Formal  Model and Analysis for the Random Event in the Intelligent Car with Stochastic Petri Nets and Z. (13th October 2022)

• Record Type: Journal Article
• Authors: Liu, Yang; Fan, Yingqi; Huang, Darong; Mi, Bo; Huang, Liyuan
• Other Names: Chen Chen Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-10-13
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/3288308
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : With the continuous development of science and technology, people's lifestyle becomes more and more intelligent, especially in intelligent transportation. However, running in a random environment, safety can be affected by various factors during operation. For an intelligent car, guaranteeing its safety in operation is important to the passengers in the vehicle. So, it is vital to verify the safety of the system of the smart car. This study proposes an integration formal method with stochastic Petri nets and Z (SPZN). Stochastic Petri nets can better simulate the occurrence of random events in the driving process of intelligent cars. With the advantages of the frame structure of Z language, the concurrent process and state before and after the system at different times can be better described. In addition, the frame structure of Z language can solve the problem of state explosion in Petri nets. Using this method, the random events that may occur during the operation can be formally modeled, and the subsequent behavior of the vehicle can be analyzed and predicted effectively. Using the reinforcement learning, the parameter λ in the stochastic Petri nets can be optimized, which can reduce the probability of bad states and ensure the stability and security of the system. Moreover, a case study of the intelligent car modeled by stochastic Petri nets and Z is given. The results show that it can improve the safety and effectiveness of the smart vehicle driving system.