Control circuit of AI image collision avoidance system

Abstract

The utility model discloses a control circuit of an AI image anti-collision system, and belongs to the field of anti-collision of engineering trucks. Comprising a main control module; the video monitoring module is used for monitoring whether an obstacle exists in a blind area of the vehicle; the speed measuring module is used for measuring the speed of the vehicle; the speed control module is used for controlling the speed of the vehicle; the video monitoring module, the measurement module and the speed control module are connected with the master control module. Compared with the prior art, the system has the beneficial effects that the master control module, the video monitoring module and the speed measuring module are matched, and the master control module can monitor a blind area of a vehicle in real time through the video monitoring module, measure the speed of the vehicle in real time in cooperation with the speed measuring module, intelligently recognize people and obstacles in the blind area, and monitor the blind area of the vehicle in real time; and the speed is automatically reduced through the speed control module, so that accidents are prevented, and AI image intelligent collision avoidance of the vehicle is realized.

Description

Technical Field

[0001] This utility model belongs to the field of engineering vehicle collision avoidance, and specifically relates to an AI image collision avoidance system control circuit. Background Technology

[0002] When forklifts, loaders, cranes, and other construction vehicles are in operation, significant blind spots make it difficult for drivers to see their surroundings when reversing or turning, increasing the risk of collisions. Forklifts, in particular, are prone to collisions with shelves, walls, and pedestrians in confined spaces such as warehouses and aisles. To mitigate these risks, safety warning devices need to be installed on construction vehicles. Existing safety warning devices for construction vehicles typically include cameras, warning lights, and buzzers. While these provide some hazard warning functionality, their simplicity limits their ability to provide comprehensive protection and their impact-avoidance effectiveness. Utility Model Content

[0003] To address the aforementioned problems, the primary objective of this invention is to provide an AI image collision avoidance system control circuit capable of achieving systematic collision avoidance.

[0004] Another objective of this invention is to provide an AI image collision avoidance system control circuit that can achieve intelligent collision avoidance reminders through AI, thereby improving the collision avoidance effect.

[0005] To achieve the above objectives, the technical solution of this utility model is as follows:

[0006] This utility model provides a control circuit for an AI image collision avoidance system, including:

[0007] Main control module;

[0008] A video surveillance module used to monitor whether there are obstacles in the blind spots of a vehicle;

[0009] Speed ​​measurement module for measuring vehicle speed;

[0010] A speed control module used to control vehicle speed;

[0011] The video monitoring module, measurement module, and speed control module are all connected to the main control module.

[0012] In this application, the main control module monitors the vehicle's blind spots in real time through the video monitoring module, and measures the vehicle's speed in real time in conjunction with the speed measurement module. It intelligently identifies people and obstacles in the blind spots and automatically reduces the speed through the speed control module to prevent accidents from occurring.

[0013] Furthermore, the main control module adopts a main control MCU, and the model of the main control MCU is ST M32F103C8T6.

[0014] Furthermore, the video monitoring module includes multiple BSD cameras, which capture images of each blind spot of the vehicle in real time and feed the captured images back to the in-vehicle display screen in real time, making it easier for the driver to judge whether there is a collision risk. At the same time, the main control MCU can also obtain the information captured by the BSD cameras and perform risk assessment in combination with the vehicle's real-time speed. When a collision risk is assessed, the vehicle speed is controlled to avoid it.

[0015] Furthermore, the speed measurement module includes a speed sensor.

[0016] Furthermore, the control circuit of the AI ​​image collision avoidance system also includes a second wireless communication module, a relay control module, and a video authentication module, all of which are connected to the main control module.

[0017] Furthermore, the second wireless communication module adopts a Bluetooth module.

[0018] Furthermore, the video authentication module uses a DMS camera.

[0019] In this application, the 2.4G module wirelessly connects to a speed sensor to measure the vehicle's speed. When the vehicle is detected to be speeding, the speed information is sent to the main control MCU. The main control MCU then controls the speed through the RS485 interface circuit to reduce the vehicle's speed, thereby achieving intelligent speed control. The RS485 interface can also be extended with a weight sensor to provide intelligent warnings when overload is detected.

[0020] Furthermore, the control circuit of the AI ​​image collision avoidance system also includes a second wireless communication module and a relay control module, both of which are connected to the main control module.

[0021] Furthermore, the second wireless communication module adopts a Bluetooth module.

[0022] In this application, the Bluetooth module connects to a mobile app (such as a smartphone) to set various vehicle parameters and view device status. User authentication is also performed via the app; if authentication fails, the main control MCU controls a relay to prevent the vehicle from starting. This setup enables intelligent vehicle authentication, preventing unlicensed drivers from operating the vehicle and thus improving vehicle safety.

