Vehicle active braking method and system based on cloud platform

Abstract

The invention specifically discloses a vehicle active braking method and system based on a cloud platform, and the method comprises the steps: enabling a vehicle to upload the road data collected by a vehicle end to the cloud platform in real time, and enabling the cloud platform to carry out the data processing according to the road data uploaded by a plurality of vehicles, so as to generate a high-confidence road adhesion coefficient sharing system; the cloud platform estimates the road adhesion coefficient of the corresponding road section in real time through the road adhesion system data sharing system, and issues the road adhesion coefficient to the vehicle driving on the related road section in real time; and in the automatic driving process of the vehicle, when it is detected that the dangerous target needs to be braked actively, braking is conducted according to the road adhesion coefficient sent by the cloud platform. The safety and real-time performance of active braking of the automatic driving vehicle can be improved, and the accuracy of automatic emergency braking is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle braking control, and more specifically, to a vehicle active braking method and system based on a cloud platform. Background Art

[0002] At present, active braking of vehicles mainly relies on sensors such as cameras, millimeter-wave radars, and wheel speed meters on the vehicle side and related algorithms to estimate the road adhesion coefficient and slip rate in real time. Through these sensors, the AEB system can obtain key data such as road environment, vehicle speed, and wheel speed, and then estimate the current road adhesion coefficient and brake as needed. However, existing technologies have several key limitations: (1) Insufficient calculation speed and real-time performance: In some extreme braking situations (for example, sudden obstacles or slippery roads), the AEB system needs to respond in a very short time. However, the computing resources on the vehicle side are limited, and it is difficult to accurately estimate the road adhesion coefficient within milliseconds. This time limit may cause the AEB system to be unable to adjust the braking force in time and make the best braking decision in the shortest time. (2) Limited sensor accuracy: The sensors on the vehicle side may be interfered with under different environmental conditions (such as heavy rain, haze, and nighttime), resulting in inaccurate data, which in turn affects the estimation accuracy of the adhesion coefficient. In addition, the sensor data of a single vehicle can only reflect local road conditions, and it is difficult to fully and accurately grasp the road conditions of the entire road section. (3) Limited data processing capability: Existing AEB systems rely on on-board computing units for real-time data processing. Due to hardware performance limitations, their processing speed and data processing capabilities are limited. This limitation restricts the system's decision-making ability during emergency braking, increasing the risk of accidents. Therefore, it is of great significance to improve the safety and real-time performance of vehicle active braking. Summary of the invention

[0003] The present invention provides a vehicle active braking method and system based on a cloud platform, which solves the problem of insufficient reaction speed and estimation accuracy when existing vehicles perform active braking due to the hardware performance limitations of the on-board equipment. It can improve the safety and real-time performance of active braking of autonomous driving vehicles and increase the accuracy of automatic emergency braking.

[0004] To achieve the above objectives, the present invention provides the following technical solutions:

[0005] A vehicle active braking method based on a cloud platform, comprising:

[0006] The vehicle uploads the road data collected by the vehicle to the cloud platform in real time. The cloud platform processes the road data uploaded by multiple vehicles to generate a high-confidence road adhesion coefficient sharing system.

[0007] The cloud platform estimates the road adhesion coefficient of the corresponding road section in real time through the road adhesion system data sharing system, and sends it to the vehicles traveling on the relevant road section in real time;

[0008] During the vehicle's autonomous driving process, when a dangerous target is detected and active braking is required, braking is performed according to the road adhesion coefficient sent by the cloud platform.

[0009] Preferably, it also includes:

[0010] The cloud platform also estimates the braking force based on the road adhesion coefficient of the corresponding road section, and sends it to the vehicles traveling on the relevant road section, so that the vehicles can brake according to the road adhesion coefficient and the corresponding braking force.

