In one embodiment of this application, the at least two lanes may include a target lane and an adjacent lane, wherein the target vehicle travels in the target lane, and the adjacent lane is a lane adjacent to the target lane.

[0052] In this application, the positioning information may include the position coordinates of each vehicle in the road segment. The position coordinates may be two-dimensional coordinates, latitude and longitude coordinates, or three-dimensional coordinates. For example, it may be any one of vehicle GPS positioning information, vehicle Beidou satellite positioning information, or vehicle two-dimensional coordinate positioning information.

[0053] In this application, the environmental parameters of the road segment may include one or more of the following: road viscosity, road curvature, road humidity, road slope, road visibility, and road friction coefficient.

[0054] In this application, the driving parameters of each vehicle in the road segment may also include vehicle mass, vehicle vector velocity, and vehicle acceleration.

[0055] Continue to refer to Figure 3 In step 330, based on the vehicle positioning information of each vehicle in the road segment, a reference vehicle that is traveling in front of the target vehicle and is closest to the target vehicle is determined in each of the at least two lanes.

[0056] Specifically, in this application, based on the vehicle positioning information of each vehicle in the road segment, the distance between each vehicle and the direction of each vehicle relative to the target vehicle can be determined, and then a reference vehicle that is traveling in front of the target vehicle and closest to the target vehicle can be determined in the at least two lanes.

[0057] Continue to refer to Figure 3 In step 350, based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters, the driving risk value of the target vehicle in the at least two lanes is calculated using a driving risk model.

[0058] In one embodiment of this application, based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters, a driving risk model is used to calculate the driving risk value of the target vehicle in at least two lanes, which can be done as follows: Figure 4 Perform the steps shown.

[0059] See Figure 4 This document illustrates a detailed flowchart of calculating the driving risk values ​​of the target vehicle in at least two lanes, according to an embodiment of this application. Specifically, it includes steps 351 to 353:

[0060] In step 351, based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle are predicted when the target vehicle changes lanes to the adjacent lane line. The adjacent lane line is the lane dividing line between the target lane and the adjacent lane.

[0061] In one specific implementation, based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lanes to the adjacent lane can be predicted as follows: Figure 5 Perform the steps shown.

[0062] See Figure 5 This document illustrates a detailed flowchart of a method for predicting the predicted driving parameters of a target vehicle and a reference vehicle according to an embodiment of this application. Specifically, it includes steps 3511 to 3512:

[0063] Step 3511: Determine the lane change time required for the target vehicle to change lanes from the target lane to the adjacent lane line.

[0064] Step 3512: Based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, and the lane change time, predict the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lanes to the adjacent lane line.

[0065] To enable those skilled in the art to better understand the prediction process of the predicted driving parameters for the target vehicle and the reference vehicle, the following will combine... Figure 6 Supplementary explanation:

[0066] See Figure 6 The figure illustrates a scenario where a target vehicle changes lanes to the adjacent lane line according to an embodiment of this application. In the figure, A1 or A2 represents the position of vehicle A when it changes lanes to the adjacent lane line, B1 represents the position of vehicle B when vehicle A is at position A1, and C1 represents the position of vehicle C when vehicle A is at position A2.

[0067] In the specific calculation, the time taken for vehicle A to travel to position A1 or A2 can be determined based on the driving parameters of vehicle A. Then, the driving positions (B1 and D1) of vehicles B and D when vehicle A travels to position A1 or A2 can be predicted based on the driving parameters of vehicles B and D. Thus, the predicted driving parameters of vehicles A, B, and D are obtained (the actual prediction is the predicted driving positions of vehicles A, B, and D).

[0068] Continue to refer to Figure 4In step 352, the predicted driving parameters of the target vehicle, the predicted driving parameters of the reference vehicle, and the environmental parameters are input into the driving risk model to obtain the driving risk value of the target vehicle in the adjacent lane.

[0069] 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 the driving risk model to obtain the first driving risk value of the target vehicle in the target lane.