[0023] Furthermore, the control circuit of this AI image collision avoidance system also includes a power conversion module, which is connected to the main control MCU. The system draws power from the vehicle battery and converts the high voltage to a low voltage suitable for use by various modules of the system through the power conversion module, thus powering the main control MCU.

[0024] Furthermore, the AI ​​image collision avoidance system control circuit also includes a signal detection module for detecting the vehicle's driving status, which is connected to the main control MCU.

[0025] Furthermore, the signal detection module includes an ACC detection unit and a reversing detection unit, both of which are connected to the main control MCU. The main control MCU performs corresponding operations based on these detection signals.

[0026] Compared with the prior art, the beneficial effects of this utility model are as follows: This application uses a main control module, a video monitoring module, and a speed measurement module in combination. The main control module can monitor the vehicle's blind spots in real time through the video monitoring module, and measure the vehicle's speed in real time in conjunction with the speed measurement module. It can intelligently identify people and obstacles in the blind spots, and automatically reduce the speed through the speed control module to prevent accidents from occurring, thereby realizing AI image-based intelligent collision avoidance for vehicles. Attached Figure Description

[0027] Figure 1 This is the circuit block diagram of this application.

[0028] Figure 2 This is the circuit schematic of the first part of the main control MCU.

[0029] Figure 3 This is the schematic diagram of the second part of the main control MCU circuit.

[0030] Figure 4 This is the circuit schematic of the first part of the power conversion module.

[0031] Figure 5 yes Figure 4 A magnified view of a portion of point A in the image.

[0032] Figure 6 yes Figure 4 A magnified view of a portion of point B in the image.

[0033] Figure 7 yes Figure 4 A magnified view of a portion of point C in the middle.

[0034] Figure 8 yes Figure 4 A magnified view of a portion of point D in the image.

[0035] Figure 9 yes Figure 4 A magnified view of a portion of point E in the middle.

[0036] Figure 10 This is the circuit schematic of the second part of the power conversion module.

[0037] Figure 11 yes Figure 10 A magnified view of a portion of point F in the middle.

[0038] Figure 12 yes Figure 10 A magnified view of a portion of point H in the middle.

[0039] Figure 13 This is the circuit schematic of the first part of the test sensor.

[0040] Figure 14 This is the circuit schematic for the second part of the test sensor.

[0041] Figure 15 This is the circuit diagram of the relay control module.

[0042] Figure 16 This is the circuit diagram of the speed control module.

[0043] Figure 17 This is the circuit schematic of the 2.4G module.

[0044] Figure 18 This is the circuit diagram of a Bluetooth module.

[0045] Figure 19 This is the circuit diagram of the reversing detection unit.

[0046] Figure 20 This is the circuit diagram of the first part of the ACC detection unit.

[0047] Figure 21 This is the circuit diagram of the second part of the reversing detection unit and ACC detection unit. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this utility model clearer, the present utility model will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present utility model and are not intended to limit the present utility model.

[0049] To achieve the above objectives, the technical solution of this utility model is as follows:

[0050] See Figure 1-20 As shown, this embodiment provides a control circuit for an AI image collision avoidance system, including:

[0051] Main control module;

[0052] A video surveillance module used to monitor whether there are obstacles in the blind spots of a vehicle;

[0053] Speed ​​measurement module for measuring vehicle speed;

[0054] A speed control module used to control vehicle speed;

[0055] The video monitoring module, measurement module, and speed control module are all connected to the main control module.

[0056] In this application, the main control module monitors the vehicle's blind spots in real time through the video monitoring module, and measures the vehicle's speed in real time in conjunction with the speed measurement module. It intelligently identifies people and obstacles in the blind spots and automatically reduces the speed through the speed control module to prevent accidents from occurring.

[0057] Furthermore, the main control module adopts a main control MCU, and the model of the main control MCU is ST M32F103C8T6.

[0058] Furthermore, the video monitoring module includes multiple BSD cameras, which capture images of each blind spot of the vehicle in real time and feed the captured images back to the in-vehicle display screen in real time, making it easier for the driver to judge whether there is a collision risk. At the same time, the main control MCU can also obtain the information captured by the BSD cameras and perform risk assessment in combination with the vehicle's real-time speed. When a collision risk is assessed, the vehicle speed is controlled to avoid it.

[0059] Furthermore, the speed measurement module includes a speed sensor.

[0060] Furthermore, the control circuit of the AI ​​image collision avoidance system also includes a first wireless communication module and an interface module. The main control module and the speed measurement module are connected through the first wireless communication module, and the main control module and the speed control module are connected through the interface module.

[0061] Furthermore, the first wireless communication module is a 2.4G module.

