Dynamic pre-decision method based on Internet of Vehicles perception

Abstract

The invention discloses a dynamic pre-decision method, device and equipment based on Internet of Vehicles perception, and a storage medium, is applied to a local node vehicle, and belongs to the technical field of vehicle safety, and the method comprises the steps: obtaining first driving state information of a first foreign node vehicle and a second action strategy of a second foreign node vehicle; determining a local response strategy corresponding to a second action strategy according to one or more of the first driving state information and the second action strategy, and acquiring second driving state information of a second foreign node vehicle; and executing a local response strategy under the condition that the second driving state information meets a preset second driving state threshold value. According to the method and the device, the driving state information and the action strategy of the non-local node vehicle are acquired to dynamically adjust and execute the local response strategy, so that the problem of insufficient vehicle safety decision-making flexibility in a driving safety scene in the prior art is effectively solved.

Description

Technical Field

[0001] The present application belongs to the field of vehicle safety technology, and specifically relates to a dynamic pre-decision method, device, equipment and storage medium based on vehicle network perception. Background Art

[0002] Vehicle-to-Everything (V2X) is a comprehensive system network that uses the intra-vehicle network, inter-vehicle network and in-vehicle mobile Internet to achieve wireless communication and information exchange between vehicles (V2V), vehicles and pedestrians (V2P), vehicles and road infrastructure (V2I) and vehicles and networks (V2N) in accordance with agreed communication protocols and data exchange standards.

[0003] At present, the Internet of Vehicles can be applied to driving safety and traffic efficiency scenarios, which mainly rely on information exchange between vehicles and driver decisions.

[0004] However, in driving safety scenarios, information interaction for vehicle safety decisions is limited to directly related vehicles and cannot meet more flexible application requirements. Summary of the invention

[0005] The present application aims to provide a dynamic pre-decision method, device, equipment and storage medium based on vehicle network perception, at least to solve the problem of insufficient flexibility in vehicle safety decision-making in driving safety scenarios.

[0006] In a first aspect, the embodiment of the present application discloses a dynamic pre-decision method based on vehicle network perception, which is applied to a local node vehicle, including:

[0007] Acquire first driving state information of a first out-of-town node vehicle, and acquire a second action strategy of a second out-of-town node vehicle when the first driving state information satisfies a preset first driving state threshold; the second action strategy is used to: process the impact of the first driving state information on the driving environment of the second out-of-town node vehicle; the second action strategy is generated by the second out-of-town node vehicle according to the first driving state information;

[0008] According to the first driving state information and one or more information of the second action strategy, a local response strategy corresponding to the second action strategy is determined, and the second driving state information of the second foreign node vehicle is obtained; the local response strategy is used to: process the impact of the execution of the second action strategy on the driving environment of the local node vehicle;

[0009] When the second driving state information meets a preset second driving state threshold, the local response strategy is executed.

[0010] In a second aspect, the embodiment of the present application further discloses a dynamic pre-decision device based on vehicle networking perception, which is applied to a local node vehicle, including:

[0011] A data acquisition module is configured to acquire first driving state information of a first out-of-town node vehicle, and acquire a second action strategy of a second out-of-town node vehicle when the first driving state information satisfies a preset first driving state threshold; the second action strategy is used to: process the impact of the first driving state information on the driving environment of the second out-of-town node vehicle; the second action strategy is generated by the second out-of-town node vehicle according to the first driving state information;

[0012] A strategy generation module, used to determine a local response strategy corresponding to the second action strategy according to the first driving state information and one or more information of the second action strategy, and obtain the second driving state information of the second foreign node vehicle; the local response strategy is used to: process the impact of the execution of the second action strategy on the driving environment of the local node vehicle;

[0013] A strategy execution module is used to execute the local response strategy when the second driving state information meets a preset second driving state threshold.

[0014] In a third aspect, an embodiment of the present application further discloses an electronic device, comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0015] In a fourth aspect, an embodiment of the present application further discloses a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0016] In summary, in the embodiment of the present application, by acquiring the first driving state information, and the second action strategy generated for the first driving state, and the second driving state information generated based on the second action strategy, the driving state of the surrounding vehicles is monitored in real time, ensuring the comprehensive perception of the external environment and improving the system's sensitivity to changes in the external environment; and then determining the corresponding local response strategy, taking into account the state information and action strategy of the external vehicle, formulating and executing the optimal local response strategy, ensuring the comprehensiveness and rationality of the decision, so as to respond to changes in the external environment in a timely manner and ensure the safety and flexibility of vehicle driving. Therefore, based on the method of the embodiment of the present application, by acquiring the driving state information and action strategy of the external node vehicle, the local response strategy can be dynamically adjusted and executed, effectively solving the problem of insufficient flexibility of vehicle safety decision-making in driving safety scenarios in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In the attached picture:

[0018] Figure 1 It is a flowchart of the steps of a dynamic pre-decision method based on vehicle networking perception provided by an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of a vehicle group topology network under the method provided in an embodiment of the present application;

[0020] Figure 3 is a flowchart of another method for dynamic pre-decision making based on Internet of Vehicles perception provided by an embodiment of the present application;

[0021] Figure 4 It is a block diagram of a dynamic pre-decision device based on Internet of Vehicles perception provided by an embodiment of the present application;

[0022] Figure 5 is a block diagram of an electronic device according to an embodiment of the present application;

[0023] Figure 6 It is a block diagram of an electronic device of another embodiment provided by the embodiments of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0026] Figure 1 This embodiment provides a dynamic pre-decision method based on Internet of Vehicles perception, which is applied to local node vehicles.

