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

Abstract

The application discloses a kind of vehicle automatic driving brake control method and device based on block chain intelligent learning, comprising: obtaining road condition information and current first vehicle parameter information;In the block chain network, obtain the second vehicle road condition braking information matched with road condition information;According to first vehicle parameter information and second vehicle road condition braking information, generate first vehicle braking control strategy;First vehicle executes first vehicle braking control strategy, and integrates first vehicle braking control strategy and first vehicle parameter information to obtain first vehicle road condition braking information;First vehicle road condition braking information is uploaded to block chain network.Through the braking control strategy made by combining the characteristics of vehicle itself and other vehicles under the same road condition, the braking control strategy that meets the needs of vehicle is obtained;Road condition braking information is fed back to block chain network, so that block chain network forms a closed loop system for continuously optimizing road condition braking information, to improve the braking effect of automatic driving vehicle.

Description

Technical Field

[0001] This 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 Technology

[0002] Currently, self-driving cars primarily rely on autonomous driving systems to achieve automatic driving. The braking system in these systems typically uses sensors distributed around the vehicle to acquire distance data from surrounding vehicles, pedestrians, or objects to determine whether braking is necessary. However, in extremely complex traffic scenarios and under varying environmental factors, relying solely on distance data to make braking decisions is insufficient to meet the requirements for vehicle safety and comfort. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for braking control of autonomous vehicles based on blockchain intelligent learning, so as to improve the braking effect of autonomous vehicles.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A vehicle autonomous driving braking control method based on blockchain intelligent learning includes:

[0006] Obtain road condition information and current first vehicle parameter information;

[0007] Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network;

[0008] A first vehicle braking control strategy is generated based on the first vehicle parameter information and the second vehicle road condition braking information.

[0009] 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.

[0010] The road condition braking information of the first vehicle is uploaded to the blockchain network.

[0011] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0012] A vehicle autonomous 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, it performs the following steps:

[0013] Obtain road condition information and current first vehicle parameter information;

[0014] Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network;

[0015] A first vehicle braking control strategy is generated based on the first vehicle parameter information and the second vehicle road condition braking information.

[0016] 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.

[0017] The road condition braking information of the first vehicle is uploaded to the blockchain network.

[0018] The beneficial effects of this invention are as follows: After acquiring road condition information, the invention matches the road condition braking information of a second vehicle with similar road condition information in the blockchain network. During the generation of the braking control strategy for the first vehicle, it combines the characteristics of the first vehicle itself with the braking control strategies of other vehicles under the same road conditions to obtain a braking control strategy that meets the needs of the first vehicle, thereby improving the braking effect of the vehicle. At the same time, the invention also feeds back the road condition braking information generated by the first vehicle to the blockchain network, so that the blockchain network forms a closed-loop system for continuously optimizing road condition braking information, thereby improving the braking effect of autonomous vehicles. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of a vehicle autonomous driving braking control method based on blockchain intelligent learning in an embodiment of the present invention.

[0020] Figure 2 This is a flowchart of another step in a vehicle autonomous driving braking control method based on blockchain intelligent learning, as described in an embodiment of the present invention.

[0021] Figure 3 This is a structural diagram of a vehicle autonomous driving braking control device based on blockchain intelligent learning, as described in an embodiment of the present invention. Detailed Implementation

[0022] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0023] Please refer to Figure 1 A method for braking control of autonomous driving vehicles based on blockchain intelligent learning, comprising:

[0024] Obtain road condition information and current first vehicle parameter information;

[0025] Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network;

[0026] A first vehicle braking control strategy is generated based on the first vehicle parameter information and the second vehicle road condition braking information.

[0027] 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.

[0028] The road condition braking information of the first vehicle is uploaded to the blockchain network.

[0029] As described above, the beneficial effects of this invention are as follows: After acquiring road condition information, the invention matches the road condition braking information of a second vehicle with similar road condition information in the blockchain network. During the generation of the braking control strategy for the first vehicle, it combines the characteristics of the first vehicle itself with the braking control strategies of other vehicles under the same road conditions to obtain a braking control strategy that meets the needs of the first vehicle, thereby improving its braking performance. Simultaneously, the invention also feeds back the road condition braking information generated by the first vehicle to the blockchain network, creating a closed-loop system that continuously optimizes road condition braking information, thereby improving the braking performance of autonomous vehicles.