[0070] In one specific implementation, 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 the driving risk model to obtain a first driving risk value of the target vehicle in the target lane. This can be done according to... Figure 7 Perform the steps shown.

[0071] See Figure 7 This document illustrates a detailed flowchart of obtaining a first driving risk value for a target vehicle in the target lane when the reference vehicle in the target lane is a freight vehicle, according to an embodiment of this application. Specifically, it includes steps 3531 to 3534:

[0072] Step 3531: Determine the probability of debris and the motion parameters of debris. The probability of debris is the probability of debris from a truck appearing in the target lane. The motion parameters of debris include the mass of debris, which is the average mass of debris in historical traffic accidents caused by debris from trucks.

[0073] It should be noted that since the debris was stationary relative to the truck before it fell, some of the debris's motion parameters are the same as some of the reference vehicle's driving parameters, such as vector velocity, acceleration, etc.

[0074] Step 3532: Input the driving parameters of the target vehicle, the motion parameters of the debris from the reference vehicle in the target lane, and the environmental parameters into the driving risk model to obtain the driving risk value of the target vehicle from the debris from the truck in the target lane.

[0075] Step 3533: Input the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane, and the environmental parameters into the driving risk model to obtain the driving risk value of the target vehicle from the reference vehicle in the target lane.

[0076] Step 3534: Based on the driving risk value from the debris from the truck, the debris probability, and the driving risk value from the reference vehicle, calculate the first driving risk of the target vehicle in the target lane.

[0077] Specifically, in step 3534, the first driving risk of the target vehicle in the target lane can be calculated using the following formula:

[0078] E1 = E t +P×E o

[0079] Wherein, E1 represents the first driving risk value of the target vehicle in the target lane; E t E represents the driving risk value derived from the reference vehicle. o The value represents the driving risk from debris falling from the truck; P represents the probability of debris falling.

[0080] Continue to refer to Figure 3 In step 370, a driving strategy for the target vehicle is recommended based on the relationship between the driving risk values ​​of the target vehicle in the at least two lanes and a predetermined driving risk threshold.

[0081] In one embodiment of this application, the adjacent lane may include a left adjacent lane and a right adjacent lane, and the driving risk value of the target vehicle in the adjacent lane 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.

[0082] In one specific implementation, a driving strategy for the target vehicle is recommended based on the relationship between the driving risk values ​​of the target vehicle in at least two lanes and a predetermined driving risk threshold. This strategy can be implemented as follows: Figure 8 Perform the steps shown.

[0083] See Figure 8 This document illustrates a detailed flowchart of a recommended driving strategy for the target vehicle according to an embodiment of this application. Specifically, it includes steps 371 to 373:

[0084] Step 371: When the first, second, and third driving risk values ​​are all greater than the driving risk threshold, it is recommended that the target vehicle reduce its speed.

[0085] Step 372: 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, then it is recommended that the target vehicle overtake.

[0086] Furthermore, 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, it is recommended that the target vehicle overtake, which can be done according to the following... Figure 9 Perform the steps shown.

[0087] See Figure 9 This document illustrates a detailed flowchart of a recommended driving strategy for the target vehicle according to an embodiment of this application. Specifically, it includes steps 3721 to 3722:

[0088] Step 3721: When the second driving risk value is less than the third driving risk value, it is recommended that the target vehicle overtake from the left adjacent lane.

[0089] Step 3722: When the third driving risk value is less than the second driving risk value, it is recommended that the target vehicle overtake from the right adjacent lane.

[0090] Step 373: When the first driving risk value is less than the driving risk threshold, it is recommended that the target vehicle not overtake.

[0091] In one specific implementation of an embodiment, the driving risk threshold may further include at least two sub-driving risk thresholds.

[0092] Specifically, a driving strategy for the target vehicle is recommended based on the relationship between the driving risk values ​​of the target vehicle in the at least two lanes and a predetermined driving risk threshold. This recommendation can be based on the 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.

[0093] 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, wherein the first sub-driving risk threshold is greater than the second sub-driving risk threshold.

