Vehicle Path Planning with Multicloud Computation Services


  • Po-Tong Wang Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan, Taiwan
  • Shao-Yu Lin Department of Computer Science, National Taipei University of Education, Taipei, Taiwan
  • Jia-Shing Sheu Department of Computer Science, National Taipei University of Education, Taipei, Taiwan



computer vision, scene recognition, cloud computing, object detection


With the development of artificial intelligence, public cloud service platforms have begun to provide common pretrained object recognition models for public use. In this study, a dynamic vehicle path-planning system is developed, which uses several general pretrained cloud models to detect obstacles and calculate the navigation area. The Euclidean distance and the inequality based on the detected marker box data are used for vehicle path planning. Experimental results show that the proposed method can effectively identify the driving area and plan a safe route. The proposed method integrates the bounding box information provided by multiple cloud object detection services to detect navigable areas and plan routes. The time required for cloud-based obstacle identification is 2 s per frame, and the time required for feasible area detection and action planning is 0.001 s per frame. In the experiments, the robot that uses the proposed navigation method can plan routes successfully.


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How to Cite

P.-T. Wang, S.-Y. Lin, and J.-S. Sheu, “Vehicle Path Planning with Multicloud Computation Services”, Adv. technol. innov., vol. 6, no. 4, pp. 213–221, Aug. 2021.




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