Multi-purpose teaching and training intelligent vehicle based on machine vision

Abstract

The invention belongs to the technical field of artificial intelligence, and discloses a multi-purpose teaching and training intelligent vehicle based on machine vision. The specific use steps of the multipurpose teaching and training intelligent vehicle in a training field given by a user are as follows: S1, initialization and starting, S2, track tracking operation, S3, detection of sidewalks or upslopes, S4, operation in a deceleration zone, S5, acceleration to pass through roundabout, S6, detection of a yellow square frame and traffic light identification, and S7, arrival at a destination: a run6 is started for acceleration in a green light, and the vehicle continuously drives to a terminal point to complete a driving task. Through theoretical pushing and field verification, the rotating speed of the motor of the intelligent vehicle is successfully optimized to 300 revolutions per minute, perfect matching of the speed and the torque is achieved, the running stability of the intelligent vehicle is remarkably improved through the adjustment, the intelligent vehicle can steadily turn at different curves, stable and smooth racing track performance is ensured, and the running safety of the intelligent vehicle is improved. The innovation not only overcomes the centrifugal force problem of the original motor at a high speed, but also enhances the controllability of the vehicle, and establishes a new benchmark for the technical development of the intelligent vehicle.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically a multi-purpose intelligent teaching and training vehicle based on machine vision. Background Technology

[0002] The rapid development of recognition technology and deep learning algorithms has made autonomous driving possible. As a robotic system, the intelligent vehicle involves three main robotic technologies: perception, path planning, and control. Intelligent recognition and decision-making technologies act like the central nervous system of a robot, forming the core of intelligent vehicle technology. In intelligent vehicles, the recognition of optical and visual information includes judgments made about road conditions, pedestrians, and landmarks, thereby enabling the intelligent vehicle to perceive its environment and change its direction of movement. After the successful design and application of the intelligent vehicle, a problem arose due to the excessive speed of the DC motor. While operating at speeds up to 500 rpm improved the efficiency of straight-line travel, it revealed serious problems during turns: the high speed caused a surge in centrifugal force, while the motor's torque was relatively insufficient to effectively counteract the centrifugal effect. Since the speed control mechanism of the DC motor relies on voltage adjustment, reducing the voltage, while slowing down the speed, also directly weakened the motor's torque output, making the intelligent vehicle more susceptible to centrifugal force during turns, risking it running off the track and ultimately leading to operational failure. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-purpose intelligent educational vehicle based on machine vision to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a multi-purpose intelligent teaching and training vehicle based on machine vision, the specific steps of using the intelligent vehicle are as follows:

[0005] S1: Initialization and Startup: Initialize Arduino, OpenMV, and touch screen hardware. The My_start() function continuously detects the touch screen start button. If it is not pressed in time, it waits. Once pressed, it exits and enters the track to run normally.

[0006] S2: Track Tracking Operation: Performs tracking tests, adjusts left and right wheel speeds according to road conditions, and calls the motor control unit;

[0007] S3: Pedestrian crossing or uphill detected: If pedestrian crossing or uphill is detected, stop for 1 second and then restart run_trackingTest1_TIME.

[0008] S4: Deceleration Zone Operation: When encountering a deceleration sign, adjust the deceleration function and continue decelerating until the end sign to ensure driving safety;

[0009] S5: Accelerate through roundabouts: Accelerate immediately after the deceleration zone, run5 quickly increases speed, accurate direction when navigating the roundabout, and pass through safely without worry;

[0010] S6: Yellow box detection and traffic light recognition: Stop when encountering a yellow box, use RGB_lamp to determine the light color, cycle through red lights, and exit when the green light is detected, making intelligent driving safer;

[0011] S7: Destination reached: Start running at the green light, accelerate, and continue driving towards the destination to complete the driving task.

[0012] Preferably, the initialization and startup steps in S1 are as follows:

[0013] Step 1: Hardware Initialization: First, configure the Arduino microprocessor, ensure the OpenMV video camera is working properly, and initialize the touch screen buttons and other necessary hardware interfaces;

[0014] Step 2: Wait for the start signal: Then, the My_start() function is called to enter the waiting state. Inside this function, a loop is implemented to continuously check the status of the touch screen start button;

[0015] Step 3: Start the smart car: If the touch screen start button is not pressed, the loop in the My_start() function will continue to wait; once the button is detected to be pressed, the function will exit immediately, marking the smart car entering the normal running state along the track.