[0094] When the first, second, and third driving risk values ​​are all greater than the first sub-driving risk threshold, it is recommended that the target vehicle reduce its speed.

[0095] When the first, second, and third driving risk values ​​are all greater than the second sub-driving risk threshold and all are less than the first sub-driving risk threshold, it is recommended that the target vehicle overtake.

[0096] When the first, second, and third driving risk values ​​are all less than the second sub-driving risk threshold, it is recommended that the target vehicle not overtake.

[0097] Those skilled in the art should understand that, based on the relationship between the driving risk values ​​of the target vehicle in the at least two lanes and the predetermined driving risk threshold, the recommended driving strategy for the target vehicle can have other specific implementations, and is not limited to the two listed above.

[0098] To enable those skilled in the art to better understand this application, the model in the prior art will be briefly described below.

[0099] The following is the formula for calculating the driving risk value between two moving vehicles (moving objects):

[0100]

[0101] Among them, SPE V_ab G represents the driving risk value between vehicle (object) a and vehicle (object) b; G is a constant (similar to the gravitational constant); R a This refers to the road condition parameters of the road surface where vehicle (object) a is located. These parameters comprehensively measure the road surface's viscosity, humidity, slope, and temperature, and are generally related to R. b Equal; R b This refers to the road condition parameters of the road surface where vehicle (object) b is located. These parameters comprehensively measure the road surface's viscosity, humidity, slope, and temperature, and are generally related to R. a Equal; M a M represents the mass of vehicle a; b Let k represent the mass of vehicle b; k3 is a constant (equal to the speed of light); k1 is a constant (generally 3 in air); This represents the straight-line distance between vehicle a and vehicle b. θ represents the relative speed between vehicle a and vehicle b. a This represents the angle between the directions of travel of vehicle a and vehicle j.

[0102] In this application, in embodiments that recommend driving strategies for vehicles in multi-vehicle road segments, a system integrating automotive cloud, regional cloud, and edge cloud can be built to recommend driving strategies for various vehicles in the vehicle network through a cloud-vehicle system, such as... Figure 10 This diagram illustrates a cloud-based recommendation of vehicle driving strategies according to an embodiment of this application. The system consists of a cloud and a vehicle-to-everything (V2X) network. All computational functions of this solution can be implemented on the vehicle cloud, allowing the vehicle to acquire its own driving parameters in real time and upload them to the vehicle cloud.

[0103] Specifically, the vehicle cloud first obtains the environmental parameters of the road segment where the target vehicle is located, as well as the driving parameters of each vehicle in the road segment. Then, based on the vehicle positioning information in the driving parameters, it determines the reference vehicle that is driving in front of the target vehicle and closest to the target vehicle in at least two lanes. It then calculates the driving risk value of the target vehicle in each of the at least two lanes using a driving risk model. Finally, based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold, it recommends a driving strategy for the target vehicle.

[0104] In some embodiments of this application, the technical solutions first obtain environmental parameters of the road segment where the target vehicle is located, as well as the driving parameters of each vehicle in the road segment. Then, based on the vehicle positioning information in the driving parameters, reference vehicles that are driving in front of and closest to the target vehicle in at least two lanes are determined. A driving risk model is then used to calculate the driving risk value of the target vehicle in each of the at least two lanes. Finally, based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold, a driving strategy for the target vehicle is recommended. Since the driving risk value of the target vehicle in a lane reflects its driving risk in that lane, a scientifically reasonable driving strategy can be recommended by referring to the relationship between the driving risk value of the target vehicle in a lane and the predetermined driving risk threshold. Therefore, the technical solutions provided in some embodiments of this application can improve vehicle driving safety.

[0105] The following describes an embodiment of the apparatus described in this application, which can be used to execute the vehicle driving strategy recommendation method in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the vehicle driving strategy recommendation method described above in this application.

[0106] Figure 11 A block diagram of a vehicle driving strategy recommendation device according to an embodiment of this application is shown.

[0107] Reference Figure 11 As shown, a vehicle driving strategy recommendation device 1100 according to an embodiment of this application includes: an acquisition unit 1101, a determination unit 1102, a calculation unit 1103, and a recommendation unit 1104.