[0027] The method may include the following steps:

[0028] Step 101, obtain first driving state information of a first out-of-town node vehicle, and when the first driving state information meets a preset first driving state threshold, obtain a second action strategy of a second out-of-town node vehicle.

[0029] Among them, the second action strategy is used to: deal with the impact of the first driving status information on the driving environment of the second external node vehicle; the second action strategy is generated by the second external node vehicle according to the first driving status information.

[0030] In some embodiments of the present application, the first driving state information of the first out-of-town node vehicle is obtained, and when the information meets the preset first driving state threshold, the second action strategy of the second out-of-town node vehicle is obtained. The first driving state information may include data such as the speed, position, and direction of the vehicle. When these data meet the preset threshold (such as the speed exceeds a certain limit or the position is close to a specific area), the system will obtain the second action strategy of the second out-of-town node vehicle. The strategy is generated by the second out-of-town node vehicle based on the first driving state information, and is used to deal with the impact of the first driving state information on its driving environment.

[0031] For example, Figure 2As shown, assuming that in a vehicle networking scenario, vehicle A is the first out-of-town node vehicle, vehicle B is the second out-of-town node vehicle, and vehicle C is the local node vehicle. The first driving state information of vehicle A includes its current speed of 80km / h, its position on a certain highway section, and its direction is north. The preset first driving state threshold is a speed exceeding 70km / h. When the speed of vehicle A reaches 80km / h, the preset threshold condition is met. At this time, the system will obtain the second action strategy of vehicle B. Vehicle B generates a deceleration strategy based on the speed and position of vehicle A to avoid possible collision risks. The second action strategy of vehicle B may include reducing the speed from 90km / h to 70km / h and staying in the right lane. By acquiring and executing this strategy, vehicle B can effectively respond to changes in the driving state of vehicle A and ensure driving safety.

[0032] Step 102, based on the first driving state information and one or more information of the second action strategy, determine the local response strategy corresponding to the second action strategy, and obtain the second driving state information of the second external node vehicle.

[0033] Among them, the local response strategy is used to deal with the impact of the execution of the second action strategy on the driving environment of the local node vehicle.

[0034] In some embodiments of the present application, a local response strategy corresponding to the second action strategy is determined based on the first driving state information and the second action strategy, or a local response strategy corresponding to the second action strategy is determined based on the second action strategy, and the second driving state information of the second foreign node vehicle is obtained. The local response strategy is to respond to the execution of the second action strategy, ensure that the local node vehicle can adapt to the action changes of the foreign node vehicle, thereby ensuring driving safety and efficiency.

[0035] For example, Figure 2 As shown, assuming that in a vehicle networking scenario, vehicle A is the first foreign node vehicle, vehicle B is the second foreign node vehicle, and vehicle C is the local node vehicle. The first driving status information of vehicle A shows that its speed is 80km / h, its location is on a certain highway section, and its direction is north. Vehicle B generates a deceleration strategy based on the status of vehicle A, reducing the speed from 90km / h to 70km / h and keeping it in the right lane. After receiving the second action strategy of vehicle B, vehicle C determines its own local response strategy. The local response strategy of vehicle C may include decelerating to 70km / h and keeping it in the middle lane to avoid a collision with vehicle B. At the same time, vehicle C obtains the second driving status information of vehicle B and confirms that the deceleration strategy of vehicle B has been executed. In this way, vehicle C can adjust its driving strategy in time to ensure safe and smooth driving.

[0036] Step 103: When the second driving state information meets a preset second driving state threshold, execute a local response strategy.

[0037] In some embodiments of the present application, the local response strategy will be executed when the second driving state information meets the preset second driving state threshold. The second driving state information includes data such as the speed, position and direction of the vehicle. When these data meet the preset threshold (such as the speed exceeds a certain limit or the position is close to a certain area), the local node vehicle will execute the previously determined local response strategy to ensure driving safety and efficiency.

[0038] For example, Figure 2 As shown, assuming that in a vehicle networking scenario, vehicle A is the first out-of-town node vehicle, vehicle B is the second out-of-town node vehicle, and vehicle C is the local node vehicle. The second driving status information of vehicle B shows that its speed is 70km / h, its position is on a certain section of highway, and its direction is north. The preset second driving status threshold is a speed below 75km / h. When the speed of vehicle B drops to 70km / h, the preset threshold condition is met. At this time, vehicle C will execute the local response strategy determined previously. The local response strategy of vehicle C may include decelerating to 65km / h and staying in the middle lane to avoid a collision with vehicle B. By executing this strategy, vehicle C can effectively respond to changes in the driving status of vehicle B and ensure driving safety.

[0039] In summary, in the embodiment of the present application, by acquiring the first driving state information, and the second action strategy generated for the first driving state, and the second driving state information generated based on the second action strategy, the driving state of the surrounding vehicles is monitored in real time, ensuring the comprehensive perception of the external environment and improving the system's sensitivity to changes in the external environment; and then determining the corresponding local response strategy, taking into account the state information and action strategy of the external vehicle, formulating and executing the optimal local response strategy, ensuring the comprehensiveness and rationality of the decision, so as to respond to changes in the external environment in a timely manner and ensure the safety and flexibility of vehicle driving. Therefore, based on the method of the embodiment of the present application, by acquiring the driving state information and action strategy of the external node vehicle, the local response strategy can be dynamically adjusted and executed, effectively solving the problem of insufficient flexibility of vehicle safety decision-making in driving safety scenarios in the prior art.