[0030] Furthermore, the second vehicle road condition braking information includes second vehicle parameter information and second vehicle braking control strategy;

[0031] The step of generating a target braking control strategy based on the first vehicle parameter information and the second vehicle road condition braking information includes:

[0032] 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.

[0033] As described above, when the first vehicle parameter information and the second vehicle parameter information do not match, the braking control strategy of the first vehicle can be adjusted by the difference between the first vehicle parameter information and the second vehicle parameter information, so as to obtain a braking control strategy that meets the needs of the first vehicle itself, and realize 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.

[0034] Furthermore, obtaining second vehicle road condition braking information matching the road condition information in the blockchain network based on the road condition information includes:

[0035] Determine whether there is second vehicle road condition braking information in the blockchain network that matches the road condition information. 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.

[0036] As described above, when there is no matching road condition braking information for a second vehicle in the blockchain network, the first vehicle can analyze the road condition information and the second vehicle's parameter information based on its own intelligent learning system to generate a braking control strategy. This allows the first vehicle to generate a braking control strategy based on historical braking control strategies combined with current road condition information and its own parameter information when driving in a new road condition scenario, thereby improving the braking effect of the autonomous vehicle in the new road condition scenario. At the same time, it can also provide data reference for subsequent vehicles entering the route.

[0037] 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:

[0038] The first vehicle's road condition braking information is used as a training set and input into the intelligent learning system to optimize the intelligent learning system.

[0039] As described above, by using the braking information of the first vehicle as a training set to optimize the intelligent learning system, the intelligent learning system can continuously learn and improve, thereby enhancing the braking performance of autonomous vehicles.

[0040] Furthermore, uploading the first vehicle's road condition braking information to the blockchain network includes:

[0041] The road condition braking information of the first vehicle is encrypted, and the encrypted road condition braking information of the first vehicle is uploaded to the blockchain network.

[0042] As described above, encrypting the road condition braking information of the first vehicle ensures the security of the information uploaded to the blockchain network and improves data security.

[0043] Another embodiment of the present invention provides a vehicle autonomous 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. When the processor executes the computer program, it performs the following steps:

[0044] Obtain road condition information and current first vehicle parameter information;

[0045] Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network;

[0046] A first vehicle braking control strategy is generated based on the first vehicle parameter information and the second vehicle road condition braking information.

[0047] 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.

[0048] The road condition braking information of the first vehicle is uploaded to the blockchain network.

[0049] As described above, the beneficial effects of this invention are as follows: After acquiring road condition information, the invention matches the road condition braking information of a second vehicle with similar road condition information in the blockchain network. During the generation of the braking control strategy for the first vehicle, it combines the characteristics of the first vehicle itself with the braking control strategies of other vehicles under the same road conditions to obtain a braking control strategy that meets the needs of the first vehicle, thereby improving its braking performance. Simultaneously, the invention also feeds back the road condition braking information generated by the first vehicle to the blockchain network, creating a closed-loop system that continuously optimizes road condition braking information, thereby improving the braking performance of autonomous vehicles.

[0050] Furthermore, the second vehicle road condition braking information includes second vehicle parameter information and second vehicle braking control strategy;

[0051] The step of generating a target braking control strategy based on the first vehicle parameter information and the second vehicle road condition braking information includes:

[0052] 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.

[0053] As described above, when the first vehicle parameter information and the second vehicle parameter information do not match, the braking control strategy of the first vehicle can be adjusted by the difference between the first vehicle parameter information and the second vehicle parameter information, so as to obtain a braking control strategy that meets the needs of the first vehicle itself, and realize 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.

[0054] Furthermore, obtaining second vehicle road condition braking information matching the road condition information in the blockchain network based on the road condition information includes:

[0055] Determine whether there is second vehicle road condition braking information in the blockchain network that matches the road condition information. 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.