[0016] Preferably, the track tracking operation in S2 refers to executing the run_trackingTest1_TIME function to start the tracking test. This function simulates track operation for a certain period of time based on the input parameters. Then, based on the road condition information captured by the camera, the speeds of the left and right wheels are calculated using a specific formula: the speed of the left wheel (Left) increases or decreases according to the deviation, while the speed of the right wheel (Right) is adjusted in the opposite direction to maintain vehicle stability. Right = 90 - (int)(1.3 * (L_cx - LEFT)). Finally, the motor(Left, Right) function is called to pass the calculated speed value to the motor controller.

[0017] Preferably, the specific steps for detecting a sidewalk or uphill slope in step S3 are as follows:

[0018] Step 1: Stop the car when a sidewalk or uphill slope is detected:

[0019] When the system detects that there is a sidewalk or uphill ahead, it immediately calls the motor(0,0) function to stop both wheels from turning at the same time, thus safely stopping the car.

[0020] Step Two: Delay and Wait:

[0021] After the car stops, a delay operation is performed, waiting for 1 second. The delay is to ensure that the car comes to a complete stop and adapts to the current environment.

[0022] Step 3: Restart the car:

[0023] After the delay ends, the deceleration function is called again to allow the car to continue the tracking test.

[0024] Preferably, the operation of the deceleration zone in S4 refers to the system immediately responding when a deceleration sign is detected during the intelligent vehicle's driving process, calling the deceleration function to reduce the vehicle's speed to ensure safe passage through the area requiring deceleration. Within the deceleration zone, the vehicle will continue to drive at the adjusted lower speed until the sensor detects the deceleration end sign.

[0025] Preferably, in S5, accelerating through the roundabout means that when the intelligent vehicle reaches the deceleration zone and successfully detects the deceleration end sign, the system will immediately exit the current deceleration function and stop the deceleration operation. Subsequently, in order to restore the normal driving speed, the vehicle will call the acceleration function run5 and gradually increase the vehicle speed by adjusting the parameters.

[0026] Preferably, the specific steps for detecting the yellow box and recognizing the traffic light in step S6 are as follows:

[0027] Step 1: Stop at the yellow box

[0028] When the intelligent vehicle is driving, its sensor system detects a yellow box on the road ahead and immediately triggers a stop mechanism.

[0029] Call the motor function to stop both wheels of the car from rotating simultaneously, ensuring that the car can safely stop in front of the yellow box;

[0030] Step 2: Traffic Light Recognition

[0031] After the car stops, the system immediately calls the RGB_lamp(1000) function to start the traffic light recognition process;

[0032] This function uses the car's camera or color sensor to identify the color of the traffic light ahead and stores the identification result in an internal variable for subsequent judgment.

[0033] Step 3: Determine the traffic light status and take action.

[0034] The system internally starts a while(1) loop to continuously monitor the status of the traffic lights;

[0035] Inside the loop, the return value of the RGB_lamp function or an internal variable is checked to determine whether the current light is red or green.

[0036] If the light is red, the car remains stopped and continues to wait in the loop;

[0037] If the light is green, the while(1) loop will exit, and the acceleration or running function will be called to make the car continue to move.

[0038] Preferably, in S7, "destination arrival" means that when the traffic light recognition function confirms that the light ahead is green, the car will exit the recognition state and immediately call the run6(1500) function to increase the speed and continue moving forward. During the subsequent driving process, the car will continuously monitor the road conditions and adjust the speed or direction as needed.

[0039] Preferably, the control system of the intelligent vehicle consists of a main control board module, a right ultrasonic transmission module, a road sign camera module, an attitude sensor module, and a road condition sensor module.

[0040] The beneficial effects of this invention are as follows:

[0041] 1. Through theoretical deduction and field verification, this invention successfully optimized the motor speed of the intelligent vehicle to 300 rpm, achieving a perfect match between speed and torque. This adjustment significantly improves the stability of the intelligent vehicle, making it more agile when turning on different curves and ensuring a smooth and stable track performance. This innovation not only overcomes the centrifugal force problem of the original motor at high speeds but also enhances the vehicle's handling, setting a new benchmark for the development of intelligent vehicle technology.