[0040] Figure 3 Another dynamic pre-decision method based on Internet of Vehicles perception provided in an embodiment of the present application is applied to a local node vehicle. The method may include the following steps:

[0041] Step 201: Acquire driving environment information sent by at least one vehicle.

[0042] In some embodiments of the present application, driving environment information sent by at least one vehicle is obtained. Driving environment information includes data such as the speed, position, direction, and timestamp of the vehicle. This information is broadcast and received through a physical channel (such as PSSCH) in the vehicle networking communication mode. Obtaining this information is the basis for vehicle networking, strategy formulation, and execution in subsequent steps.

[0043] For example, suppose that in a vehicle networking scenario, vehicle A and vehicle B are foreign node vehicles, and vehicle C is a local node vehicle. Vehicle A and vehicle B periodically broadcast their driving environment information through the PSSCH channel. The driving environment information of vehicle A includes a speed of 80km / h, a position on a certain highway section, a direction of north, and a timestamp of the current time. The driving environment information of vehicle B includes a speed of 70km / h, a position on the same highway section, a direction of north, and a timestamp of the current time. Vehicle C receives and parses the driving environment information of vehicles A and B through the PSSCH channel to provide data support for subsequent strategy formulation and execution. In this way, vehicle C can obtain the driving environment information of surrounding vehicles in real time to ensure driving safety and efficiency.

[0044] Step 202, the vehicle whose driving environment information matches the preset first driving environment threshold is determined as the first external node vehicle, and the vehicle whose driving environment information matches the preset second driving environment threshold is determined as the second external node vehicle, so that the local node vehicle and the first external node vehicle and the second external node vehicle establish a vehicle group topology network.

[0045] In some embodiments of the present application, a vehicle whose driving environment information matches a preset second driving environment threshold is determined as a second foreign node vehicle, and a vehicle whose driving environment information matches a preset first driving environment threshold is determined as a first foreign node vehicle. In this way, the local node vehicle can establish a vehicle group topology network with the second foreign node vehicle and the first foreign node vehicle. The preset driving environment thresholds may include parameters such as speed, position, and direction, which are used to screen qualified vehicles to ensure the effectiveness and security of the networking.

[0046] For example, suppose that in a vehicle networking scenario, vehicles A, B, and C are foreign node vehicles, and vehicle D is a local node vehicle. The driving environment information of vehicle A shows that its speed is 80 km / h, its location is on a certain highway section, and its direction is north. The preset second driving environment threshold is a speed exceeding 75 km / h. The driving environment information of vehicle B shows that its speed is 70 km / h, its location is on the same highway section, and its direction is north. The preset first driving environment threshold is a speed below 75 km / h. The driving environment information of vehicle C shows that its speed is 60 km / h, its location is on the same highway section, and its direction is north. After receiving these driving environment information, vehicle D determines vehicle A as the second foreign node vehicle because its speed exceeds the threshold of 75 km / h. Vehicle B is determined as the first foreign node vehicle because its speed is below the threshold of 75 km / h. Vehicle C does not meet any preset driving environment thresholds and is therefore not included in the vehicle group topology network. In this way, vehicle D establishes a vehicle group topology network with vehicle A and vehicle B, providing a basis for subsequent strategy formulation and execution.

[0047] In some embodiments of the present application, surrounding vehicles may be added to the vehicle group topology network in advance according to some conditions.

[0048] For example, vehicle A can decide whether to add a vehicle (such as vehicle B) to its vehicle group topology based on the following location distance or signal strength between the two vehicles:

[0049] For example, when calculating the distance between vehicle B and vehicle A, it can be directly determined whether it is within a preset distance threshold, for example, using the following formula (Haversine formula) to calculate the distance between the two vehicles.

[0050]

[0051] Where r is the radius of the earth, φ1 and φ2 are the latitudes of vehicle A and vehicle B respectively, Δφ is the latitude difference between vehicle A and vehicle B, and Δλ is the longitude difference between vehicle A and vehicle B;

[0052] Or for vehicle A, it can be checked whether the broadcast signal strength of vehicle B is greater than a preset threshold, for example, based on the received PC5 signal strength (Sidelink Received Signal Strength Indicator, S-RSSI value), to determine whether the signal strength is strong enough. If the S-RSSI value is greater than the set threshold, it is considered that the signal strength is strong enough.

[0053] Step 203, determine the vehicle whose driving environment information matches the preset third driving environment threshold as a third external node vehicle, and add the third external node vehicle to the vehicle group topology network, and / or determine the vehicle whose driving environment information matches the preset fourth driving environment threshold as a fourth external node vehicle, and add the fourth external node vehicle to the vehicle group topology network.

[0054] In some embodiments of the present application, a vehicle whose driving environment information matches a preset third driving environment threshold is determined as a third foreign node vehicle and added to the vehicle group topology network. At the same time, a vehicle whose driving environment information matches a preset fourth driving environment threshold is determined as a fourth foreign node vehicle and added to the vehicle group topology network. The preset third and fourth driving environment thresholds may include parameters such as speed, position, and direction, which are used to screen qualified vehicles to ensure the effectiveness and security of networking.