[0056] As described above, when there is no matching road condition braking information for a second vehicle in the blockchain network, the first vehicle can analyze the road condition information and the second vehicle's parameter information based on its own intelligent learning system to generate a braking control strategy. This allows the first vehicle to generate a braking control strategy based on historical braking control strategies combined with current road condition information and its own parameter information when driving in a new road condition scenario, thereby improving the braking effect of the autonomous vehicle in the new road condition scenario. At the same time, it can also provide data reference for subsequent vehicles entering the route.

[0057] 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:

[0058] The first vehicle's road condition braking information is used as a training set and input into the intelligent learning system to optimize the intelligent learning system.

[0059] As described above, by using the braking information of the first vehicle as a training set to optimize the intelligent learning system, the intelligent learning system can continuously learn and improve, thereby enhancing the braking performance of autonomous vehicles.

[0060] Furthermore, uploading the first vehicle's road condition braking information to the blockchain network includes:

[0061] The road condition braking information of the first vehicle is encrypted, and the encrypted road condition braking information of the first vehicle is uploaded to the blockchain network.

[0062] As described above, encrypting the road condition braking information of the first vehicle ensures the security of the information uploaded to the blockchain network and improves data security.

[0063] The vehicle autonomous driving braking control method and device based on blockchain intelligent learning provided by this invention can be applied to vehicle autonomous driving scenarios, improving the vehicle's braking control effect. The following is a detailed description of the implementation methods:

[0064] Example 1

[0065] Please refer to Figure 1 as well as Figure 2 A method for braking control of autonomous driving vehicles based on blockchain intelligent learning, comprising:

[0066] S1. Obtain road condition information and current first vehicle parameter information; among which, road condition information includes weather, road material, road braking coefficient and road conditions and other related information; vehicle parameter information includes vehicle type, size, load and other inherent vehicle parameters.

[0067] S2. Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network. The second vehicle road condition braking information includes second vehicle parameter information and second vehicle braking control strategy. At the same time, determine whether there is second vehicle road condition braking information that matches the road condition information in the blockchain network. If not, analyze the road condition information and first vehicle parameter information through the intelligent learning system to generate first vehicle braking control strategy.

[0068] Among them, when matching based on the blockchain network, priority matching can also be based on time; for example, it can determine whether there are vehicles with similar characteristics to this vehicle in the same time period, such as obtaining the braking data of the vehicle in front in real time through the blockchain as the road condition braking information of the second vehicle; when matching, road condition information matching is performed based on relevant information such as weather, road material, road braking coefficient and road conditions.

[0069] 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 and the second vehicle parameter information match. If not, adjust the second vehicle braking control strategy according to the first vehicle parameter information to obtain the first vehicle braking control strategy; if so, no adjustment is needed due to similar characteristics. Related adjustments refer to the fact that due to differences in vehicle characteristics, the braking coefficients of different vehicles will also change. Therefore, the intelligent driving system makes specific adjustments based on the differences in parameters between the two vehicles to obtain a braking control strategy that meets the needs of the first vehicle itself.

[0070] S4. The first vehicle executes its braking control strategy and integrates the braking control strategy and vehicle parameter information to obtain the vehicle's road condition braking information. During the execution of the braking control strategy, the intelligent learning system analyzes the collected data to optimize the vehicle's braking control decisions. It also collects real-time road conditions, speed, distance, and other data through vehicle sensors. Simultaneously, the vehicle's road condition braking information is used as a training set and input into the intelligent learning system to optimize it. In other words, after completing the braking control strategy, the system analyzes the results of its execution and feeds the results back to the braking control system to optimize the intelligent learning system. Essentially, after the vehicle obtains various road information in real time through blockchain, it uses AI to analyze and judge the transmitted data to optimize braking control.

[0071] 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 immutability. That is, this vehicle is not only a data acquirer but also a data contributor.

[0072] As data contributors, vehicles upload data to the blockchain platform based on their braking performance. Each vehicle provides valuable data on its road friction coefficient and braking status based on its own braking experience, offering reference data for other vehicles in the blockchain system.

[0073] As a data acquirer, a vehicle requests braking data from the road ahead via the blockchain and calculates the optimal braking strategy based on its own vehicle characteristics; for example, when braking, the vehicle combines its own data and braking distance to derive the braking coefficient. Through the blockchain, a data loop is formed, allowing vehicles not only to acquire data from other vehicles but also to contribute their own braking data to other vehicles, creating a closed-loop optimization system.