[0042] 2. This invention integrates mechanical sophistication, electronic intelligence, sensitive sensing, touchscreen interaction, and machine vision recognition technology into a highly integrated innovative platform. Employing mainstream programming languages ​​such as C, C++, and Python, it provides abundant opportunities for programming learning and practice for teenagers, vocational school students, and university students. Users can not only delve into programming knowledge but also flexibly modify code according to specific environment, participate in machine vision-related robot design competitions, and showcase their creativity and skills. This multi-purpose robot is not only a bridge for technological exploration but also an important tool for cultivating future scientific and technological talents. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall process structure of the present invention;

[0044] Figure 2 This is a schematic diagram of the software operation judgment box of the present invention;

[0045] Figure 3 This is a schematic diagram of the electrical wiring of the present invention;

[0046] Figure 4 This is a schematic diagram of the actual product of the present invention;

[0047] Figure 5 This is a schematic diagram of the practice area for the present invention;

[0048] Figure 6 This is a schematic diagram of the mechanical structure of the intelligent vehicle of the present invention.

[0049] In the diagram: 1. Photoelectric sensor 2; 2. Photoelectric sensor 1; 3. Housing plate 4; 4. Ultrasonic sensor 1; 5. Housing plate 3; 6. Housing plate 1; 7. Housing plate 2; 8. Ultrasonic sensor 4; 9. Ultrasonic sensor 3; 10. Speaker; 11. Ultrasonic sensor 5; 12. Housing plate 6; 13. Control circuit; 14. Ultrasonic sensor 6; 15. Housing plate 5; 16. Motor; 17. Robotic arm; 18. Ultrasonic sensor 2; 19. Photoelectric sensor 5; 20. Photoelectric sensor 6; 21. Wheel; 22. Photoelectric sensor 3; 23. Photoelectric sensor 4. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] like Figures 1 to 6 As shown in the figure, this embodiment of the invention provides a multi-purpose intelligent teaching and training vehicle based on machine vision. The specific steps for using this intelligent vehicle are as follows:

[0052] S1: Initialization and Startup: Initialize hardware such as Arduino, OpenMV, and touch screen. The My_start() function continuously detects the touch screen start button. If it is not pressed in time, it waits. Once pressed, it exits and enters the track to run normally.

[0053] S2: Track Tracking Operation: Performs tracking tests, adjusts left and right wheel speeds according to road conditions, and calls the motor control unit;

[0054] S3: Pedestrian crossing or uphill detected: If pedestrian crossing or uphill is detected, stop for 1 second and then restart run_trackingTest1_TIME.

[0055] S4: Deceleration Zone Operation: When encountering a deceleration sign, adjust the deceleration function and continue decelerating until the end sign to ensure driving safety;

[0056] S5: Accelerate through roundabouts: Accelerate immediately after the deceleration zone; Run5 quickly increases speed. Precise direction before the roundabout ensures safe passage.

[0057] S6: Yellow box detection and traffic light recognition: Stop when encountering a yellow box, use RGB_lamp to determine the light color, cycle through red lights, and exit when the green light is detected, making intelligent driving safer;

[0058] S7: Destination reached: Start running at the green light, accelerate, and continue driving towards the destination to complete the driving task.

[0059] Enter the destination function: exit the traffic light recognition function when the light turns green and call the run6(1500) function.

[0060] Continue to the destination: The car continues to run until it reaches its destination, completing the current driving task.

[0061] The specific initialization and startup steps in S1 are as follows:

[0062] Step 1: Hardware Initialization: First, configure the Arduino microprocessor, ensure the OpenMV video camera is working properly, and initialize the touch screen buttons and other necessary hardware interfaces, such as the motor drive interface.

[0063] Step Two: Waiting for the Startup Signal: Then, the `My_start()` function is called to enter the waiting state. Inside this function, a loop is implemented to continuously check the status of the touchscreen start button;

[0064] Step 3: Start the smart car: If the touch screen start button is not pressed, the loop in the My_start() function will continue to wait; once the button is detected to be pressed, the function will exit immediately, marking the smart car entering the normal running state along the track.