[0055] For example, suppose that in a vehicle networking scenario, vehicle A, vehicle B, vehicle C, and vehicle D are out-of-town node vehicles, and vehicle E is a local node vehicle. The driving environment information of vehicle A shows that its speed is 80km / h, its location is on a certain highway section, and its direction is north. The preset third driving environment threshold is a speed exceeding 75km / h. The driving environment information of vehicle B shows that its speed is 70km / h, its location is on the same highway section, and its direction is north. The preset fourth driving environment threshold is a speed below 75km / h. The driving environment information of vehicle C shows that its speed is 60km / h, its location is on the same highway section, and its direction is north. The driving environment information of vehicle D shows that its speed is 50km / h, its location is on the same highway section, and its direction is north. After receiving these driving environment information, vehicle E determines vehicle A as the third out-of-town node vehicle because its speed exceeds the threshold of 75km / h, and adds it to the vehicle group topology network. Vehicle B is identified as the fourth out-of-town node vehicle because its speed is below the threshold of 75 km / h, and is added to the vehicle group topology network. Vehicles C and D do not meet any preset driving environment thresholds and are therefore not included in the vehicle group topology network. In this way, vehicle E establishes a vehicle group topology network with vehicles A and B, providing a basis for subsequent strategy formulation and execution.

[0056] Step 204, obtaining first driving state information of the first out-of-town node vehicle, and obtaining a second action strategy of the second out-of-town node vehicle when the first driving state information meets a preset first driving state threshold.

[0057] Among them, the second action strategy is used to: deal with the impact of the first driving status information on the driving environment of the second external node vehicle; the second action strategy is generated by the second external node vehicle according to the first driving status information.

[0058] The method shown in this step has been explained in step 101 and will not be repeated here.

[0059] Step 205, based on the first driving state information and one or more information of the second action strategy, determine the local response strategy corresponding to the second action strategy, and obtain the second driving state information of the second external node vehicle.

[0060] Among them, the local response strategy is used to deal with the impact of the execution of the second action strategy on the driving environment of the local node vehicle.

[0061] The method shown in this step has been explained in step 102 and will not be repeated here.

[0062] Optionally, step 205 may determine a local response strategy corresponding to the second action strategy according to the first driving state information and the second action strategy, and perform the following sub-steps:

[0063] Sub-step 2051, generating an initial environment prediction result of the driving environment according to the first driving state information.

[0064] In some embodiments of the present application, an initial environment prediction result of the driving environment is generated based on the first driving state information. The initial environment prediction result is a prediction of the current driving environment, including the change trend of data such as the speed, position, and direction of the vehicle. By analyzing the first driving state information, the change of the driving environment in the future can be predicted, providing a basis for subsequent strategy formulation.

[0065] For example, suppose that in a vehicle networking scenario, vehicle A is an out-of-town node vehicle and vehicle C is a local node vehicle. The first driving status information of vehicle A shows that its speed is 80km / h, its position is on a certain section of highway, and its direction is north. Vehicle C generates an initial environmental prediction result of the driving environment based on data such as the speed, position, and direction of vehicle A. The prediction results may include the speed change trend of vehicle A in the next 5 minutes (for example, the speed may gradually decrease to 70km / h), the position change trend (for example, vehicle A will continue to travel north along the highway), and the direction change trend (for example, vehicle A will maintain the current direction). By generating these prediction results, vehicle C can understand the changes in the driving environment in advance and provide data support for subsequent strategy formulation.

[0066] Sub-step 2052, based on the initial environment prediction result, determines the first environment prediction result of the driving environment generated by the second out-of-town node vehicle under the second action strategy.

[0067] In some embodiments of the present application, a first environmental prediction result of the driving environment generated by the second out-of-town node vehicle under the second action strategy is determined based on the initial environmental prediction result. The first environmental prediction result is a prediction of the change in the driving environment of the second out-of-town node vehicle after the second action strategy is executed. By analyzing the initial environmental prediction result and the second action strategy, the change trend of the speed, position, direction and other data of the second out-of-town node vehicle after the strategy is executed can be predicted.

[0068] For example, suppose that in a vehicle networking scenario, vehicle A is the second foreign node vehicle and vehicle C is the local node vehicle. The initial environmental prediction result of vehicle A shows that its speed will gradually decrease from 80km / h to 70km / h, and its position will continue to travel north along the highway. The second action strategy of vehicle A is to slow down to 60km / h and change to the right lane. Vehicle C determines the first environmental prediction result based on the initial environmental prediction result and the second action strategy of vehicle A. The prediction results may include the speed change trend of vehicle A in the next 5 minutes (for example, the speed will gradually decrease to 60km / h), the position change trend (for example, vehicle A will change to the right lane and continue to travel north), and the direction change trend (for example, vehicle A will maintain the current direction). By determining these prediction results, vehicle C can understand in advance the changes in the driving environment of vehicle A after executing the second action strategy, providing data support for the subsequent local response strategy formulation.

[0069] Sub-step 2053, determining a local response strategy according to the first environment prediction result.

[0070] In some embodiments of the present application, a local response strategy will be determined based on the first environment prediction result. The local response strategy is to respond to changes in the driving environment of the second foreign node vehicle after executing the second action strategy, to ensure that the local node vehicle can adapt to these changes, thereby ensuring driving safety and efficiency. By analyzing the first environment prediction result, an optimal local response strategy can be formulated to respond to changes in the actions of foreign node vehicles.

[0071] For example, suppose that in a vehicle networking scenario, vehicle A is the second foreign node vehicle and vehicle C is the local node vehicle. The first environment prediction result of vehicle A shows that its speed will gradually decrease from 80km / h to 60km / h and change to the right lane. Based on this prediction result, vehicle C determines its own local response strategy. This strategy may include slowing down to 65km / h and staying in the middle lane to avoid a collision with vehicle A. At the same time, vehicle C may adjust its driving path to ensure that it can maintain a safe distance when vehicle A changes lanes. By determining these response strategies, vehicle C can effectively respond to changes in the driving environment of vehicle A and ensure safe and smooth driving.

