Autonomous driving paper index
Computer Vision-Driven Navigation and Decision Making in Autonomous Industrial Vehicles
One-line summary
The use of autonomous systems to increase productivity, lessen reliance on manual labor, and reduce operational errors in material handling has increased due to the quick development of industrial automation.
Engineering notes
Key topics: autonomous driving, bird's eye view, bev, camera-based perception, perception, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The use of autonomous systems to increase productivity, lessen reliance on manual labor, and reduce operational errors in material handling has increased due to the quick development of industrial automation. In order to accomplish these goals, autonomous industrial vehicles, or AIVs, are essential because they make it possible for dependable cargo transportation in factories, warehouses, and other semi-structured settings. Robust navigation and adaptive decision-making, however, continue to be major obstacles, especially in busy and dynamic industrial environments. A computer vision-based framework for autonomous industrial vehicle navigation and decision-making is presented in this article. The suggested system uses camera-based perception to create depth maps, identify obstacles in real time, and extract road masks and uses this data to generate Bird's Eye View (BEV) maps to calculate safe routes, prevent collisions, and improve navigation by combining adaptive decision-making algorithms with image processing techniques. This article presents a computer vision-based framework for autonomous industrial vehicle navigation and decision-making. The proposed system generates depth maps, detects obstacles in real time, and extracts road masks using camera-based perception. The vehicle can calculate safe routes, prevent collisions, and dynamically optimize navigation choices by combining adaptive decision-making algorithms with image processing techniques. The framework has been tested in controlled environments to assess performance and has been implemented using Python with OpenCV, NumPy, and Matplotlib.
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