[0065] The meticulously designed initialization and startup process lays a solid foundation for the stable operation of the intelligent vehicle. Comprehensive hardware initialization ensures the coordinated operation of all components, enhancing system reliability. The mechanism of waiting for the startup signal empowers the user, increasing ease of use. Once started, the intelligent vehicle can quickly enter a track-side running state, demonstrating its efficient responsiveness and automation advantages. This process improves the user experience and ensures the smooth operation of the intelligent vehicle.

[0066] The track tracking operation in S2 refers to executing the `run_trackingTest1_TIME` function to initiate the tracking test. This function simulates track operation for a certain period of time based on the input parameters. Subsequently, based on road condition information captured by the camera (such as the difference between the centerline position L_cx and the preset LEFT offset), the speeds of the left and right wheels are calculated using a specific formula: the left wheel speed (Left) increases or decreases according to the deviation, while the right wheel speed (Right) is adjusted in the opposite direction to maintain vehicle stability. Specifically, the calculations are: Left = 90 + (int)(1.3 * (L_cx - LEFT)) and Right = 90 - (int)(1.3 * (L_cx - LEFT)). Finally, the `motor(Left, Right)` function is called to transmit the calculated speed values ​​to the motor controller, thereby achieving precise control of the left and right wheels of the intelligent vehicle and ensuring that the vehicle can flexibly respond to track changes and operate stably.

[0067] The track-tracking mechanism significantly improves the driving stability and track adaptability of intelligent vehicles through precise calculations and real-time adjustments. This mechanism utilizes cameras to capture road condition information, rapidly responds to track changes, and adjusts the speed of the left and right wheels using a unique formula to ensure stable vehicle operation. This design not only enhances the automation level of intelligent vehicles but also strengthens their ability to cope with complex road conditions, providing strong support for their outstanding performance on various tracks, while simultaneously improving the user experience and overall system performance.

[0068] The specific steps for detecting a sidewalk or uphill slope in step S3 are as follows:

[0069] Step 1: Stop the car when a sidewalk or uphill slope is detected:

[0070] When the system detects that there is a sidewalk or uphill ahead, it immediately calls the motor(0,0) function to stop both wheels from turning at the same time, thus safely stopping the car.

[0071] Step Two: Delay and Wait:

[0072] After the vehicle stops, a 1-second delay is performed. This delay is to ensure the vehicle comes to a complete stop and adapts to the current environment (such as waiting for pedestrians to cross or assessing the difficulty of going uphill).

[0073] Step 3: Restart the car:

[0074] After the delay ends, the `run_trackingTest1_TIME(70,3600,1.8)` function is called again to allow the car to continue the tracking test. This step ensures that the car can continue its preset running task after confirming that the environment is safe or is ready to deal with the uphill.

[0075] Detailed steps for detecting pedestrian crossings or inclines significantly enhance the flexibility and safety of intelligent vehicles. Instant stopping ensures respect for pedestrians and road safety, while delayed waiting enhances environmental adaptability, allowing the vehicle to handle complex situations more reliably. Restarting and decelerating ensures that the vehicle can continue to complete tasks efficiently after confirming safety. This process design optimizes the user experience and enhances the practicality and reliability of autonomous driving technology.

[0076] In S4, the deceleration zone operation refers to the system's immediate response when the intelligent vehicle detects a deceleration sign. The system calls the deceleration function `run_trackingTest3(70,2200,1.8)` to reduce the vehicle's speed, ensuring safe passage through the deceleration zone. Within the deceleration zone, the vehicle continues to travel at the adjusted lower speed until the sensor detects the end of deceleration sign. This process is designed to allow the vehicle to flexibly respond to various speed changes on the road, improving driving safety and stability.

[0077] The automatic deceleration process in the deceleration zone significantly enhances the road adaptability of intelligent vehicles. It ensures that the vehicle responds quickly upon detecting a deceleration sign, reducing speed to ensure safety, and maintaining stable driving within the deceleration zone until it passes safely. This process not only improves driving safety but also enhances driving stability through precise speed control, providing passengers with a safer and more comfortable travel experience and promoting the mature application of intelligent vehicle technology.

[0078] In S5, accelerating through the roundabout refers to the intelligent vehicle's immediate exit from the current deceleration function and cessation of deceleration when it reaches the deceleration zone and successfully detects the deceleration end sign. Subsequently, to restore normal driving speed, the vehicle calls the acceleration function `run5`, gradually increasing speed by adjusting parameters. When facing roundabouts, the vehicle precisely controls its steering system based on real-time road condition information, ensuring accurate and stable direction, thus safely and smoothly navigating the roundabout area. This process demonstrates the intelligent vehicle's autonomous decision-making and coping capabilities in complex environments.