[0072] Optionally, step 205 may determine a local response strategy corresponding to the second action strategy according to the second action strategy, and perform the following sub-steps:

[0073] Sub-step 2054, determining a second environmental prediction result of the driving environment generated by the second external node vehicle under the second action strategy.

[0074] In some embodiments of the present application, a second environmental prediction result of the driving environment generated by the second out-of-town node vehicle under the second action strategy will be determined. The second environmental prediction result is another prediction of the change in the driving environment of the second out-of-town node vehicle after the second action strategy is executed. By analyzing the execution effect of the second action strategy, the change trend of the speed, position, direction and other data of the second out-of-town node vehicle in the future period of time can be more accurately predicted.

[0075] For example, suppose that in a vehicle networking scenario, vehicle A is the second out-of-town node vehicle and vehicle C is the local node vehicle. The second action strategy of vehicle A is to slow down to 60km / h and change to the right lane. Based on the second environment prediction result of vehicle A, vehicle C determines the second environment prediction result of vehicle A after executing the second action strategy. The prediction results may include the speed change trend of vehicle A in the next 5 minutes (for example, the speed will gradually decrease to 60km / h and remain stable), the position change trend (for example, vehicle A will change to the right lane and continue to drive north), and the direction change trend (for example, vehicle A will maintain the current direction). By determining these prediction results, vehicle C can more accurately understand the changes in the driving environment of vehicle A after executing the second action strategy, and provide more accurate data support for the subsequent local response strategy formulation.

[0076] Sub-step 2055, determining a local response strategy based on the second environment prediction result.

[0077] In some embodiments of the present application, a local response strategy will be determined based on the second environment prediction results. The local response strategy is to respond to changes in the driving environment of the second foreign node vehicle after executing the second action strategy, ensuring that the local node vehicle can adapt to these changes, thereby ensuring driving safety and efficiency. By analyzing the second environment prediction results, an optimal local response strategy can be formulated to respond to changes in the actions of foreign node vehicles.

[0078] For example, suppose that in a vehicle networking scenario, vehicle A is the second foreign node vehicle and vehicle C is the local node vehicle. The second environment prediction result of vehicle A shows that its speed will gradually decrease from 80km / h to 60km / h and change to the right lane. Based on this prediction result, vehicle C determines its own local response strategy. This strategy may include slowing down to 65km / h and staying in the middle lane to avoid a collision with vehicle A. At the same time, vehicle C may adjust its driving path to ensure that it can maintain a safe distance when vehicle A changes lanes. By determining these response strategies, vehicle C can effectively respond to changes in the driving environment of vehicle A and ensure safe and smooth driving.

[0079] Step 206: When the second driving state information meets a preset second driving state threshold, execute a local response strategy.

[0080] The method shown in this step has been explained in step 103 and will not be repeated here.

[0081] Optionally, the second driving state information includes a plurality of second driving state sub-information, the second driving state threshold includes a second driving state sub-threshold corresponding to each second driving state sub-information, and step 206 includes the following sub-steps:

[0082] Sub-step 2061, determining the number of conditions fulfilled for the second driving status sub-information that meets the corresponding second driving status sub-threshold.

[0083] In some embodiments of the present application, the number of conditions fulfilled for the second driving state sub-information that satisfies the corresponding second driving state sub-threshold is determined. The second driving state information includes multiple sub-information, such as speed, position, direction, etc., and each sub-information has a corresponding sub-threshold. By detecting whether these sub-information meet their corresponding sub-thresholds, the number of sub-information that meet the conditions can be counted. This step is to evaluate whether the current driving state is significant enough to trigger a local response strategy.

[0084] For example, suppose in a vehicle networking scenario, vehicle A is an out-of-town node vehicle and vehicle C is a local node vehicle. The second driving status information of vehicle A includes a speed of 80km / h, a position on a certain highway section, and a direction of north. The preset second driving status sub-thresholds include: a speed exceeding 75km / h, a position within a specific highway section, and a direction of north. Vehicle C detects that the speed, position, and direction of vehicle A all meet the corresponding sub-threshold conditions, so the number of sub-information that meets the conditions is counted as 3. In this way, vehicle C can evaluate the significance of the current driving status and provide a basis for the subsequent formulation of local response strategies.

[0085] Sub-step 2062, when the number of conditions met is greater than a preset number threshold, execute the local response strategy.

[0086] In some embodiments of the present application, a local response strategy will be executed when the number of conditions met is greater than a preset number threshold. The number of conditions met refers to the number of second driving status sub-information that meets the second driving status sub-threshold. The preset number threshold is a set standard for determining whether a local response strategy needs to be executed. When the number of sub-information that meets the condition exceeds the preset number threshold, the local node vehicle will execute the previously determined local response strategy to ensure driving safety and efficiency.

