Vehicle automatic driving brake control method and device based on block chain intelligent learning

Abstract

The invention discloses a vehicle automatic driving braking control method and device based on block chain intelligent learning. The method comprises the steps that road condition information and current first vehicle parameter information are acquired; acquiring second vehicle road condition braking information matched with the road condition information in the block chain network; generating a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; the first vehicle executes a first vehicle braking control strategy and integrates the first vehicle braking control strategy and the first vehicle parameter information to obtain first vehicle road condition braking information; and uploading the first vehicle road condition braking information to a block chain network. A brake control strategy meeting the vehicle requirement is obtained by combining the characteristics of the vehicle and brake control strategies made by other vehicles under the same road condition; and the road condition braking information is fed back to the block chain network, so that the block chain network forms a closed-loop system for continuously optimizing the road condition braking information, and the braking effect of the automatic driving vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a vehicle autonomous driving braking control method and device based on blockchain intelligent learning. Background Art

[0002] At present, driverless cars mainly rely on driverless systems to achieve automatic driving of vehicles. Among them, the driverless braking system usually relies on sensors distributed around the vehicle body to obtain distance data from surrounding vehicles, pedestrians or objects to determine whether the vehicle needs to brake. However, in extremely complex traffic scenes and under different environmental factors, it is difficult to meet the requirements for vehicle safety and comfort by making braking judgments based on distance data. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a vehicle automatic driving braking control method and device based on blockchain intelligent learning to improve the braking effect of the automatic driving vehicle.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: A vehicle automatic driving braking control method based on blockchain intelligent learning, comprising: Obtaining road condition information and current first vehicle parameter information; Acquire, in the blockchain network, second vehicle road condition braking information that matches the road condition information; Generate a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information; The first vehicle road condition braking information is uploaded to the blockchain network.

[0005] In order to solve the above technical problems, another technical solution adopted by the present invention is: A vehicle automatic driving braking control device based on blockchain intelligent learning includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtaining road condition information and current first vehicle parameter information; Acquire, in the blockchain network, second vehicle road condition braking information that matches the road condition information; Generate a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information; The first vehicle road condition braking information is uploaded to the blockchain network.

[0006] The beneficial effects of the present invention are as follows: after acquiring road condition information, the road condition braking information of a second vehicle with similar road condition information is matched in the blockchain network, and in the process of generating a braking control strategy for the first vehicle, the characteristics of the first vehicle itself and the braking control strategies made by other vehicles under the same road conditions are combined, so as to obtain a braking control strategy that meets the needs of the first vehicle itself and improve the braking effect of its own vehicle; at the same time, the road condition braking information generated by the first vehicle itself is also fed back to the blockchain network, so that the blockchain network forms a closed-loop system that continuously optimizes the road condition braking information, thereby improving the braking effect of the autonomous driving vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a flowchart of the steps of a vehicle automatic driving braking control method based on blockchain intelligent learning in an embodiment of the present invention; Figure 2 This is another step flow chart of a vehicle automatic driving braking control method based on blockchain intelligent learning in an embodiment of the present invention; Figure 3 This is a structural diagram of a vehicle automatic driving braking control device based on blockchain intelligent learning in an embodiment of the present invention. DETAILED DESCRIPTION

[0008] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0009] Please refer to Figure 1 , a vehicle automatic driving braking control method based on blockchain intelligent learning, comprising: Obtaining road condition information and current first vehicle parameter information; Acquire, in the blockchain network, second vehicle road condition braking information that matches the road condition information; Generate a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information; The first vehicle road condition braking information is uploaded to the blockchain network.

[0010] From the above description, it can be seen that the beneficial effects of the present invention are: after obtaining road condition information, the road condition braking information of the second vehicle with similar road condition information is matched in the blockchain network, and in the process of generating the braking control strategy of the first vehicle, the characteristics of the first vehicle itself and the braking control strategies made by other vehicles under the same road conditions are combined, so as to obtain a braking control strategy that meets the needs of the first vehicle itself and improve the braking effect of its own vehicle; at the same time, the road condition braking information generated by the first vehicle itself is also fed back to the blockchain network, so that the blockchain network forms a closed-loop system that continuously optimizes the road condition braking information, thereby improving the braking effect of the autonomous driving vehicle.