[0079] The specific steps for detecting the yellow box and recognizing the traffic light in S6 are as follows:

[0080] Step 1: Stop at the yellow box

[0081] When the intelligent vehicle is driving, its sensor system detects a yellow box on the road ahead and immediately triggers a stop mechanism.

[0082] Call the motor function to stop both wheels of the car from rotating simultaneously, ensuring that the car can safely stop in front of the yellow box;

[0083] Step 2: Traffic Light Recognition

[0084] After the car stops, the system immediately calls the RGB_lamp(1000) function to start the traffic light recognition process;

[0085] This function uses the car's camera or color sensor to identify the color of the traffic light ahead and stores the identification result in an internal variable for subsequent judgment.

[0086] Step 3: Determine the traffic light status and take action.

[0087] The system starts a while(1) loop to continuously monitor the status of traffic lights.

[0088] Inside the loop, the current light (red or green) is determined by checking the return value of the RGB_lamp function or its internal variables.

[0089] If the light is red, the car remains stopped and continues to wait in the loop.

[0090] If the light is green, the while(1) loop will exit, and the acceleration or running function will be called to make the car continue to move.

[0091] By following clearly defined procedures, the safety of the intelligent vehicle is significantly improved. It can stop instantly to avoid danger, accurately recognize traffic lights to avoid violations, and protect the safety of people and vehicles. At the same time, the detailed procedures enhance system reliability, facilitating developer debugging and user maintenance, ensuring smooth operation. Furthermore, it improves the user experience, increases user trust and satisfaction, and provides greater peace of mind through predictive behavior. More importantly, this lays a solid foundation for intelligent vehicle technology, driving its continuous progress and intelligent development, and providing unlimited possibilities for future complex applications.

[0092] In S7, "destination arrival" refers to the car exiting the traffic light recognition state and immediately calling the run6(1500) function when the traffic light recognition function confirms that the light ahead is green. This allows the car to accelerate to a higher speed and continue driving. During the subsequent journey, the car will continuously monitor road conditions and adjust its speed or direction as needed to ensure a safe and efficient arrival at the destination. As the destination approaches, the car will successfully complete its mission and end the entire journey.

[0093] The optimized destination arrival process not only improves the driving efficiency of the intelligent vehicle but also ensures a safe arrival. By responding instantly to green lights, accelerating forward, and continuously monitoring road conditions to adjust driving strategies, the vehicle achieves a perfect balance between efficiency and safety. This process enhances the user experience, increases user trust and satisfaction with the vehicle's performance, and provides solid technical support and confidence for the application of intelligent vehicles in more complex scenarios, driving its intelligent development forward.

[0094] (1) Main function code (C / C++ programming)

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[0102] (2) Deciphering information codes from serial port 2

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[0107] (4) OpenMV track camera code (Python programming)

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[0114] The control system of the intelligent vehicle consists of a main control board module, a right ultrasonic transmission module, a road sign camera module, an attitude sensor module, and a road condition sensor module.

[0115] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0116] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-purpose intelligent educational vehicle based on machine vision, characterized in that: The specific steps for using the multi-purpose intelligent education and training vehicle are as follows: S1: Initialization and Startup: Initialize Arduino, OpenMV, and touch screen hardware. The My_start() function continuously detects the touch screen start button. If it is not pressed in time, it waits. Once pressed, it exits and enters the track to run normally. S2: Track Tracking Operation: Performs tracking tests, adjusts left and right wheel speeds according to road conditions, and calls the motor control unit; S3: Pedestrian crossing or uphill detected: If pedestrian crossing or uphill is detected, stop for 1 second and then restart run_trackingTest1_TIME. S4: Deceleration Zone Operation: When encountering a deceleration sign, adjust the deceleration function and continue decelerating until the end sign to ensure driving safety; S5: Accelerate through roundabouts: Accelerate immediately after the deceleration zone, run5 quickly increases speed, accurate direction when navigating the roundabout, and pass through safely without worry; S6: Yellow box detection and traffic light recognition: Stop when encountering a yellow box, use RGB_lamp to determine the light color, cycle through red lights, and exit when the green light is on, making intelligent driving safer; S7: Destination reached: Start running at the green light, accelerate, and continue driving towards the destination to complete the driving task.

2. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: The specific initialization and startup steps in S1 are as follows: Step 1: Hardware Initialization: First, configure the Arduino microprocessor, ensure the OpenMV video camera is working properly, and initialize the touch screen buttons and other necessary hardware interfaces; Step 2: Wait for the start signal: Then, the My_start() function is called to enter the waiting state. Inside this function, a loop is implemented to continuously check the status of the touch screen start button; Step 3: Start the smart car: If the touch screen start button is not pressed, the loop in the My_start() function will continue to wait; once the button is detected to be pressed, the function will exit immediately, marking the smart car entering the normal running state along the track.

3. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: In S2, track tracking refers to executing the `run_trackingTest1_TIME` function to start the tracking test. This function simulates track operation for a certain period of time based on the input parameters. Then, based on the road condition information captured by the camera, it calculates the speeds of the left and right wheels using a specific formula: the left wheel speed (Left) increases or decreases according to the deviation, while the right wheel speed (Right) is adjusted in the opposite direction to maintain vehicle stability. Specifically, the calculations are: Left = 90 + (int)(1.3 * (L_cx - LEFT)) and Right = 90 - (int)(1.3 * (L_cx - LEFT)). Finally, the `motor(Left, Right)` function is called to pass the calculated speed values ​​to the motor controller.

4. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: The specific steps for detecting a sidewalk or uphill slope in S3 are as follows: Step 1: Stop the car when a sidewalk or uphill is detected: When the system detects a sidewalk or uphill ahead, it immediately calls the motor(0,0) function to stop both wheels from turning at the same time, thus safely stopping the car; Step 2: Delay and wait: After the car stops, perform a delay operation and wait for 1 second. The delay is to ensure that the car stops completely and adapts to the current environment. Step 3: Restart the car: After the delay ends, call the deceleration function again to allow the car to continue the tracking test.

5. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: The operation of the deceleration zone in S4 refers to the system's immediate response when a deceleration sign is detected during the intelligent vehicle's operation. The system calls the deceleration function to reduce the vehicle's speed to ensure safe passage through the area requiring deceleration. Within the deceleration zone, the vehicle will continue to travel at the adjusted lower speed until the sensor detects the deceleration end sign.

6. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: In S5, accelerating through the roundabout means that when the intelligent vehicle reaches the deceleration zone and successfully detects the deceleration end sign, the system will immediately exit the current deceleration function and stop the deceleration operation. Subsequently, in order to restore the normal driving speed, the vehicle will call the acceleration function run5 and gradually increase the vehicle speed by adjusting the parameters.

7. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: The specific steps for detecting the yellow box and recognizing the traffic light in S6 are as follows: Step 1: Stop at the yellow box: When the intelligent car is driving, its sensor system detects a yellow box on the road ahead and immediately triggers the stop mechanism; the motor function is called to stop the left and right wheels of the car at the same time, ensuring that the car can stop safely in front of the yellow box; Step 2: Traffic light recognition: After the car stops, the system immediately calls the RGB_lamp(1000) function to start the traffic light recognition process; this function uses the car's camera or color sensor to recognize the color of the traffic light in front and stores the recognition result in an internal variable for subsequent judgment. Step 3: Determine the traffic light status and take action: The system starts a while(1) loop to continuously monitor the traffic light status; inside the loop, it checks the return value of the RGB_lamp function or the internal variable to determine whether the light is red or green. If it is red, the car remains stopped and continues to wait in the loop. If the light is green, the while(1) loop will exit, and the acceleration or running function will be called to make the car continue to move.

8. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: In S7, "destination arrival" means that when the traffic light recognition function confirms that the light ahead is green, the car will exit the recognition state and immediately call the run6(1500) function to increase the speed and continue moving forward. During the subsequent driving process, the car will continuously monitor the road conditions and adjust the speed or direction as needed.

9. The multi-purpose intelligent teaching and training vehicle based on machine vision according to claim 1, characterized in that: The intelligent vehicle consists of a vehicle body, a main control board module, a right ultrasonic transmission module, a road sign camera module, an attitude sensor module, and a road condition sensor module.

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