[0087] For example, suppose that in a vehicle networking scenario, vehicle A is an out-of-town node vehicle and vehicle C is a local node vehicle. The second driving status information of vehicle A includes a speed of 80 km / h, a position on a certain highway section, and a direction of north. The preset second driving status sub-thresholds include: a speed exceeding 75 km / h, a position within a specific highway section, and a direction of north. Vehicle C detects that the speed, position, and direction of vehicle A all meet the corresponding sub-threshold conditions, and counts the number of sub-information that meet the conditions as 3. The preset quantity threshold is 2. Since the number of conditions met (3) is greater than the preset quantity threshold (2), vehicle C will execute the previously determined local response strategy, such as slowing down to 65 km / h and staying in the middle lane to avoid a collision with vehicle A. In this way, vehicle C can effectively respond to changes in the driving environment of vehicle A and ensure safe and smooth driving.

[0088] Step 207, obtaining the local driving status information of the local node vehicle, and sending the local driving status information to the third foreign node vehicle.

[0089] In some embodiments of the present application, the local driving status information of the local node vehicle is obtained and sent to the third foreign node vehicle. The local driving status information includes data such as the speed, position, and direction of the vehicle. This information is broadcast and received through the physical channel (such as PSSCH) in the vehicle network communication mode. The local driving status information is sent to ensure that the third foreign node vehicle can understand the driving status of the local node vehicle in real time, so as to make corresponding strategy adjustments and executions.

[0090] For example, Figure 2As shown, assume that in a vehicle networking scenario, vehicle A is a local node vehicle and vehicle B is a third external node vehicle. The local driving status information of vehicle A shows that its speed is 75km / h, its position is on a certain highway section, and its direction is north. Vehicle A broadcasts its local driving status information to vehicle B through the PSSCH channel. After vehicle B receives the driving status information of vehicle A, it can understand the speed, position and direction of vehicle A in real time, so as to make corresponding strategy adjustments. For example, vehicle B can adjust its driving strategy according to the speed and position of vehicle A to ensure driving safety and efficiency. In this way, vehicles A and B can share information and work together to improve the overall performance of the vehicle networking system.

[0091] Step 208 : In response to the determination of the local response strategy, the local response strategy is sent to the third external node vehicle.

[0092] In some embodiments of the present application, after determining the local response strategy, the strategy will be sent to the third external node vehicle. The local response strategy is to respond to the action changes of the external node vehicle and ensure that the local node vehicle can adapt to these changes, thereby ensuring driving safety and efficiency. By sending the local response strategy to the third external node vehicle, information sharing and collaborative work can be achieved, thereby improving the overall performance of the Internet of Vehicles system.

[0093] For example, Figure 2 As shown, it is assumed that in a vehicle networking scenario, vehicle A is a local node vehicle and vehicle B is a third external node vehicle. Vehicle A determines its own local response strategy based on the driving status information and the second action strategy of vehicle B. The strategy may include decelerating to 65km / h and staying in the middle lane to avoid collision with vehicle B. After determining the local response strategy, vehicle A sends the strategy to vehicle B through the PSSCH channel. After receiving the local response strategy, vehicle B can understand vehicle A's action plan in real time and make corresponding strategy adjustments. For example, vehicle B can adjust its driving speed and position according to vehicle A's deceleration strategy to ensure driving safety. In this way, vehicles A and B can share information and work together to improve the overall performance of the vehicle networking system.

[0094] Step 209, sending local driving status information to the fourth foreign node vehicle.

[0095] In some embodiments of the present application, the local driving status information of the local node vehicle is sent to the fourth foreign node vehicle. The local driving status information includes data such as the speed, position, and direction of the vehicle. This information is broadcast and received through a physical channel (such as PSSCH) in the vehicle networking communication mode. The local driving status information is sent to ensure that the fourth foreign node vehicle can understand the driving status of the local node vehicle in real time, so as to make corresponding strategy adjustments and executions.

[0096] For example, Figure 2 As shown, assume that in a vehicle networking scenario, vehicle A is a local node vehicle and vehicle C is a fourth external node vehicle. The local driving status information of vehicle A shows that its speed is 75km / h, its position is on a certain highway section, and its direction is north. Vehicle A broadcasts its local driving status information to vehicle C through the PSSCH channel. After vehicle C receives the driving status information of vehicle A, it can understand the speed, position and direction of vehicle A in real time, so as to make corresponding strategy adjustments. For example, vehicle C can adjust its driving strategy according to the speed and position of vehicle A to ensure driving safety and efficiency. In this way, vehicles A and C can share information and work together to improve the overall performance of the vehicle networking system.

[0097] In summary, in the embodiment of the present application, by acquiring the first driving state information, and the second action strategy generated for the first driving state, and the second driving state information generated based on the second action strategy, the driving state of the surrounding vehicles is monitored in real time, ensuring the comprehensive perception of the external environment and improving the system's sensitivity to changes in the external environment; and then determining the corresponding local response strategy, taking into account the state information and action strategy of the external vehicle, formulating and executing the optimal local response strategy, ensuring the comprehensiveness and rationality of the decision, so as to respond to changes in the external environment in a timely manner and ensure the safety and flexibility of vehicle driving. Therefore, based on the method of the embodiment of the present application, by acquiring the driving state information and action strategy of the external node vehicle, the local response strategy can be dynamically adjusted and executed, effectively solving the problem of insufficient flexibility of vehicle safety decision-making in driving safety scenarios in the prior art.