[0011] Preferably, it also includes:

[0012] The driving state of the vehicle itself is obtained, and the slip distance of the vehicle is estimated according to the road adhesion coefficient corresponding to the current road section and the driving state, so as to control the braking distance of the vehicle.

[0013] Preferably, the vehicle uploads the road data collected by the vehicle to the cloud platform in real time, including:

[0014] The road lane information, surrounding vehicle information, wheel speed and vehicle position information collected by the vehicle are combined with the vehicle's daily driving and braking data and uploaded to the cloud platform.

[0015] Preferably, the cloud platform performs data processing based on the road surface data uploaded by multiple vehicles, including:

[0016] The road surface data is filtered and weighted through data processing algorithms.

[0017] Preferably, the real-time transmission to vehicles traveling on the relevant road section includes:

[0018] Through the vehicle location information uploaded by the vehicle, the road section is matched on the cloud platform to obtain the road adhesion coefficient of the corresponding road section, which is then sent to the vehicle end for use.

[0019] The present invention also provides a vehicle active braking system based on a cloud platform, using the above-mentioned vehicle active braking method, comprising: a domain controller, a TBOX, a camera and a laser radar;

[0020] The domain controller is respectively connected to the TBOX, the camera and the laser radar signal, and the domain controller collects road surface data of the vehicle through the camera and the laser radar;

[0021] The TBOX is connected to the cloud platform for communication, and the domain controller uploads the road surface data to the cloud platform through the TBOX;

[0022] The cloud platform processes the road surface data uploaded by multiple vehicles to generate a high-confidence road surface adhesion coefficient information system to estimate the road surface adhesion coefficient of the corresponding road section in real time, and sends it to the vehicle end in real time to facilitate active braking of the vehicle.

[0023] Preferably, it also includes: a wheel speed meter;

[0024] The wheel speed meter is connected to the domain controller signal, and the wheel speed meter is used to detect the wheel speed of the vehicle;

[0025] The domain controller uploads the wheel speed to the cloud platform through the TBOX, and the cloud platform estimates the sliding distance of the vehicle during braking according to the road adhesion coefficient and the wheel speed.

[0026] Preferably, it also includes: an AEB control module;

[0027] The AEB control module is connected to the domain controller signal, and the AEB control module controls the vehicle to perform active braking according to the received road adhesion coefficient and the sliding distance.

[0028] Preferably, it also includes: a navigation and positioning device;

[0029] The navigation and positioning device is connected to the domain controller signal for obtaining vehicle position information in real time;

[0030] The domain controller uploads the vehicle position information to the cloud platform through the TBOX, and the cloud platform matches the vehicle with the road section according to the vehicle position information to send the road adhesion coefficient of the corresponding road section.

[0031] The present invention provides a vehicle active braking method and system based on a cloud platform, which uploads road data collected by the vehicle to the cloud platform, and the cloud platform estimates the corresponding road adhesion coefficient in real time and sends it to the vehicle to perform braking control. This solves the problem of insufficient reaction speed and estimation accuracy when the existing vehicle performs active braking due to the hardware performance limitations of the in-vehicle equipment, improves the safety and real-time performance of active braking of autonomous driving vehicles, and increases the accuracy of automatic emergency braking. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below.

[0033] Figure 1 It is a schematic diagram of a vehicle active braking method based on a cloud platform provided by the present invention.

[0034] Figure 2It is a schematic diagram of a vehicle-cloud collaborative road adhesion coefficient sharing system provided in an embodiment of the present invention.

[0035] Figure 3 It is a schematic diagram of a vehicle active braking system based on a cloud platform provided by the present invention. DETAILED DESCRIPTION

[0036] In order to enable persons skilled in the art to better understand the solutions of the embodiments of the present invention, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and implementation modes.

[0037] In view of the problems of insufficient reaction speed and estimation accuracy during active braking of current autonomous driving vehicles, the present invention provides a vehicle active braking method and system based on a cloud platform, which solves the problems of insufficient reaction speed and estimation accuracy during active braking of existing vehicles due to the hardware performance limitations of on-board equipment, and can improve the safety and real-time performance of active braking of autonomous driving vehicles and increase the accuracy of automatic emergency braking.