[0062] Furthermore, the interface module adopts an RS485 interface.

[0063] In this application, the 2.4G module wirelessly connects to a speed sensor to measure the vehicle's speed. When the vehicle is detected to be speeding, the speed information is sent to the main control MCU. The main control MCU then controls the speed through the RS485 interface circuit to reduce the vehicle's speed, thereby achieving intelligent speed control. The RS485 interface can also be extended with a weight sensor to provide intelligent warnings when overload is detected.

[0064] Furthermore, the control circuit of the AI ​​image collision avoidance system also includes a second wireless communication module, a relay control module, and a video authentication module, all of which are connected to the main control module.

[0065] Furthermore, the second wireless communication module adopts a Bluetooth module.

[0066] Furthermore, the video authentication module uses a DMS camera.

[0067] In this application, the Bluetooth module connects to a mobile app (such as a smartphone) to set various vehicle parameters and view device status. User authentication is also performed via the app. During authentication, the DMS camera captures an image of the user for video verification. If authentication is successful, the user is granted permission to start the vehicle; if authentication fails, user permission is denied, and the main control MCU controls a relay to prevent the vehicle from starting. This setup enables intelligent vehicle authentication, preventing unlicensed drivers from operating the vehicle and thus improving vehicle safety.

[0068] Furthermore, the control circuit of this AI image collision avoidance system also includes a power conversion module, which is connected to the main control MCU. The system draws power from the vehicle battery and converts the high voltage to a low voltage suitable for use by various modules of the system through the power conversion module, thus powering the main control MCU.

[0069] Furthermore, the AI ​​image collision avoidance system control circuit also includes a signal detection module for detecting the vehicle's driving status, which is connected to the main control MCU.

[0070] Furthermore, the signal detection module includes an ACC detection unit and a reversing detection unit, both of which are connected to the main control MCU. The main control MCU performs corresponding operations based on these detection signals.

[0071] For details, see Figure 19 During reversing detection, the reversing signal is divided by 100K and 20K resistors. When the input voltage does not exceed 20V, the MCU can directly detect the voltage value after voltage division. The MCU determines whether the vehicle is moving forward or in reverse based on the detected voltage level. When the input voltage exceeds 20V, the output voltage is regulated by a DZ7 voltage regulator to ensure that the output signal does not exceed 3.3V, which can be normally detected by the MCU as a high level, thus triggering the reversing operation.

[0072] For details, see Figure 20 In ACC signal detection, a resistor voltage divider is first used, and then the signal is stabilized at 3.3V through a Zener diode. Then, the conduction of transistor Q8 is controlled to send the signal to the MCU. This can protect the main control MCU from damage when the input voltage exceeds the limit.

[0073] The above are merely preferred embodiments of the present utility model and are not intended to limit the present utility model. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present utility model should be included within the protection scope of the present utility model.

Claims

1. A control circuit for an AI image collision avoidance system, characterized in that, include: Main control module; A video surveillance module used to monitor whether there are obstacles in the blind spots of a vehicle; Speed ​​measurement module for measuring vehicle speed; A speed control module used to control vehicle speed; The video monitoring module, measurement module, and speed control module are all connected to the main control module. The main control module uses a main control MCU, and the model of the main control MCU is STM32F103C8T6; The video surveillance module includes multiple BSD cameras.

2. The AI ​​image collision avoidance system control circuit as described in claim 1, characterized in that, The speed measurement module includes a speed sensor.

3. The AI ​​image collision avoidance system control circuit as described in claim 1, characterized in that, The AI ​​image collision avoidance system control circuit also includes a first wireless communication module and an interface module. The main control module and the speed measurement module are connected through the first wireless communication module, and the main control module and the speed control module are connected through the interface module.

4. The AI ​​image collision avoidance system control circuit as described in claim 3, characterized in that, The first wireless communication module is a 2.4G module, and the interface module is an RS485 interface.

5. The AI ​​image collision avoidance system control circuit as described in claim 1, characterized in that, The control circuit of the AI ​​image collision avoidance system also includes a second wireless communication module, a relay control module, and a video authentication module, all of which are connected to the main control module.

6. The AI ​​image collision avoidance system control circuit as described in claim 1, characterized in that, The control circuit of the AI ​​image collision avoidance system also includes a power conversion module, which is connected to the main control MCU.

7. The AI ​​image collision avoidance system control circuit as described in claim 1, characterized in that, The AI ​​image collision avoidance system control circuit also includes a signal detection module for detecting the vehicle's driving status, which is connected to the main control MCU.

8. The AI ​​image collision avoidance system control circuit as described in claim 7, characterized in that, The signal detection module includes an ACC detection unit and a reversing detection unit, both of which are connected to the main control MCU.

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