[0074] Example 2

[0075] Please refer to Figure 3 A vehicle autonomous 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, it performs the following steps:

[0076] S1. Obtain road condition information and current first vehicle parameter information.

[0077] S2. Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network. The second vehicle road condition braking information includes second vehicle parameter information and second vehicle braking control strategy. At the same time, determine whether there is second vehicle road condition braking information that matches the road condition information in the blockchain network. If not, analyze the road condition information and first vehicle parameter information through an intelligent learning system to generate a first vehicle braking control strategy.

[0078] 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 and the second vehicle parameter information match. If not, adjust the second vehicle braking control strategy based on the first vehicle parameter information to obtain the first vehicle braking control strategy.

[0079] 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 the data integration is completed, 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.

[0080] 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; thus achieving encryption of the uploaded data.

[0081] In summary, the present invention provides a vehicle autonomous driving braking control method and device based on blockchain intelligent learning. After acquiring road condition information, it matches the road condition braking information of a second vehicle with similar road condition information in the blockchain network. During the generation of the braking control strategy for the first vehicle, it combines the characteristics of the first vehicle itself with the braking control strategies of other vehicles under the same road conditions to obtain a braking control strategy that meets the needs of the first vehicle, thereby improving the braking effect of the vehicle. At the same time, it also feeds back the road condition braking information generated by the first vehicle itself to the blockchain network, so that the blockchain network forms a closed-loop system for continuously optimizing road condition braking information, thereby improving the braking effect of autonomous vehicles.

[0082] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention's specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A vehicle autonomous driving braking control method based on blockchain intelligent learning, characterized in that, include: Obtain road condition information and current first vehicle parameter information; Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network; A first vehicle braking control strategy is generated based on 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 the first vehicle parameter information to obtain the first vehicle road condition braking information. The road condition braking information of the first vehicle is uploaded to the blockchain network.

2. The vehicle autonomous driving braking control method based on blockchain intelligent learning according to claim 1, characterized in that, The second vehicle road condition braking information includes second vehicle parameter information and second vehicle braking control strategy; The step of generating a target braking control strategy based on the first vehicle parameter information and the second vehicle road condition braking information includes: 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. The vehicle autonomous driving braking control method based on blockchain intelligent learning according to claim 1, characterized in that, The step of obtaining second vehicle road condition braking information that matches the road condition information from the blockchain network based on the road condition information includes: Determine whether there is second vehicle road condition braking information in the blockchain network that matches the road condition information. 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. The vehicle autonomous driving braking control method based on blockchain intelligent learning according to claim 3, characterized in that, 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's road condition braking information is used as a training set and input into the intelligent learning system to optimize the intelligent learning system.

5. The vehicle autonomous driving braking control method based on blockchain intelligent learning according to claim 1, characterized in that, Uploading the first vehicle's road condition braking information to the blockchain network includes: The road condition braking information of the first vehicle is encrypted, and the encrypted road condition braking information of the first vehicle is uploaded to the blockchain network.

6. A vehicle autonomous 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, it performs the following steps: Obtain road condition information and current first vehicle parameter information; Obtain second vehicle road condition braking information that matches the road condition information from the blockchain network; A first vehicle braking control strategy is generated based on 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 the first vehicle parameter information to obtain the first vehicle road condition braking information. The road condition braking information of the first vehicle is uploaded to the blockchain network.

7. A vehicle autonomous 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 second vehicle braking control strategy; The step of generating a target braking control strategy based on the first vehicle parameter information and the second vehicle road condition braking information includes: 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. A vehicle autonomous driving braking control device based on blockchain intelligent learning according to claim 6, characterized in that, The step of obtaining second vehicle road condition braking information that matches the road condition information from the blockchain network based on the road condition information includes: Determine whether there is second vehicle road condition braking information in the blockchain network that matches the road condition information. 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. A vehicle autonomous driving braking control device based on blockchain intelligent learning according to claim 8, characterized in that, 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's road condition braking information is used as a training set and input into the intelligent learning system to optimize the intelligent learning system.

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

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