[0038] like Figure 1 and Figure 2 As shown, a vehicle active braking method based on a cloud platform includes:

[0039] S1: The vehicle uploads the road data collected by the vehicle to the cloud platform in real time. The cloud platform processes the road data uploaded by multiple vehicles to generate a high-confidence road adhesion coefficient sharing system.

[0040] S2: The cloud platform estimates the road adhesion coefficient of the corresponding road section in real time through the road adhesion system data sharing system, and sends it to the vehicles traveling on the relevant road section in real time.

[0041] S3: During the process of autonomous driving, when a dangerous target is detected and active braking is required, braking is performed according to the road adhesion coefficient sent by the cloud platform.

[0042] Specifically, the vehicle-side data collection module collects the road data of the vehicle, and combines the data of the vehicle's daily driving and braking, and uploads it to the cloud platform through TBOX. The cloud platform receives road data from multiple vehicles to generate a high-confidence road adhesion coefficient information system, and sends these data to the vehicles traveling on the relevant road sections in real time. When the vehicle needs to brake during automatic driving, the road adhesion coefficient can be directly called. Continuously collect and analyze data during the daily driving and braking of the vehicle, dynamically adjust the parameters of the estimation model, and optimize the estimated road adhesion coefficient. By mapping the positioning data to the corresponding road section, and processing the data using data fusion technology, the road section is matched in the cloud, the road adhesion coefficient information of the road section is obtained, and then the data is sent to the vehicles traveling on the road section. When these vehicles need to perform automatic emergency braking, they can use the high-precision data provided by the cloud platform to make quick decisions and adjust the braking force. This method combines vehicle-side data collection and cloud-based data processing. By sharing and integrating road data estimated by multiple vehicles, it can solve the problems of insufficient reaction speed and estimation accuracy in extreme braking conditions, and improve the decision-making speed and accuracy of the AEB system under various road conditions.

[0043] The method also includes: the cloud platform also estimates the braking force based on the road adhesion coefficient of the corresponding road section, and sends it to the vehicle traveling on the relevant road section, so that the vehicle brakes according to the road adhesion coefficient and the corresponding braking force.

[0044] In actual applications, the cloud platform also calculates the braking force required for braking within the set braking distance based on the vehicle speed and road adhesion coefficient, and the braking force can be calculated using the acceleration formula.

[0045] The method also includes: acquiring the driving state of the vehicle itself, and estimating the slip distance of the vehicle according to the road adhesion coefficient corresponding to the current road section and the driving state, so as to control the braking distance of the vehicle.

[0046] Furthermore, the vehicle uploads the road data collected by the vehicle to the cloud platform in real time, including:

[0047] The road lane information, surrounding vehicle information, wheel speed and vehicle position information collected by the vehicle are combined with the vehicle's daily driving and braking data and uploaded to the cloud platform.

[0048] Furthermore, the cloud platform performs data processing based on the road surface data uploaded by multiple vehicles, including:

[0049] The road surface data is filtered and weighted through data processing algorithms.

[0050] In practical applications, the data processing algorithm can use filtering algorithms (such as Kalman filtering) to remove outliers, use weighting algorithms to increase the weight of reliable data to increase the confidence of the data, or directly use the average method, that is, sum up the results of all vehicles and take the average. In this way, the cloud platform can generate a high-precision, high-confidence road adhesion coefficient sharing system.

[0051] Furthermore, the real-time transmission to vehicles traveling on the relevant road section includes:

[0052] Through the vehicle location information uploaded by the vehicle, the road section is matched on the cloud platform to obtain the road adhesion coefficient of the corresponding road section, which is then sent to the vehicle end for use.

