Trustworthiness of Self-Driving Vehicles for Intelligent Transportation Systems in Industry Applications

Chowdhury, Abdullahi; Karmakar, Gour; Islam, Syed

doi:10.1109/tii.2020.2987431

Cited by 46 publications

(18 citation statements)

References 24 publications

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“…Besides, outstanding defense approaches in the last decade were also discussed in the paper [11]. According to that, a fused model from CertainLogic and Dempster-Shafer Theory has been introduced by [6] to measure the trust value of each participated vehicle in the system considering On-Board Unit components, GPS data, and safety messages.…”

Section: Related Workmentioning

confidence: 99%

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Nguyen¹,

Nguyen²,

Leppänen³

et al. 2022

Preprint

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Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.

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Section: Related Workmentioning

confidence: 99%

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Nguyen¹,

Nguyen²,

Leppänen³

et al. 2022

Preprint

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show abstract

“…Therefore, to promote intelligent vehicles, it is crucial to building confidence among users toward their use. Several studies (e.g., Wang et al, 2019 ; Chowdhury et al, 2021 ; Manfreda and Ljubi, 2021 ) have found support for the influence of attitude on the acceptance of intelligent vehicles. These studies indicate that the direct experience (such as a test ride on intelligent vehicles) induces a positive attitude in participants toward their acceptance.…”

Section: Literature Review and Theoretical Backgroundmentioning

confidence: 99%

An Empirical Study on Drivers’ Willingness to Use Automatic Features of Intelligent Vehicles: A Psychological Empowerment Perspective

Gupta

Zhou

2021

Front. Psychol.

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With the advancement in AI and related technologies, we are witnessing more remarkable use of intelligent vehicles. Intelligent vehicles use smart automatic features that make travel happier, safer, and efficient. However, not many studies examine their adoption or the influence of intelligent vehicles on user behavior. In this study, we specifically examine how intelligent vehicles’ sensing and acting abilities drive their adoption from the lens of psychological empowerment theory. We identify three dimensions of users’ perceived empowerment (perceived cognitive empowerment, perceived emotional empowerment, and perceived behavioral empowerment). Based on this theory, we argue that product features (sensing and acting in intelligent vehicles) empower users to use the product. Our proposed model is validated by an online survey of 312 car owners who are familiar with driving conditions, the results of this study reveal that driver’s perceived empowerment is vital for using automatic features of intelligent vehicles. Theoretically, this study combines the concept of empowerment with the intelligent-driving scenario and reasonably explains the mechanism of the intelligence of vehicles on users’ behavior intention.

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“…The situation becomes worse in case two conflicting opinions arise about the same device. Uncertainty and the combining ability of DST 7 aids the members of an edge-based IoT network compute a final trust value and re-assess its decision using the uncertainty in the computed trust values. Also when a particular edge device in an IoT resumes the awake mode after the sleep mode, the strength of the sensed information may weaken.…”

Section: Introductionmentioning

confidence: 99%

DEIT: Dempster Shafer Theory‐based edge‐centric Internet of Things‐specific trust model

Bhargava

Verma

2021

Trans Emerging Tel Tech

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The potential benefits of edge-centric Internet of Things (IoT) come with increased security threats and because the edge devices may be compromised into sending or forwarding wrong information. To negate the effect of wrong information on inference, such messages need to be identified. Identification and dropping of messages in the network as early as possible would minimize the communication overhead. Trust values of nodes help in the early identification of such messages. In this work, the Dempster Shafer Theory (DST)-based edge-centric IoT-specific Trust Model (DEIT) is designed to compute trust to include the specific characteristics of IoT. Edge servers use DEIT to compute trust value of edge devices in the network. In DEIT, DST combines the available trust messages. DIET also uses the degradation variable, credibility variable, punishment variable, re-compensating variable, and pardoning variable to compute final trust value. The computed trust value assists the edges in taking correct decisions. Simulation results demonstrate that it can immediately capture two major types of misbehavior, namely, information drop and modification under both sparse and dense traffic conditions. Through scenarios in the results section, the depiction of various possible misbehaviors has been discussed. Scenario 1 and 2 discuss a situation where a malicious edge device misbehaves in two ways, namely, dropping and modification of information, respectively.In scenario 3 and 4, situations are discussed when the trust opinion of the trusted and distrusted devices are neutralized with time using the degradation factor to illustrate and compare changes in trust values in two different situation.

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Trustworthiness of Self-Driving Vehicles for Intelligent Transportation Systems in Industry Applications

Cited by 46 publications

References 24 publications

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

An Empirical Study on Drivers’ Willingness to Use Automatic Features of Intelligent Vehicles: A Psychological Empowerment Perspective

DEIT: Dempster Shafer Theory‐based edge‐centric Internet of Things‐specific trust model

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