I. Product Requirements
Applicable to practical training projects in"New Energy Vehicle Driving Assistance System Maintenance,""Assembly and Calibration of Environmental Perception Components,"and"Intelligent Sensor Technology,"covering visual sensor structural understanding, assembly and calibration, and basic application development. The visual sensor experiment box integrates comprehensive application training equipment for UVC protocols, visual sensor structural principles, lane recognition, traffic light recognition, and facial recognition. It requires three types of visual sensors, one piece of 13.3-inch industrial computer, and a test vehicle. It includes a wireless keyboard and mouse, specialized tools, and is designed to meet diverse development and learning needs. It also comes with instructional videos and relevant practical training accessories.
 
II. Technical Requirements:
1. Communication Protocol: Utilizes the UVC protocol. The UVC (USB Video Class) protocol is a USB device protocol standard primarily used for data transmission between digital video devices. It defines how video data is transmitted via a USB interface and is widely used in webcams, video capture cards, digital cameras, and other video devices.
2. Input and Output Interfaces: The experiment box supports dual USB interfaces and a Gigabit Ethernet port, and is expandable with COM interfaces, LPT interfaces, GPIO interfaces, and other ports.
3. Function 1: Incorporates Flash technology to complete the disassembly and assembly of visual sensor components, providing practical training on the structure and principles of visual sensors.
4. Function 2: Incorporates visual sensor assembly and testing software to provide practical training on visual sensor testing.
5. Function 3: Incorporates visual sensor calibration software to complete assembly, calibration, and performance testing of visual sensors.
6. Function 4: Incorporates a visual sensor application system to complete comprehensive application training experiments in lane recognition, traffic light recognition, and facial recognition.
 
III. Practical Training (Experimental) Projects:
1. Visual Sensor Structural Cognition Training;
2. Visual Sensor Performance Testing;
3. Visual Sensor Assembly and Calibration;
4. Visual Sensor Lane Recognition Application Development Experiment;
5. Visual Sensor Traffic Light Recognition Application Development Experiment;
6. Visual Sensor Face Recognition Application Development Experiment;
 
IV. Teaching Resources:
1. Learning Worksheet: Covers visual sensor recognition, visual sensor quality testing, visual sensor debugging, visual sensor calibration, lane recognition testing, traffic light recognition testing, face recognition testing, and lane keeping experiments using a ROS car.
2. Simulation Testing: Using intelligent driving simulation software, complete lane departure warning (LDW) and lane centering control (LCC).
 
V. Performance Parameters:
1. Appearance Parameters:
1) Box Dimensions: Approximately 505*420*180mm
2) Operating Voltage: DC12V
3) Operating Temperature: -20°C to 60°C
2. Industrial Computer Parameters:
1) Processor: Intel Core i7
2) RAM: 4GB
3) Hard Drive: 128GB SSD
4) Operating System: Windows 10
3. Vision Sensor Parameters:
Monocular Camera Parameters:
Dimensions: 44*36*22mm
Operating Voltage: 5V
Focal Length: 6mm
Resolution: 1080P
Horizontal Angle: 60°
Vertical Angle: 32°
Frequency: 50Hz
Output Format: MJPG, supports YUY2/YUYV
Supported Operating Systems: Windows/Ubuntu, UVC Protocol
Fisheye Camera Parameters:
Dimensions: 44*36*26mm
Operating Voltage: 5V
Resolution: 1080P
Horizontal Angle: 180°
Vertical Angle: 180°
Frequency: 50Hz
Output Format: MJPG, supports YUY2/YUYV
Supported Operating Systems: Windows/Ubuntu, UVC Protocol
4. Test Vehicle Parameters:
1) Dimensions: 269*194*65mm
2) CPU: ARM Cortex-A72 64-bit @ 1.5GHz (quad-core)
3) Computing Power: 0.2TOPS (FP16)
4) Interfaces: 2 USB 2.0 ports, 2 USB 3.0 Type-C ports
5) Minimum Turning Radius: 0.8m
6) Servo: S20F 20kg torque digital servo
7) Motor: MD36 35W brushed DC motor
8) Motor Reduction Ratio: 1:27
9) Maximum Speed: 1.3m/s
10) Maximum Gradeability: 17°
11) Obstacle Crossing Capability: 30mm

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