Security Analysis of Merging Control for Connected and Automated Vehicles

Jarouf, Abdulah; Meskin, Nader; Al-Kuwari, Saif; Shakerpour, Mohammad; Cassanderas, Christos G.

doi:10.1109/iv51971.2022.9827242

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Secure communication protocols: During data transit, the adoption of secure communication protocols is integral. Protocols such as transport layer security (TLS) and Internet protocol security (IPSec) deploy robust encryption and authentication mechanisms to maintain data confidentiality and integrity [185,186].…”

Section: Privacy Preservation Techniques In Vehicular Communicationsmentioning

confidence: 99%

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

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Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

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Section: Privacy Preservation Techniques In Vehicular Communicationsmentioning

confidence: 99%

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

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show abstract

“…So, driving factors, especially inappropriate driving behavior caused by emotions, need to be considered when developing vehicle assistance systems. At the same time, with the rise of intelligent vehicles, people prefer that the vehicle can provide assistance driving functions that detect the current state of the driver and automatically correct the driving behavior [3,4]. Therefore, it is very important to establish a prediction model based on emotions to improve the safety of vehicle driving and meet people's needs for intelligent vehicle assistance functions.…”

Section: Introductionmentioning

confidence: 99%

Driving Violation Prediction Based on an Emotional Style Transfer Network

Wang

2023

Sustainability

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Emotions are closely related to driving behavior, and drivers with different emotions have different degrees of bad driving behavior. In order to explore the relationship between emotions and driving violations, a prediction model based on an emotional style transfer network is proposed. First, inspired by the idea of generative adversarial networks (GAN), the eigenvalues of emotions are extracted. Secondly, the one-way propagation method of the GAN network is improved to cyclic generation, which avoids the problems of non-convergence and long periods in the data training process, improving the utilization of training data. Thirdly, a driving violation prediction model is designed. In this model, the emotion factors are designed as time-related sequences, and by improving the Long Short-Term Memory (LSTM) model, the encoding and decoding processes of the time-related sequences are added to form the context, which improves the accuracy of prediction. Finally, the experimental and simulation data show that the proposed model has significant advantages in loss value, accuracy rate, macro-average score, and other indicators. At the same time, an emotion-induction scheme is given to reduce the possibility of driving violations. Furthermore, the proposed model can provide a theoretical basis for the impact of emotions on driving safety.

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