Conflict Management Algorithms Development Using the Automated Framework for Autonomous Vehicles

Sziroczák, Dávid; Rohács, Dániel

doi:10.3311/bmezalazone2022-019

Cited by 1 publication

(2 citation statements)

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“…Therefore, FDI attacks were implemented and injected into the sensor responsible for measuring the position of the ego car. The equation of the FDI attack is shown in Equation (1). The D act refers to the relative distance between the ego car and the lead car measured by the sensor.…”

Section: Implementation Of Cyberattackmentioning

confidence: 99%

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Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model

et al. 2022

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Technological advancement has transformed traditional vehicles into autonomous vehicles. Autonomous vehicles play an important role since they are considered an essential component of smart cities. The autonomous vehicle is an intelligent vehicle capable of maintaining safe driving by avoiding crashes caused by drivers. Unlike traditional vehicles, which are fully controlled and operated by humans, autonomous vehicles collect information about the outside environment using sensors to ensure safe navigation. Autonomous vehicles reduce environmental impact because they usually use electricity to operate instead of fossil fuel, thus decreasing the greenhouse gasses. However, autonomous vehicles could be threatened by cyberattacks, posing risks to human life. For example, researchers reported that Wi-Fi technology could be vulnerable to cyberattacks through Tesla and BMW autonomous vehicles. Therefore, further research is needed to detect cyberattacks targeting the control components of autonomous vehicles to mitigate their negative consequences. This research will contribute to the security of autonomous vehicles by detecting cyberattacks in the early stages. First, we inject False Data Injection (FDI) attacks into an autonomous vehicle simulation-based system developed by MathWorks. Inc. Second, we collect the dataset generated from the simulation model after integrating the cyberattack. Third, we implement an intelligent symmetrical anomaly detection method to identify false data cyber-attacks targeting the control system of autonomous vehicles through a compromised sensor. We utilize long short-term memory (LSTM) deep networks to detect False Data Injection (FDI) attacks in the early stage to ensure the stability of the operation of autonomous vehicles. Our method classifies the collected dataset into two classifications: normal and anomaly data. The experimental result shows that our proposed model’s accuracy is 99.95%. To this end, the proposed model outperforms other state-of-the-art models in the same study area.

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Section: Implementation Of Cyberattackmentioning

confidence: 99%

“…Recently, the market value for autonomous vehicles (AVs) has been growing rap-idly, and it is estimated to reach 100 billion in the near future [1]. In India, fossil fuel cars will be prohibited by 2030 because of their negative impact on the environment [2].…”

Section: Introductionmentioning

confidence: 99%