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— 葡萄酒 | 威士忌 | 白兰地 | 啤酒 —
— 葡萄酒 | 威士忌 | 白兰地 | 啤酒 —
The autonomous tractor application market is rapidly growing as self-driving technologies continue to advance and mature. More and more enterprises and organizations are investing in and promoting the application of autonomous tractors. Autonomous tractors have tremendous potential in the logistics and transportation industries, as they can improve efficiency, reduce costs, and provide safer and more reliable transportation services.
The development of the autonomous tractor application market is influenced by several key factors. First is the maturity and reliability of the technology. Autonomous tractors rely on advanced sensors, artificial intelligence, and communication technologies to perceive and process data from the surrounding environment in real-time and make accurate decisions. Secondly, the establishment of regulations and policies is crucial. Autonomous tractors must meet traffic safety and regulatory requirements, and therefore, governments need to develop relevant rules and policies to ensure their safety and compliance.
Vehicle-side Perception: Multiple sensors, such as high-definition cameras, LiDAR, and GNSS/IMU systems, are used to acquire basic data. Through artificial intelligence and deep learning algorithms, the raw data from cameras and LiDAR is processed to detect fixed and moving obstacles.
Network Communication: 1) An INDUSTRIAL 5G ROUTER with multiple Ethernet ports is used to solve the network channel problem, with a SIM card installed. 2) The system is equipped with the Ntrip protocol, which can be configured with differential accounts from different manufacturers to obtain precise GNSS positioning information.
1) The Operations Monitoring System: Remotely view the real-time driving status of vehicles, remote video monitoring, abnormal notifications, and remote personnel takeover.
2) The Operations Management System: Automatically generate vehicle reports, operations reports, and route reports.
3) The Data Analysis System: Optimize operating time and routes based on historical and driving path data.
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