[0108] The acquisition unit 1101 is used to acquire environmental parameters of the road segment where the target vehicle is located, and driving parameters of each vehicle in the road segment, wherein the road segment includes at least two lanes, and the driving parameters include vehicle positioning information; the determination unit 1102 is used to determine, based on the vehicle positioning information of each vehicle in the road segment, a reference vehicle that is driving in front of the target vehicle and is closest to the target vehicle in each of the at least two lanes; the calculation unit 1103 is used to calculate the driving risk value of the target vehicle in each of the at least two lanes based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters, using a driving risk model; and the recommendation unit 1104 is used to recommend a driving strategy for the target vehicle based on the relationship between the driving risk value of the target vehicle in each of the at least two lanes and a predetermined driving risk threshold.

[0109] In some embodiments of this application, based on the foregoing scheme, the at least two lanes include a target lane and an adjacent lane, the target vehicle travels in the target lane, and the adjacent lane is the lane adjacent to the target lane.

[0110] In some embodiments of this application, based on the foregoing scheme, the calculation unit 1103 includes: a prediction unit, used to predict the predicted driving parameters of the target vehicle and the reference vehicle when the target vehicle changes lanes to an adjacent lane line, based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, wherein the adjacent lane line is the lane dividing line between the target lane and the adjacent lane; a first input unit, used to input the predicted driving parameters of the target vehicle, the predicted driving 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 a second input unit, used to input 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.

[0111] In some embodiments of this application, based on the foregoing scheme, the prediction unit is configured to: determine the lane change time required for the target vehicle to change lanes from the target lane to the adjacent lane; and, based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, and the lane change time, predict the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lanes to the adjacent lane.

[0112] In some embodiments of this application, based on the foregoing scheme, the first input unit is configured to: determine the probability of debris and debris movement parameters, wherein the probability of debris is the probability of debris from a truck appearing in the target lane, and the debris movement parameters include debris mass, which is the average mass of debris in historical traffic accidents caused by truck debris; input the driving parameters of the target vehicle, the debris movement parameters of a reference vehicle in the target lane, and the environmental parameters into a driving risk model to obtain the driving risk value of the target vehicle from truck debris in the target lane; input the driving parameters of the target vehicle, the driving parameters of the reference vehicle in the target lane, and the environmental parameters into the driving risk model to obtain the driving risk value of the target vehicle from the reference vehicle in the target lane; and calculate the first driving risk of the target vehicle in the target lane based on the driving risk value from truck debris, the probability of debris, and the driving risk value from the reference vehicle.

[0113] In some embodiments of this application, based on the foregoing scheme, 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.

[0114] In some embodiments of this application, based on the foregoing scheme, the recommendation unit 1104 is configured to: recommend that the target vehicle reduce its speed when the first, second, and third driving risk values ​​are all greater than the driving risk threshold; recommend that the target vehicle overtake 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; and recommend that the target vehicle not overtake when the first driving risk value is less than the driving risk threshold.

[0115] In some embodiments of this application, based on the foregoing scheme, the recommendation unit 1104 is configured to: recommend the target vehicle to overtake from the left adjacent lane when the second driving risk value is less than the third driving risk value; and recommend the target vehicle to overtake from the right adjacent lane when the third driving risk value is less than the second driving risk value.

[0116] In some embodiments of this application, based on the foregoing scheme, the driving risk threshold includes at least two sub-driving risk thresholds, and the recommendation unit 1104 is configured to recommend a driving strategy for the target vehicle based on the driving risk values ​​of the target vehicle in the at least two lanes and the magnitude relationship between the at least two sub-driving risk thresholds.

[0117] In some embodiments of this application, based on the foregoing scheme, the environmental parameters of the road segment include one or more of the following: road viscosity, road curvature, road humidity, road slope, road visibility, and road friction coefficient.

[0118] In some embodiments of this application, based on the aforementioned scheme, the driving parameters of each vehicle in the road segment also include vehicle mass, vehicle vector velocity, and vehicle acceleration.