[0011] Further, the second vehicle road condition braking information includes second vehicle parameter information and a second vehicle braking control strategy; The generating of the target braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information comprises: Determine whether the first vehicle parameter information matches the second vehicle parameter information; if not, adjust the second vehicle braking control strategy according to the first vehicle parameter information to obtain the first vehicle braking control strategy.

[0012] From the above description, it can be seen that when the first vehicle parameter information does not match the second vehicle parameter information, the braking control strategy of the first vehicle can be adjusted by adjusting the difference between the first vehicle parameter information and the second vehicle parameter information to obtain a braking control strategy that meets the needs of the first vehicle itself, thereby achieving that the braking control strategies of vehicles with different parameters can be converted to each other to meet the braking control needs of different vehicles.

[0013] Further, obtaining second vehicle road condition braking information matching the road condition information in the blockchain network according to the road condition information includes: Determine whether there is second vehicle road condition braking information matching the road condition information in the blockchain network; if not, analyze the road condition information and the first vehicle parameter information through an intelligent learning system to generate the first vehicle braking control strategy.

[0014] From the above description, it can be seen that when there is no second vehicle road condition braking information matching the road condition information in the blockchain network, the first vehicle can analyze the road condition information and the second vehicle parameter information based on its own intelligent learning system to generate a braking control strategy, so that when the first vehicle drives into a new road condition scenario, it can generate a braking control strategy based on the historical braking control strategy combined with the current road condition information and the first vehicle's own parameter information, thereby improving the braking effect of the autonomous driving vehicle in the new road condition scenario; at the same time, it can also provide data reference for subsequent vehicles entering the route.

[0015] Furthermore, after integrating the first vehicle braking control strategy and the first vehicle parameter information to obtain the first vehicle road condition braking information, the method further includes: The first vehicle road condition braking information is input into the intelligent learning system as a training set to optimize the intelligent learning system.

[0016] From the above description, it can be seen that by optimizing the intelligent learning system using the first vehicle's road condition braking information as a training set, the intelligent learning system can continuously learn and improve in a cycle, thereby improving the braking effect of the autonomous driving vehicle.

[0017] Furthermore, uploading the first vehicle road condition braking information to the blockchain network includes: The first vehicle road condition braking information is encrypted, and the encrypted first vehicle road condition braking information is uploaded to the blockchain network.

[0018] From the above description, it can be seen that by encrypting the road condition braking information of the first vehicle, the security of the information uploaded to the blockchain network is guaranteed, thereby improving data security.

[0019] Another embodiment of the present invention provides a vehicle automatic driving braking control device based on blockchain intelligent learning, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtaining road condition information and current first vehicle parameter information; Acquire, in the blockchain network, second vehicle road condition braking information that matches the road condition information; Generate a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information; The first vehicle road condition braking information is uploaded to the blockchain network.

[0020] From the above description, it can be seen that the beneficial effects of the present invention are: after obtaining road condition information, the road condition braking information of the second vehicle with similar road condition information is matched in the blockchain network, and in the process of generating the braking control strategy of the first vehicle, the characteristics of the first vehicle itself and the braking control strategies made by other vehicles under the same road conditions are combined, so as to obtain a braking control strategy that meets the needs of the first vehicle itself and improve the braking effect of its own vehicle; at the same time, the road condition braking information generated by the first vehicle itself is also fed back to the blockchain network, so that the blockchain network forms a closed-loop system that continuously optimizes the road condition braking information, thereby improving the braking effect of the autonomous driving vehicle.