[0053] It can be seen that the present invention provides a vehicle active braking method based on a cloud platform, which uploads the road data collected by the vehicle to the cloud platform, and the cloud platform estimates the corresponding road adhesion coefficient in real time and sends it to the vehicle to perform braking control. This solves the problem of insufficient reaction speed and estimation accuracy when the existing vehicle performs active braking due to the hardware performance limitations of the in-vehicle equipment, improves the safety and real-time performance of active braking of autonomous driving vehicles, and increases the accuracy of automatic emergency braking.

[0054] Accordingly, if Figure 3 As shown, the present invention also provides a vehicle active braking system based on a cloud platform, using the above-mentioned vehicle active braking method, including: a domain controller, a TBOX, a camera and a laser radar. The domain controller is respectively connected to the TBOX, the camera and the laser radar signals, and the domain controller collects the road surface data of the vehicle through the camera and the laser radar. The TBOX is connected to the cloud platform for communication, and the domain controller uploads the road surface data to the cloud platform through the TBOX. The cloud platform performs data processing based on the road surface data uploaded by multiple vehicles to generate a high-confidence road surface adhesion coefficient information system to estimate the road surface adhesion coefficient of the corresponding road section in real time, and send it to the vehicle end in real time to facilitate active braking of the vehicle.

[0055] The system further includes: a wheel speed meter; the wheel speed meter is connected to the domain controller by signal, and the wheel speed meter is used to detect the wheel speed of the vehicle. The domain controller uploads the wheel speed to the cloud platform through the TBOX, and the cloud platform estimates the sliding distance of the vehicle during braking according to the road adhesion coefficient and the wheel speed.

[0056] In actual applications, vehicles are equipped with a variety of sensors, including cameras, millimeter-wave radars, and wheel speed meters, which collect real-time data on road conditions around the vehicle. Through the domain controller, the vehicle-side system estimates the road adhesion coefficient and slip rate of the current section of the road based on the data obtained by the sensors and the vehicle's own driving status (such as speed, steering angle, etc.), combined with a specific algorithm. Many of these methods are conventional solutions and are relatively easy for professionals to obtain. In order to improve the accuracy of the estimation, the system continuously collects and analyzes data during the daily driving and braking of the vehicle, and dynamically adjusts the parameters of the estimation model every 24 hours, making the estimated road adhesion coefficient more accurate.

[0057] The system also includes: an AEB control module; the AEB control module is connected to the domain controller signal, and the AEB control module controls the vehicle to perform active braking according to the received road adhesion coefficient and the sliding distance.

[0058] In actual applications, the cloud platform generates reliable road adhesion coefficient data. The system will match the road section in the cloud through the location information provided by TBOX, obtain the road adhesion coefficient information of the road section, and then send this data to the vehicles traveling on the road section through the 5G network. When these vehicles need to perform automatic emergency braking, the AEB system can use the high-precision data provided by the cloud platform to make quick decisions and adjust the braking force. This process avoids the reaction delay caused by insufficient computing power on the vehicle side under extreme braking conditions, effectively improves the reaction speed and accuracy of the AEB system, and reduces the risk of accidents.

[0059] The system also includes: a navigation positioning device; the navigation positioning device is connected to the domain controller by signal, and is used to obtain vehicle position information in real time. The domain controller uploads the vehicle position information to the cloud platform through the TBOX, and the cloud platform matches the vehicle with the road section according to the vehicle position information to send the road adhesion coefficient of the corresponding road section.

[0060] It can be seen that the present invention provides a vehicle active braking system based on a cloud platform, which uploads the road data collected by the vehicle to the cloud platform, and the cloud platform estimates the corresponding road adhesion coefficient in real time and sends it to the vehicle to perform braking control. This solves the problem of insufficient reaction speed and estimation accuracy when the existing vehicle performs active braking due to the hardware performance limitations of the in-vehicle equipment, improves the safety and real-time performance of active braking of autonomous driving vehicles, and increases the accuracy of automatic emergency braking.

