<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the development of science and technology, the realization of intelligent vehicle autonomous driving has become a popular research. In order to realize the optimal autonomous driving behavior, this paper proposes a vehicle-splittable task offloading algorithm for edge offloading in the IoT environment based on the study of edge computing technology and the integration of 5G communication. On this basis, a task offloading model is built by combining Markov decision learning algorithm in order to improve the ability of real-time computation of intelligent vehicles in real driving environment and, at the same time, to realize the recording of dynamic driving behavior of the vehicle and the prediction of intelligent and safe paths in the dynamic IoV environment. Simulation experiments verify the performance of the VPEO algorithm. When there are varying numbers of vehicles and tasks, the VPEO algorithm performs better according to the experimental results. After 750 iterations, the VPEO algorithm achieves a 94% success rate when calculating tasks and stabilizes. In the intelligent warning experiments, the VPEO algorithm measured the average accuracy of lane offset distance, the average judgment correctness of vehicle class, and vulnerable traffic participant in relation to the position of the lane line were 84.66%, 92.26% and 94.69%, respectively. The VPEO algorithm can perform real-time calculations of intelligent driving information, and warnings about intelligent driving safety can be provided.</p>
</abstract>

With the development of science and technology, the realization of intelligent vehicle autonomous driving has become a popular research. In order to realize the optimal autonomous driving behavior, this paper proposes a vehicle-splittable task offloading algorithm for edge offloading in the IoT environment based on the study of edge computing technology and the integration of 5G communication. On this basis, a task offloading model is built by combining Markov decision learning algorithm in order to improve the ability of real-time computation of intelligent vehicles in real driving environment and, at the same time, to realize the recording of dynamic driving behavior of the vehicle and the prediction of intelligent and safe paths in the dynamic IoV environment. Simulation experiments verify the performance of the VPEO algorithm. When there are varying numbers of vehicles and tasks, the VPEO algorithm performs better according to the experimental results. After 750 iterations, the VPEO algorithm achieves a 94% success rate when calculating tasks and stabilizes. In the intelligent warning experiments, the VPEO algorithm measured the average accuracy of lane offset distance, the average judgment correctness of vehicle class, and vulnerable traffic participant in relation to the position of the lane line were 84.66%, 92.26% and 94.69%, respectively. The VPEO algorithm can perform real-time calculations of intelligent driving information, and warnings about intelligent driving safety can be provided.

Smart Vehicle Driving Behavior Analysis Based on 5G, IoT and Edge Computing Technologies

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the development of science and technology, the realization of intelligent vehicle autonomous driving has become a popular research. In order to realize...

Smart Vehicle Driving Behavior Analysis Based on 5G, IoT and Edge Computing Technologies

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 22. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0113

Schlüsselwörter
5G network, Internet of Things, Edge computing, VPEO algorithm, Smart car driving

© 2024 Haoxuan Jin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Smart Vehicle Driving Behavior Analysis Based on 5G, IoT and Edge Computing Technologies

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 15. Dez. 2023

Akzeptiert: 22. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0113

Schlüsselwörter5G network, Internet of Things, Edge computing, VPEO algorithm, Smart car driving

© 2024 Haoxuan Jin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
5G network, Internet of Things, Edge computing, VPEO algorithm, Smart car driving