[0021] Further, the second vehicle road condition braking information includes second vehicle parameter information and a second vehicle braking control strategy; The generating of the target braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information comprises: Determine whether the first vehicle parameter information matches the second vehicle parameter information; if not, adjust the second vehicle braking control strategy according to the first vehicle parameter information to obtain the first vehicle braking control strategy.

[0022] From the above description, it can be seen that when the first vehicle parameter information does not match the second vehicle parameter information, the braking control strategy of the first vehicle can be adjusted by adjusting the difference between the first vehicle parameter information and the second vehicle parameter information to obtain a braking control strategy that meets the needs of the first vehicle itself, thereby achieving that the braking control strategies of vehicles with different parameters can be converted to each other to meet the braking control needs of different vehicles.

[0023] Further, obtaining second vehicle road condition braking information matching the road condition information in the blockchain network according to the road condition information includes: Determine whether there is second vehicle road condition braking information matching the road condition information in the blockchain network; if not, analyze the road condition information and the first vehicle parameter information through an intelligent learning system to generate the first vehicle braking control strategy.

[0024] From the above description, it can be seen that when there is no second vehicle road condition braking information matching the road condition information in the blockchain network, the first vehicle can analyze the road condition information and the second vehicle parameter information based on its own intelligent learning system to generate a braking control strategy, so that when the first vehicle drives into a new road condition scenario, it can generate a braking control strategy based on the historical braking control strategy combined with the current road condition information and the first vehicle's own parameter information, thereby improving the braking effect of the autonomous driving vehicle in the new road condition scenario; at the same time, it can also provide data reference for subsequent vehicles entering the route.

[0025] Furthermore, after integrating the first vehicle braking control strategy and the first vehicle parameter information to obtain the first vehicle road condition braking information, the method further includes: The first vehicle road condition braking information is input into the intelligent learning system as a training set to optimize the intelligent learning system.

[0026] From the above description, it can be seen that by optimizing the intelligent learning system using the first vehicle's road condition braking information as a training set, the intelligent learning system can continuously learn and improve in a cycle, thereby improving the braking effect of the autonomous driving vehicle.

[0027] Furthermore, uploading the first vehicle road condition braking information to the blockchain network includes: The first vehicle road condition braking information is encrypted, and the encrypted first vehicle road condition braking information is uploaded to the blockchain network.

[0028] From the above description, it can be seen that by encrypting the road condition braking information of the first vehicle, the security of the information uploaded to the blockchain network is guaranteed, thereby improving data security.

[0029] The vehicle automatic driving braking control method and device based on blockchain intelligent learning provided by the present invention can be applied to the scene of vehicle automatic driving to improve the vehicle's control effect on braking. The following is an explanation through specific implementation methods: Embodiment 1 Please refer to Figure 1 as well as Figure 2 , a vehicle automatic driving braking control method based on blockchain intelligent learning, comprising: S1. Obtaining road condition information and current first vehicle parameter information; wherein the road condition information includes relevant information such as weather, road material, road braking coefficient and road conditions; the vehicle parameter information includes vehicle type, size, load and other vehicle-specific parameters.

[0030] S2. Obtain second vehicle road condition braking information matching the road condition information in the blockchain network, where the second vehicle road condition braking information includes second vehicle parameter information and a second vehicle braking control strategy. At the same time, determine whether there is second vehicle road condition braking information matching the road condition information in the blockchain network. If not, analyze the road condition information and the first vehicle parameter information through an intelligent learning system to generate a first vehicle braking control strategy.

[0031] Among them, when matching based on the blockchain network, priority matching can also be performed based on time; for example, it is determined whether there are vehicles with road usage similar to the characteristics of this vehicle in a similar time period, such as obtaining the braking data of the vehicle in front in real time through the blockchain as the second vehicle's road condition braking information; when matching, road condition information is matched based on relevant information such as weather, road material, road braking coefficient and road conditions.

