Research on agricultural vehicle safety warning system based on lidar

Intelligent agricultural vehicles have been widely used in the process of farming and harvesting in the field, which has brought great convenience to agricultural production. However, there are also safety issues such as accidental collision of agricultural vehicles or other agricultural machinery during operation. The use of sensing technology for the timely and accurate detection and pre-warning of obstacles during the operation of agricultural machinery is critically important for ensuring safety. In this paper, a two-dimensional lidar is used to detect obstacles in front of tractors with the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm and the Minimum Cost Maximum Flow algorithm(MCMF). A method to judge whether the obstacle is static or dynamic and a classification model of different security warning levels for obstacles in different states is proposed. Actual vehicle tests were conducted, with static obstacles tested repeatedly, and dynamic obstacles tested at different directions and speeds. The results showed that the overall average warning accuracy rate is 89.95%. Prediction results were robust for obstacles in different states, indicating that this system is able to ensure the safety of agricultural vehicles during their operation and promoted the development of agricultural mechanization.