1. Core Technologies

Autonomous Driving:

   - Environmental Perception: GPS, LiDAR, cameras, millimeter-wave radar, etc., used for identifying terrain, crops, and obstacles.

   - Path Planning: SLAM algorithms, AI decision systems, optimizing operation paths.

   - Control Systems: Electric/hydraulic actuators, precisely controlling steering, speed, and operation tools.

Remote Driving:

   - Low-latency Communication: 5G, satellite communication ensuring real-time video transmission and control command issuance.

   - Human-Machine Interface: VR/AR assisted operations, multi-screen monitoring of agricultural machinery status and environment.

2. Application Scenarios

Autonomous Driving:

  - Large-scale field seeding, fertilization, and harvesting.

  - UAV precise pesticide spraying.

  - 24-hour continuous operation.

Remote Driving:

  - Handling complex terrains or sudden obstacles (e.g., remote takeover for obstacle avoidance).

  - Operations in high-risk environments (e.g., pesticide spraying, fire monitoring).

  - Coordinated scheduling of multiple agricultural machines (one-control-multiple-machines mode).

3. Advantages

1) Efficiency Improvement

   - Reduced reliance on human labor, extended operation time.

   - Precise operations reduce seed and fertilizer waste (e.g., variable rate technology).

2) Enhanced Safety  

   - Remote operations reduce personnel exposure to harsh environments.

3) Flexibility  

   - Autonomous driving handles routine tasks, remote driving addresses sudden needs.