Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Girdhar, Mansi; Hong, Junho; Moore, John M.

doi:10.1109/ojvt.2023.3265363

Cited by 23 publications

(10 citation statements)

References 210 publications

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“…These studies highlight the importance of tailored risk assessment frameworks to address the unique challenges of ATs. Frameworks leveraging driving simulators [19], trajectory planning [20], liability considerations [21], and cybersecurity [22] illustrate the diverse application and critical nature of comprehensive risk assessments. Collectively, this body of work highlights the need for interdisciplinary approaches to ensure the safe integration of ATs into the transportation ecosystem.…”

Section: Deployment Risk Assessmentsmentioning

confidence: 99%

Route Risk Index for Autonomous Trucks

Jones,

Bridgelall,

Tolliver

2024

Applied Sciences

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The proliferation of autonomous trucking demands a sophisticated understanding of the risks associated with the diverse U.S. interstate system. Traditional risk assessment models, while beneficial, do not adequately address the state and regional variations in factors that significantly impact the safety and efficiency of autonomous freight transport. This study addresses the problem by developing a composite risk index that evaluates the safety of U.S. interstate routes for autonomous trucking, considering both state and regional differences in traffic volumes, road conditions, safety records, and weather patterns. The potential for autonomous trucking to transform the freight industry necessitates a risk assessment model that is as dynamic and multifaceted as the system it aims to navigate. This work contributes a regionally sensitive risk index using GIS methodologies, integrating data from national databases, and applying statistical analysis to normalize risk factors. The findings reveal significant state and regional disparities in risk factors, such as the predominance of precipitation-related risks in the Southeast and traffic in the Far West. This work provides a targeted approach to risk assessment for policymakers and infrastructure planners and offers a strategic tool for logistics companies in optimizing autonomous trucking routes. The long-term benefit is a scalable model that can adapt to evolving data inputs and contribute to the broader application of risk assessment strategies in various domains.

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Section: Deployment Risk Assessmentsmentioning

confidence: 99%

Route Risk Index for Autonomous Trucks

Jones,

Bridgelall,

Tolliver

2024

Applied Sciences

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“…Similarly, Zhang et al (2021) [63] extended the Unified Theory of Technology Acceptance and Use model and found that risk expectation and consumer innovativeness are influential factors in the acceptance of autonomous vehicles. The development of autonomous vehicles has been facilitated by advances in artificial intelligence (AI) and machine learning (ML) technologies [66][67][68]. The cybersecurity of autonomous vehicles has also been a matter of debate.…”

Section: Literature Review Research Model and Hypothesesmentioning

confidence: 99%

Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future

Bilici,

Türkoğlu

2024

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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Autonomous (driverless) cars, which have entered the automotive industry with the developments in automotive and the advancement of artificial intelligence technologies, are rapidly finding a place in the marketing field. At this point, there are factors affecting consumers' concerns and willingness to use autonomous vehicles. In order to discover these factors, the readiness of consumers and the aspects in which they are ready for this technology are issues that need to be investigated. As a result of this situation, consumers' readiness to use autonomous vehicles, their attitudes toward using them, and their intentions to use them in the future are essential. This study aims to reveal the factors affecting consumers' attitudes and intentions towards using autonomous cars. Research data was collected via an online survey method. The convenience sampling method was used in the research. The research model was tested by structural equation modeling using Smart PLS. As a result of the research, it was found that discomfort and distrust dimensions significantly and negatively affected consumers' attitudes towards usage. It was found that the dimensions of optimism, innovativeness, and anthropomorphism significantly and positively affected consumers' attitudes toward use, and users' attitudes towards use significantly and positively affected their intention to use. The research results show that brands that put autonomous cars on the market should give importance to improvements in the dimensions of optimism, innovation, and anthropomorphism and should make improvements that will eliminate consumers' discomfort and insecurity.

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“…Then, we remove the predictions of the injected points from the predicted label set and calculate the accuracy. This can be mathematically expressed as Equation (10).…”

Section: Robustness Evaluation Metricsmentioning

confidence: 99%

“…The susceptibility of DL networks to adversarial attacks raises concerns regarding their use in safety-critical applications like AVs, as the security of AVs is correlated with the DL networks those AVs employ. As a result, adversarial attacks against AVs have attracted a lot of attention, and numerous studies were conducted to examine the adversarial vulnerabilities of AVs and defend against them [10,11].…”

Section: Introductionmentioning

confidence: 99%

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

Mahima,

Perera,

Anavatti

et al. 2023

Sensors

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Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks. This phenomenon has recently received increased interest in the field of autonomous vehicles and has been extensively researched on 2D image-based perception tasks and 3D object detection. However, the adversarial robustness of 3D LiDAR semantic segmentation in autonomous vehicles is a relatively unexplored topic. This study expands the adversarial examples to LiDAR-based 3D semantic segmentation. We developed and analyzed three LiDAR point-based adversarial attack methods on different networks developed on the SemanticKITTI dataset. The findings illustrate that the Cylinder3D network has the highest adversarial susceptibility to the analyzed attacks. We investigated how the class-wise point distribution influences the adversarial robustness of each class in the SemanticKITTI dataset and discovered that ground-level points are extremely vulnerable to point perturbation attacks. Further, the transferability of each attack strategy was assessed, and we found that networks relying on point data representation demonstrate a notable level of resistance. Our findings will enable future research in developing more complex and specific adversarial attacks against LiDAR segmentation and countermeasures against adversarial attacks.

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Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models

Cited by 23 publications

References 210 publications

Route Risk Index for Autonomous Trucks

Route Risk Index for Autonomous Trucks

Autonomous Vehicle Technology and Technology Acceptance: The Role of Technological Readiness on Consumers' Attitudes Towards Driverless Cars and Intention to Use in the Future

Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving

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