[0098] refer to Figure 4 , which shows a dynamic pre-decision device 30 based on vehicle networking perception provided by an embodiment of the present application, applied to a local node vehicle, including:

[0099] The data acquisition module 301 is used to acquire the first driving state information of the first foreign node vehicle, and acquire the second action strategy of the second foreign node vehicle when the first driving state information meets the preset first driving state threshold; the second action strategy is used to: process the impact of the first driving state information on the driving environment of the second foreign node vehicle; the second action strategy is generated by the second foreign node vehicle according to the first driving state information;

[0100] The strategy generation module 302 is used to determine the local response strategy corresponding to the second action strategy according to the first driving state information and one or more information of the second action strategy, and obtain the second driving state information of the second foreign node vehicle; the local response strategy is used to: process the impact of the execution of the second action strategy on the driving environment of the local node vehicle;

[0101] The strategy execution module 303 is used to execute the local response strategy when the second driving state information meets the preset second driving state threshold.

[0102] Optionally, the policy generation module 302 includes:

[0103] An initial environment submodule, used to generate an initial environment prediction result of a driving environment according to the first driving state information;

[0104] The first environment submodule determines, based on the initial environment prediction result, a first environment prediction result of the driving environment generated by the second out-of-town node vehicle under the second action strategy;

[0105] The first strategy generation submodule is used to determine a local response strategy according to the first environment prediction result.

[0106] Optionally, the policy generation module 302 includes:

[0107] The second environment submodule is used to determine a second environment prediction result of the driving environment generated by the second foreign node vehicle under the second action strategy;

[0108] The second strategy generation submodule is used to determine the local response strategy according to the second environment prediction result.

[0109] Optionally, the second driving state information includes a plurality of second driving state sub-information, the second driving state threshold includes a second driving state sub-threshold corresponding to each second driving state sub-information, and the strategy execution module 303 includes:

[0110] A condition counting submodule, used to determine the number of conditions fulfilled for the second driving state sub-information that meets the corresponding second driving state sub-threshold;

[0111] The trigger execution submodule is used to execute the local response strategy when the number of conditions met is greater than the preset number threshold.

[0112] Optionally, the device 30 further includes:

[0113] A third status sending module, used for acquiring local driving status information of a local node vehicle, and sending the local driving status information to a third foreign node vehicle;

[0114] A strategy sending module is used to send the local response strategy to the third foreign node vehicle in response to the determination of the local response strategy.

[0115] Optionally, the device 30 further includes:

[0116] The fourth status sending module is used to send local driving status information to the fourth external node vehicle.

[0117] Optionally, the device 30 further includes:

[0118] An environment acquisition module, used to acquire driving environment information sent by at least one vehicle;

[0119] The first vehicle identification module is used to determine a vehicle whose driving environment information matches a preset first driving environment threshold as a first external node vehicle, and to determine a vehicle whose driving environment information matches a preset second driving environment threshold as a second external node vehicle, so that the local node vehicle establishes a vehicle group topology network with the first external node vehicle and the second external node vehicle.

[0120] Optionally, the device 30 further includes:

[0121] The second vehicle identification module is used to determine a vehicle whose driving environment information matches a preset third driving environment threshold as a third external node vehicle, and add the third external node vehicle to the vehicle group topology network, and / or determine a vehicle whose driving environment information matches a preset fourth driving environment threshold as a fourth external node vehicle, and add the fourth external node vehicle to the vehicle group topology network.

[0122] In summary, in the embodiment of the present application, by acquiring the first driving state information, and the second action strategy generated for the first driving state, and the second driving state information generated based on the second action strategy, the driving state of the surrounding vehicles is monitored in real time, ensuring the comprehensive perception of the external environment and improving the system's sensitivity to changes in the external environment; and then determining the corresponding local response strategy, taking into account the state information and action strategy of the external vehicle, formulating and executing the optimal local response strategy, ensuring the comprehensiveness and rationality of the decision, so as to respond to changes in the external environment in a timely manner and ensure the safety and flexibility of vehicle driving. Therefore, based on the method of the embodiment of the present application, by acquiring the driving state information and action strategy of the external node vehicle, the local response strategy can be dynamically adjusted and executed, effectively solving the problem of insufficient flexibility of vehicle safety decision-making in driving safety scenarios in the prior art.

[0123] Reference Figure 5 , the electronic device 500 may include one or more of the following components: a processing component 502 , a memory 504 , a power component 506 , a multimedia component 508 , an audio component 510 , an input / output (I / O) interface 512 , a sensor component 514 , and a communication component 516 .

[0124] The processing component 502 generally controls the overall operation of the electronic device 500, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 502 may include one or more processors 520 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 502 may include one or more modules to facilitate the interaction between the processing component 502 and other components. For example, the processing component 502 may include a multimedia module to facilitate the interaction between the multimedia component 508 and the processing component 502.

[0125] The memory 504 is used to store various types of data to support the operation of the electronic device 500. Examples of such data include instructions for any application or method operating on the electronic device 500, contact data, phone book data, messages, pictures, multimedia, etc. The memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0126] The power supply component 506 provides power to the various components of the electronic device 500. The power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 500.

[0127] The multimedia component 508 includes an interface that provides an output interface between the electronic device 500 and the user. In some embodiments, the interface may include a liquid crystal display (LCD) and a touch panel (TP). If the interface includes a touch panel, the interface may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 508 includes a front camera and / or a rear camera. When the electronic device 500 is in an operating mode, such as a shooting mode or a multimedia mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0128] The audio component 510 is used to output and / or input audio signals. For example, the audio component 510 includes a microphone (MIC), and when the electronic device 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is used to receive an external audio signal. The received audio signal can be further stored in the memory 504 or sent via the communication component 516. In some embodiments, the audio component 510 also includes a speaker for outputting audio signals.