[0061] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A vehicle active braking method based on a cloud platform, characterized in that: include: The vehicle uploads the road data collected by the vehicle to the cloud platform in real time. The cloud platform processes the road data uploaded by multiple vehicles to generate a high-confidence road adhesion coefficient sharing system. The cloud platform estimates the road adhesion coefficient of the corresponding road section in real time through the road adhesion system data sharing system, and sends it to the vehicles traveling on the relevant road section in real time; During the vehicle's autonomous driving process, when a dangerous target is detected and active braking is required, braking is performed according to the road adhesion coefficient sent by the cloud platform.

2. The vehicle active braking method based on a cloud platform according to claim 1, characterized in that: Also includes: The cloud platform also estimates the braking force based on the road adhesion coefficient of the corresponding road section, and sends it to the vehicles traveling on the relevant road section, so that the vehicles can brake according to the road adhesion coefficient and the corresponding braking force.

3. The vehicle active braking method based on a cloud platform according to claim 2, characterized in that: Also includes: The driving state of the vehicle itself is obtained, and the slip distance of the vehicle is estimated according to the road adhesion coefficient corresponding to the current road section and the driving state, so as to control the braking distance of the vehicle.

4. The vehicle active braking method based on a cloud platform according to claim 3 is characterized in that: The vehicle uploads the road data collected by the vehicle to the cloud platform in real time, including: The road lane information, surrounding vehicle information, wheel speed and vehicle position information collected by the vehicle are combined with the vehicle's daily driving and braking data and uploaded to the cloud platform.

5. The vehicle active braking method based on a cloud platform according to claim 4 is characterized in that: The cloud platform processes the road surface data uploaded by multiple vehicles, including: The road surface data is filtered and weighted through data processing algorithms.

6. The vehicle active braking method based on a cloud platform according to claim 5, characterized in that: The real-time transmission to the vehicles traveling on the relevant road section includes: Through the vehicle location information uploaded by the vehicle, the road section is matched on the cloud platform to obtain the road adhesion coefficient of the corresponding road section, which is then sent to the vehicle end for use.

7. A vehicle active braking system based on a cloud platform, using the vehicle active braking method according to any one of claims 1 to 6, characterized in that: include: Domain controller, TBOX, camera and lidar; The domain controller is respectively connected to the TBOX, the camera and the laser radar signal, and the domain controller collects road surface data of the vehicle through the camera and the laser radar; The TBOX is connected to the cloud platform for communication, and the domain controller uploads the road surface data to the cloud platform through the TBOX; The cloud platform processes the road surface data uploaded by multiple vehicles to generate a high-confidence road surface adhesion coefficient information system to estimate the road surface adhesion coefficient of the corresponding road section in real time, and sends it to the vehicle end in real time to facilitate active braking of the vehicle.

8. The cloud platform-based vehicle active braking system according to claim 7, characterized in that: Also includes: Wheel speed meter; The wheel speed meter is connected to the domain controller signal, and the wheel speed meter is used to detect the wheel speed of the vehicle; The domain controller uploads the wheel speed to the cloud platform through the TBOX, and the cloud platform estimates the sliding distance of the vehicle during braking according to the road adhesion coefficient and the wheel speed.

9. The cloud platform-based vehicle active braking system according to claim 8, characterized in that: Also includes: AEB control module; The AEB control module is connected to the domain controller signal, and the AEB control module controls the vehicle to perform active braking according to the received road adhesion coefficient and the sliding distance.

10. The cloud platform-based vehicle active braking system according to claim 9, characterized in that: Also includes: Navigation and positioning device; The navigation and positioning device is connected to the domain controller signal for obtaining vehicle position information in real time; The domain controller uploads the vehicle position information to the cloud platform through the TBOX, and the cloud platform matches the vehicle with the road section according to the vehicle position information to send the road adhesion coefficient of the corresponding road section.

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