[0119] In some embodiments of this 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.

[0120] Figure 12 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0121] It should be noted that, Figure 12 The computer system 1200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0122] like Figure 12 As shown, the computer system 1200 includes a Central Processing Unit (CPU) 1201, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1202 or programs loaded from storage portion 1208 into Random Access Memory (RAM) 1203, such as performing the methods described in the above embodiments. Various programs and data required for system operation are also stored in RAM 1203. The CPU 1201, ROM 1202, and RAM 1203 are interconnected via bus 1204. An Input / Output (I / O) interface 1205 is also connected to bus 1204.

[0123] The following components are connected to I / O interface 1205: an input section 1206 including a keyboard, mouse, etc.; an output section 1207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1208 including a hard disk, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1209 performs communication processing via a network such as the Internet. A drive 1210 is also connected to I / O interface 1205 as needed. Removable media 1211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1210 as needed so that computer programs read from them can be installed into storage section 1208 as needed.

[0124] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1209, and / or installed from removable medium 1211. When the computer program is executed by central processing unit (CPU) 1201, it performs various functions defined in the system of this application.

[0125] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0127] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0128] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0129] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0130] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0131] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0132] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for recommending vehicle driving strategies, characterized in that, The method includes: The environmental parameters of the road segment where the target vehicle is located, as well as the driving parameters of each vehicle in the road segment, are obtained. The road segment includes at least two lanes, and the at least two lanes include a target lane and an adjacent lane. The adjacent lane is the lane adjacent to the target lane. The driving parameters include vehicle positioning information. Based on the vehicle positioning information of each vehicle in the road segment, a reference vehicle that is traveling in front of the target vehicle and closest to the target vehicle is determined in each of the at least two lanes. Based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters, the driving risk values ​​of the target vehicle in the at least two lanes are calculated using a driving risk model. Based on the relationship between the driving risk values ​​of the target vehicle in the at least two lanes and a predetermined driving risk threshold, a driving strategy for the target vehicle is recommended, including a strategy for indicating whether to overtake. Specifically, based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters, a driving risk model is used to calculate the driving risk value of the target vehicle in at least two lanes, including: Determine the lane-changing time required for the target vehicle to change lanes from the target lane to the adjacent lane line, wherein the adjacent lane line is the lane dividing line between the target lane and the adjacent lane; Based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, as well as the lane change time, predict the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lanes to the adjacent lane. The predicted driving parameters of the target vehicle, the predicted driving parameters of the reference vehicle, and the environmental parameters are input into the driving risk model to obtain the driving risk value of the target vehicle in the adjacent lane. When the reference vehicle in the target lane is a freight vehicle, the probability of debris and the motion parameters of debris are determined. The motion parameters of debris include the mass of debris, which is the average mass of debris in historical traffic accidents caused by truck debris. The driving parameters of the target vehicle, the motion parameters of the debris from the reference vehicle in the target lane, and the environmental parameters are input into the driving risk model to obtain the driving risk value of the target vehicle from the debris from the truck in the target lane. 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 the driving risk model to obtain the driving risk value of the target vehicle from the reference vehicle in the target lane. Calculate the product of the probability of the debris and the driving risk value from the debris from the truck, and calculate the sum of the driving risk value from the reference vehicle and the product to obtain the first driving risk value of the target vehicle in the target lane.

2. The method according to claim 1, characterized in that, The adjacent lanes include the left adjacent lane and the right adjacent lane, and the driving risk value of the target vehicle in the adjacent lanes includes the second driving risk value of the target vehicle in the left adjacent lane and the third driving risk value of the target vehicle in the right adjacent lane.

3. The method according to claim 2, characterized in that, The step of recommending a driving strategy for the target vehicle based on the relationship between the driving risk value of the target vehicle in at least two lanes and a predetermined driving risk threshold includes: When the first, second, and third driving risk values ​​are all greater than the driving risk threshold, it is recommended that the target vehicle reduce its speed. 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, it is recommended that the target vehicle overtake. When the first driving risk value is less than the driving risk threshold, it is recommended that the target vehicle not overtake.

