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Academic Journal of Computing & Information Science, 2022, 5(14); doi: 10.25236/AJCIS.2022.051420.

FPGA-based vehicle real-time path planning system


Kai Yuan, Guangming Li, Xiaojuan Liang

Corresponding Author:
Kai Yuan

School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, Shaanxi, 710021, China


In order to realize the requirements of low latency and low power consumption in real-time path planning for private vehicles, this paper designs an FPGA accelerator based on real-time road information and Dijkstra's shortest path algorithm, which is applied to the path planning system of vehicle edge computing. The system is designed using Xilinx High-Level Synthesis (HLS) compiler and is implemented in the programming logic of a Xilinx Zynq FPGA. The experiment is carried out on a city map, and the results show that the system has lower circuit area and power consumption, and its computing performance is 3.8 times that of ARM, which is suitable for edge computing platform.


intelligent transportation, edge computing, FPGA, path planning

Cite This Paper

Kai Yuan, Guangming Li, Xiaojuan Liang. FPGA-based vehicle real-time path planning system. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 14: 147-152. https://doi.org/10.25236/AJCIS.2022.051420.


[1] Ming, Y., Li, Y.Q., Zhang, Z.H. and Yan, W.Q. (2022) A survey of path planning algorithms for autonomous vehicles. Sae International Journal of Commercial Vehicles, 14, 97-109.

[2] Boeing, G. (2016) Osmnx: new methods for acquiring, constructing, analyzing, and visualizing complex street. Clinical Orthopaedics and Related Research, 65, 126–139.

[3] Ai, Y., Peng, M. and Zhang, K. (2017) Edge cloud computing technologies for internet of things: a primer. Digital Communications and Networks, 4, 77–86.

[4] Liu, Y., Li, Y., Niu, Y. and Jin, D.P. (2020) Joint optimization of path planning and resource allocation in mobile edge computing. IEEE Transactions on Mobile Computing, 19, 2129-2144.

[5] Ning, Z.L., Zhang, K.Y. and Wang, X.J. (2021) Intelligent edge computing in internet of vehicles: a joint computation offloading and caching solution. IEEE Transactions on Intelligent Transportation Systems, 22, 2212-2225.

[6] Issa, H.H. and Ahmed, S.M.E. (2019) FPGA implementation of floating point based cuckoo search algorithm. IEEE Access, 7, 134434-134447.

[7] Gebremichael, T., Ledwaba, L.P.I. and Eldefrawy, M.H. (2020) Security and privacy in the industrial internet of things: current standards and future challenges. IEEE Access, 8, 152351-152366.