Blockchain-Autonomous Driving Systems

2021 ◽  
pp. 87-114
Author(s):  
P. Lalitha Surya Kumari
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Intelligent Vehicle ◽  
Privacy And Security ◽  
Trust Network ◽  
Vehicle Communication ◽  
Blockchain Technology ◽  
Vehicle Data

Blockchain is the upcoming new information technology that could have quite a lot of significant future applications. In this chapter, the communication network for the reliable environment of intelligent vehicle systems is considered along with how the blockchain technology generates trust network among intelligent vehicles. It also discusses different factors that are effecting or motivating automotive industry, data-driven intelligent transportation system (D2ITS), structure of VANET, framework of intelligent vehicle data sharing based on blockchain used for intelligent vehicle communication and decentralized autonomous vehicles (DAV) network. It also talks about the different ways the autonomous vehicles use blockchain. Block-VN distributed architecture is discussed in detail. The different challenges of research and privacy and security of vehicular network are discussed.

scholarly journals A Review of the Transformation of Road Transport Systems: Are We Ready for the Next Step in Artificially Intelligent Sustainable Transport?

2019 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Umair Hasan ◽  
Andrew Whyte ◽  
Hamad Al Jassmi
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Critical Evaluation ◽  
Value Added ◽  
Pollutant Emissions ◽  
Transport Systems ◽  
Privacy And Security ◽  
Pilot Studies ◽  
Empirical Modelling

Mobility is experiencing a revolution, as advanced communications, computers with big data capacities, efficient networks of sensors, and signals, are developing value-added applications such as intelligent spaces and autonomous vehicles. Another new technology that is both promising and might even be pervasive for faster, safer and more environmentally-friendly public transport (PT) is the development of autonomous vehicles (AVs). This study aims to understand the state of the current research on the artificially intelligent transportation system (ITS) and AVs through a critical evaluation of peer-reviewed literature. This study’s findings revealed that the majority of existing research (around 82% of studies) focused on AVs. Results show that AVs can potentially reduce more than 80% of pollutant emissions per mile if powered by alternate energy resources (e.g., natural gas, biofuel, electricity, hydrogen cells, etc.). Not only can private vehicle ownership be cut down by bringing in ridesharing but the average vehicle miles travelled (VMT) should also be reduced through improved PT. The main benefits of AV adoption were reported in the literature to be travel time, traffic congestion, cost and environmental factors. Findings revealed barriers such as technological uncertainties, lack of regulation, unawareness among stakeholders and privacy and security concerns, along with the fact that lack of simulation and empirical modelling data from pilot studies limit the application. AV–PT was also found to be the most sustainable strategy in dense urban areas to shift the heavy trip load from private vehicles.

scholarly journals Ethical Challenges and Countermeasures of Autonomous Vehicles

2021 ◽  
Vol 233 ◽  
pp. 04016
Author(s):  
Xi Zhu ◽  
Zhiqiang Gu ◽  
Zhen Wang
Keyword(s):  
21St Century ◽  
Autonomous Vehicles ◽  
New Technologies ◽  
Ethical Dilemmas ◽  
Autonomous Driving ◽  
Intelligent Vehicle ◽  
Connected Vehicles ◽  
Ethical Challenges ◽  
Autonomous Cars ◽  
Vehicle Market

Autonomous driving is one of the greatest new technologies in the 21st century, but to a certain extent, it has caused conflicts in bioethical standards and the ownership of accident responsibility. For the ethical dilemma of autonomous cars, every countrie currently does not have a perfect solution. First of all, this paper predicts the development of intelligent connected vehicles through the analysis of the domestic and foreign intelligent vehicle market. Then lists and analyzes the legal and countermeasures of ethical dilemmas in various countries. According to the knowledge of psychology and ethics, this paper puts forward a way to solve this problem which is more in line with the development of industry and ethics.

Blockchain Security Using Secure Multi-Party Computation

2021 ◽  
pp. 178-195
Author(s):  
Jenila Livingston L. M. ◽  
Ashutosh Satapathy ◽  
Agnel Livingston L. G. X. ◽  
Merlin Livingston L. M.
Keyword(s):  
Autonomous Vehicles ◽  
Cooperative Control ◽  
Personal Information ◽  
Autonomous Vehicle ◽  
Privacy Preserving ◽  
Human Interaction ◽  
Security And Privacy ◽  
Blockchain Technology ◽  
Vehicle Data ◽  
Real Model