[0032] S3. Generate a braking control strategy for the first vehicle based on the first vehicle parameter information and the second vehicle road condition braking information. Specifically: determine whether the first vehicle parameter information matches the second vehicle parameter information. If not, adjust the braking control strategy for the second vehicle based on the first vehicle parameter information to obtain the braking control strategy for the first vehicle. If yes, no adjustment is required due to similar characteristics. Related adjustment means that due to different vehicle characteristics, the braking coefficients of different vehicles will also change. Therefore, the intelligent driving system makes specific adjustments based on the difference in parameters between the two vehicles so that a braking control strategy that meets the needs of the first vehicle can be obtained.

[0033] S4. The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and the first vehicle parameter information to obtain the first vehicle road condition braking information; when executing the braking control strategy, the intelligent learning system analyzes the collected data to optimize the vehicle's braking control decision; and collects real-time road conditions, speed, distance and other data through vehicle sensors; at the same time, the first vehicle road condition braking information is input into the intelligent learning system as a training set to optimize the intelligent learning system. That is, after completing the braking control strategy, the results of executing the braking control strategy are analyzed, and the results are fed back to the braking control system to optimize the intelligent learning system. That is, after the vehicle obtains various types of road information in real time through the blockchain, it uses AI to analyze and judge the transmitted data to optimize the braking control.

[0034] S5. Upload the first vehicle's road condition braking information to the blockchain network. When uploading data to the blockchain network, the sensor data is uploaded to the blockchain node in an encrypted manner to ensure data security and non-tamperability. That is, the vehicle is not only a data acquirer, but also a data contributor.

[0035] As a data contributor, the vehicle uploads data through the blockchain platform based on its own braking conditions. Each vehicle provides valuable road friction coefficient and its braking conditions based on its own braking experience, providing reference data for other vehicles in the blockchain system.

[0036] As a data acquirer, the vehicle requests the braking data of the road ahead through the blockchain, and calculates the best braking strategy based on the characteristics of the vehicle itself; for example, when braking, the vehicle calculates the braking coefficient based on its own data and braking distance. By forming a data loop through the blockchain, the vehicle can not only obtain data from other vehicles, but also contribute its own braking data to other vehicles, forming a closed-loop optimization system.

[0037] Embodiment 2 Please refer to Figure 3, a vehicle automatic driving braking control device based on blockchain intelligent learning, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program: S1. Obtain road condition information and current first vehicle parameter information.

[0038] S2. Obtaining second vehicle road condition braking information matching the road condition information in the blockchain network, the second vehicle road condition braking information including second vehicle parameter information and second vehicle braking control strategy. At the same time, it is also determined whether there is second vehicle road condition braking information matching the road condition information in the blockchain network. If not, the road condition information and the first vehicle parameter information are analyzed by the intelligent learning system to generate the first vehicle braking control strategy.

[0039] S3. Generate a first vehicle braking control strategy based on the first vehicle parameter information and the second vehicle road condition braking information. Specifically: determine whether the first vehicle parameter information matches the second vehicle parameter information. If not, adjust the second vehicle braking control strategy based on the first vehicle parameter information to obtain the first vehicle braking control strategy.

[0040] S4. The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and the first vehicle parameter information to obtain the first vehicle road condition braking information; after completing the data integration, the first vehicle road condition braking information is further input into the intelligent learning system as a training set to optimize the intelligent learning system.

[0041] S5. Upload the first vehicle's road condition braking information to the blockchain network. Specifically, encrypt the first vehicle's road condition braking information, and upload the encrypted first vehicle's road condition braking information to the blockchain network. Encryption of the uploaded data is achieved.

[0042] In summary, the present invention provides a vehicle automatic driving braking control method and device based on blockchain intelligent learning. After acquiring road condition information, the road condition braking information of a second vehicle with similar road condition information is matched in the blockchain network. In the process of generating a braking control strategy for the first vehicle, the first vehicle's own characteristics and the braking control strategies made by other vehicles under the same road conditions are combined to obtain a braking control strategy that meets the needs of the first vehicle itself, thereby improving the braking effect of the vehicle itself. At the same time, the road condition braking information generated by the first vehicle itself is fed back to the blockchain network, so that the blockchain network forms a closed-loop system that continuously optimizes the road condition braking information, thereby improving the braking effect of the automatic driving vehicle.