4. The method according to claim 3, characterized in that, 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 method of recommending the target vehicle to overtake includes: When the second driving risk value is less than the third driving risk value, it is recommended that the target vehicle overtake from the left adjacent lane; When the third driving risk value is less than the second driving risk value, it is recommended that the target vehicle overtake from the right adjacent lane.

5. The method according to claim 2, characterized in that, The driving risk threshold includes at least two sub-driving risk thresholds. The method of recommending a driving strategy for the target vehicle based on the relationship between the driving risk values ​​of the target vehicle in the at least two lanes and the predetermined driving risk threshold includes: Based on the 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, a driving strategy for the target vehicle is recommended.

6. The method according to any one of claims 1 to 5, characterized in that, The environmental parameters of the road section include one or more of the following: road viscosity, road curvature, road humidity, road slope, road visibility, and road friction coefficient.

7. The method according to any one of claims 1 to 5, characterized in that, The driving parameters of each vehicle in the road segment also include vehicle mass, vehicle vector velocity, and vehicle acceleration.

8. The method according to any one of claims 1 to 5, characterized in that, The vehicle positioning information includes any one of the following: vehicle GPS positioning information, vehicle Beidou satellite positioning information, and vehicle two-dimensional coordinate positioning information.

9. A vehicle driving strategy recommendation device, characterized in that, The device includes: The acquisition unit is used to acquire environmental parameters of the road segment where the target vehicle is located, as well as driving parameters of each vehicle in the road segment. The road segment includes at least two lanes, including a target lane and an adjacent lane. The adjacent lane is the lane adjacent to the target lane. The driving parameters include vehicle positioning information. The determining unit is used to determine, based on the vehicle positioning information of each vehicle in the road segment, a reference vehicle that is traveling in front of the target vehicle and is closest to the target vehicle in at least two lanes. The calculation unit is used to calculate the driving risk value of the target vehicle in the at least two lanes based on the driving parameters of the target vehicle, the driving parameters of the reference vehicle, and the environmental parameters, using a driving risk model. The recommendation unit is used to recommend a driving strategy for the target vehicle based on the relationship between the driving risk value of the target vehicle in the at least two lanes and a predetermined driving risk threshold. The driving strategy includes a strategy for indicating whether to overtake. The computing unit is further used to perform the following steps: Determine the lane-changing time required for the target vehicle to change lanes from the target lane to the adjacent lane line, wherein the adjacent lane line is the lane dividing line between the target lane and the adjacent lane; Based on the driving parameters of the target vehicle and the driving parameters of the reference vehicle, as well as the lane change time, predict the predicted driving parameters of the target vehicle and the predicted driving parameters of the reference vehicle when the target vehicle changes lanes to the adjacent lane. The predicted driving parameters of the target vehicle, the predicted driving parameters of the reference vehicle, and the environmental parameters are input into the driving risk model to obtain the driving risk value of the target vehicle in the adjacent lane. When the reference vehicle in the target lane is a freight vehicle, the probability of debris and the motion parameters of debris are determined. The motion parameters of debris include the mass of debris, which is the average mass of debris in historical traffic accidents caused by truck debris. The driving parameters of the target vehicle, the motion parameters of the debris from the reference vehicle in the target lane, and the environmental parameters are input into the driving risk model to obtain the driving risk value of the target vehicle from the debris from the truck in the target lane. 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 the driving risk model to obtain the driving risk value of the target vehicle from the reference vehicle in the target lane. Calculate the product of the probability of the debris and the driving risk value from the debris from the truck, and calculate the sum of the driving risk value from the reference vehicle and the product to obtain the first driving risk value of the target vehicle in the target lane.

10. A computer-readable storage medium having a computer program stored thereon, the computer program including executable instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 8.

11. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the executable instructions to implement the method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, Includes a computer program carried on a computer-readable medium, which, when executed, implements the method as described in any one of claims 1 to 8.

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