[0129] The input / output I / O interface 512 provides an interface between the processing component 502 and the peripheral interface module, which may be a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0130] The sensor assembly 514 includes one or more sensors for providing various aspects of status assessment for the electronic device 500. For example, the sensor assembly 514 can detect the open / closed state of the electronic device 500, the relative positioning of components, such as the display and keypad of the electronic device 500, and the sensor assembly 514 can also detect the position change of the electronic device 500 or a component of the electronic device 500, the presence or absence of user contact with the electronic device 500, the orientation or acceleration / deceleration of the electronic device 500, and the temperature change of the electronic device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0131] The communication component 516 is used to facilitate wired or wireless communication between the electronic device 500 and other devices. The electronic device 500 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 516 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0132] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to implement the methods provided in the embodiments of the present application.

[0133] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions, and the instructions can be executed by a processor 520 of an electronic device 500 to perform the above method. For example, the non-transitory storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0134] Figure 6600 is a block diagram of an electronic device 600 according to another embodiment of the present invention. For example, the electronic device 600 may be provided as a server. Figure 6 , the electronic device 600 includes a processing component 622, which further includes one or more processors, and a memory resource represented by a memory 632, for storing instructions that can be executed by the processing component 622, such as an application. The application stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 622 is configured to execute instructions to perform the method provided in the embodiment of the present application.

[0135] The electronic device 600 may also include a power supply component 626 configured to perform power management of the electronic device 600, a wired or wireless network interface 650 configured to connect the electronic device 600 to a network, and an input / output (I / O) interface 658. The electronic device 600 may operate based on an operating system stored in the memory 632, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0136] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0137] It should be understood that the present application is not limited to the precise structures that have been 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 present application is limited only by the appended claims.

Claims

1. A dynamic pre-decision method based on Internet of Vehicles perception, characterized in that: Applied to local node vehicles, including: Acquire first driving state information of a first out-of-town node vehicle, and acquire a second action strategy of a second out-of-town node vehicle when the first driving state information satisfies a preset first driving state threshold; the second action strategy is used to: process the impact of the first driving state information on the driving environment of the second out-of-town node vehicle; the second action strategy is generated by the second out-of-town node vehicle according to the first driving state information; According to the first driving state information and one or more information of the second action strategy, a local response strategy corresponding to the second action strategy is determined, and the second driving state information of the second foreign node vehicle is obtained; the local response strategy is used to: process the impact of the execution of the second action strategy on the driving environment of the local node vehicle; When the second driving state information meets a preset second driving state threshold, the local response strategy is executed.

2. The method according to claim 1, characterized in that The determining, according to the first driving state information and one or more information of the second action strategy, a local response strategy corresponding to the second action strategy includes: generating an initial environment prediction result of the driving environment according to the first driving state information; Determine, based on the initial environment prediction result, a first environment prediction result of the driving environment generated by the second foreign node vehicle under the second action strategy; The local response strategy is determined according to the first environment prediction result.

3. The method according to claim 1, characterized in that The second driving state information includes a plurality of second driving state sub-information, the second driving state threshold includes a second driving state sub-threshold corresponding to each of the second driving state sub-information, and when the second driving state information satisfies a preset second driving state threshold, executing the local response strategy includes: Determine the number of conditions fulfilled for the second driving state sub-information that satisfies the corresponding second driving state sub-threshold; When the number of conditions met is greater than a preset threshold, the local response strategy is executed.

4. The method according to claim 1, characterized in that The determining, according to the first driving state information and one or more information of the second action strategy, a local response strategy corresponding to the second action strategy includes: Determine a second environment prediction result of the driving environment generated by the second foreign node vehicle under the second action strategy; The local response strategy is determined according to the second environment prediction result.

5. The method according to claim 1, characterized in that The method further comprises: Acquire local driving status information of the local node vehicle, and send the local driving status information to a third foreign node vehicle; In response to the determination of the local response strategy, the local response strategy is sent to the third foreign node vehicle.

6. The method according to claim 1, characterized in that The method further comprises: Acquiring driving environment information sent by at least one vehicle; The vehicle whose driving environment information matches the preset first driving environment threshold is determined as the first external node vehicle, and the vehicle whose driving environment information matches the preset second driving environment threshold is determined as the second external node vehicle, so that the local node vehicle and the first external node vehicle and the second external node vehicle establish a vehicle group topology network.

7. The method according to claim 6, characterized in that The method further comprises: The vehicle whose driving environment information matches a preset third driving environment threshold is determined as a third external node vehicle, and the third external node vehicle is added to the vehicle group topology network, and / or the vehicle whose driving environment information matches a preset fourth driving environment threshold is determined as a fourth external node vehicle, and the fourth external node vehicle is added to the vehicle group topology network.

8. A dynamic pre-decision device based on vehicle network perception, characterized in that: Applied to local node vehicles, including: A data acquisition module is used to acquire first driving state information of a first foreign node vehicle, and when the first driving state information meets a preset first driving state threshold, acquire a second action strategy of a second foreign node vehicle; the second action strategy is used to: process the impact of the first driving state information on the driving environment of the second foreign node vehicle; the second action strategy is generated by the second foreign node vehicle according to the first driving state information; A strategy generation module, used to determine a local response strategy corresponding to the second action strategy according to the first driving state information and one or more information of the second action strategy, and obtain the second driving state information of the second foreign node vehicle; the local response strategy is used to: process the impact of the execution of the second action strategy on the driving environment of the local node vehicle; A strategy execution module is used to execute the local response strategy when the second driving state information meets a preset second driving state threshold.

9. An electronic device, characterized in that: include: A processor, a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 7.

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