[0043] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A vehicle automatic driving braking control method based on blockchain intelligent learning, characterized in that: include: Obtaining road condition information and current first vehicle parameter information; Acquire, in the blockchain network, second vehicle road condition braking information that matches the road condition information; Generate a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information; The first vehicle road condition braking information is uploaded to the blockchain network.

2. According to claim 1, a vehicle automatic driving braking control method based on blockchain intelligent learning is characterized in that: The second vehicle road condition braking information includes second vehicle parameter information and a second vehicle braking control strategy; The generating of the target braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information comprises: Determine whether the first vehicle parameter information matches the second vehicle parameter information; if not, adjust the second vehicle braking control strategy according to the first vehicle parameter information to obtain the first vehicle braking control strategy.

3. According to claim 1, a vehicle automatic driving braking control method based on blockchain intelligent learning is characterized in that: The acquiring, in the blockchain network according to the road condition information, second vehicle road condition braking information matching the road condition information comprises: Determine whether there is second vehicle road condition braking information matching the road condition information in the blockchain network; if not, analyze the road condition information and the first vehicle parameter information through an intelligent learning system to generate the first vehicle braking control strategy.

4. According to claim 3, a vehicle automatic driving braking control method based on blockchain intelligent learning is characterized in that: After the first vehicle braking control strategy and the first vehicle parameter information are integrated to obtain the first vehicle road condition braking information, the following further comprises: The first vehicle road condition braking information is input into the intelligent learning system as a training set to optimize the intelligent learning system.

5. According to claim 1, a vehicle automatic driving braking control method based on blockchain intelligent learning is characterized in that: The uploading of the first vehicle road condition braking information to the blockchain network includes: The first vehicle road condition braking information is encrypted, and the encrypted first vehicle road condition braking information is uploaded to the blockchain network.

6. A vehicle automatic driving braking control device based on blockchain intelligent learning, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: Obtaining road condition information and current first vehicle parameter information; Acquire, in the blockchain network, second vehicle road condition braking information that matches the road condition information; Generate a first vehicle braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information; The first vehicle executes the first vehicle braking control strategy, and integrates the first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information; The first vehicle road condition braking information is uploaded to the blockchain network.

7. A vehicle automatic driving braking control device based on blockchain intelligent learning according to claim 6, characterized in that: The second vehicle road condition braking information includes second vehicle parameter information and a second vehicle braking control strategy; The generating of the target braking control strategy according to the first vehicle parameter information and the second vehicle road condition braking information comprises: Determine whether the first vehicle parameter information matches the second vehicle parameter information; if not, adjust the second vehicle braking control strategy according to the first vehicle parameter information to obtain the first vehicle braking control strategy.

8. The vehicle automatic driving braking control device based on blockchain intelligent learning according to claim 6 is characterized in that: The acquiring, in the blockchain network according to the road condition information, second vehicle road condition braking information matching the road condition information comprises: Determine whether there is second vehicle road condition braking information matching the road condition information in the blockchain network; if not, analyze the road condition information and the first vehicle parameter information through an intelligent learning system to generate the first vehicle braking control strategy.

9. The vehicle automatic driving braking control device based on blockchain intelligent learning according to claim 8 is characterized in that: After the first vehicle braking control strategy and the first vehicle parameter information are integrated to obtain the first vehicle road condition braking information, the following further comprises: The first vehicle road condition braking information is input into the intelligent learning system as a training set to optimize the intelligent learning system.

10. The vehicle automatic driving braking control device based on blockchain intelligent learning according to claim 6 is characterized in that: The uploading of the first vehicle road condition braking information to the blockchain network includes: The first vehicle road condition braking information is encrypted, and the encrypted first vehicle road condition braking information is uploaded to the blockchain network.

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