In secure multi-party computation (SMC), multiple distributed parties jointly carry out the computation over their confidential data without compromising data security and privacy. It is a new emerging cryptographic technique used in huge applications such as electronic auction bidding, electronic voting, protecting personal information, secure transaction processing, privacy preserving data mining, and privacy preserving cooperative control of connected autonomous vehicles. This chapter presents two model paradigms of SMC (i.e., ideal model prototype and real model prototype). It also deals with the type and applications of adversaries, properties, and the techniques of SMC. The three prime types of SMC techniques such as randomization, cryptographic techniques using oblivious transfer, and anonymization methods are discussed and illustrated by protective procedures with suitable examples. Finally, autonomous vehicle interaction leveraged with blockchain technology to store and use vehicle data without any human interaction is also discussed.

scholarly journals Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

2021 ◽  
Vol 2 ◽  
Author(s):  
Ovidiu Vermesan ◽  
Reiner John ◽  
Patrick Pype ◽  
Gerardo Daalderop ◽  
Kai Kriegel ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Gap Analysis ◽  
Autonomous Driving ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
The Internet ◽  
High Definition ◽  
Data Infrastructure ◽  
Traffic Lights ◽  
Digital Infrastructure

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.

A V2V System Module for Inter Vehicle Communication

2016 ◽  
Vol 850 ◽  
pp. 16-22
Author(s):  
Özge Özdemir ◽  
İslam Kılıç ◽  
Ahmet Yazıcı ◽  
Kemal Özkan
Keyword(s):  
Autonomous Vehicles ◽  
Communication Systems ◽  
Autonomous Driving ◽  
Vehicle Communication ◽  
V2v Communication ◽  
Camera Systems ◽  
Vehicle To Vehicle ◽  
System Module ◽  
Vehicle To Infrastructure ◽  
Vehicle Sensors

An advanced driver assistance system (ADAS) is the premium technology for autonomous driving. It uses data from vision/camera systems, data from in vehicle sensors, and data from vehicle-to-vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication systems. The next generation systems even autonomous vehicles are expected to use the V2V information to increase the safety for non-line of sight environments. Exchanging some data like vehicle position, speed, status etc., helps to the driver about potential problems, or to avoid collisions. In this paper, a V2V communication system module is designed and tested on the vehicles.

scholarly journals MMW Radar-Based Technologies in Autonomous Driving: A Review

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7283
Author(s):  
Taohua Zhou ◽  
Mengmeng Yang ◽  
Kun Jiang ◽  
Henry Wong ◽  
Diange Yang
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Radar Data ◽  
Intelligent Vehicles ◽  
Data Types ◽  
Automated Driving ◽  
Driving Assistance ◽  
High Level

With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

scholarly journals MedShard: Electronic Health Record Sharing Using Blockchain Sharding

2021 ◽  
Vol 13 (11) ◽  
pp. 5889
Author(s):  
Faiza Hashim ◽  
Khaled Shuaib ◽  
Farag Sallabi
Keyword(s):  
Healthcare System ◽  
Small Groups ◽  
Network Performance ◽  
Communication Overhead ◽  
Privacy And Security ◽  
Health Records ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Simulation Results ◽  
Electronic Health

Electronic health records (EHRs) are important assets of the healthcare system and should be shared among medical practitioners to improve the accuracy and efficiency of diagnosis. Blockchain technology has been investigated and adopted in healthcare as a solution for EHR sharing while preserving privacy and security. Blockchain can revolutionize the healthcare system by providing a decentralized, distributed, immutable, and secure architecture. However, scalability has always been a bottleneck in blockchain networks due to the consensus mechanism and ledger replication to all network participants. Sharding helps address this issue by artificially partitioning the network into small groups termed shards and processing transactions parallelly while running consensus within each shard with a subset of blockchain nodes. Although this technique helps resolve issues related to scalability, cross-shard communication overhead can degrade network performance. This study proposes a transaction-based sharding technique wherein shards are formed on the basis of a patient’s previously visited health entities. Simulation results show that the proposed technique outperforms standard-based healthcare blockchain techniques in terms of the number of appointments processed, consensus latency, and throughput. The proposed technique eliminates cross-shard communication by forming complete shards based on “the need to participate” nodes per patient.

Female Voice Agents in Fully Autonomous Vehicles Are Not Only More Likeable and Comfortable, But Also More Competent

2020 ◽  
Vol 64 (1) ◽  
pp. 1033-1037
Author(s):  
Jiayuan Dong ◽  
Emily Lawson ◽  
Jack Olsen ◽  
Myounghoon Jeon
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Preference Ranking ◽  
Uncanny Valley ◽  
Tracking Data ◽  
Effective Solution ◽  
User Perception ◽  
Future Studies ◽  
Underlying Mechanisms ◽  
The Voice

Driving agents can provide an effective solution to improve drivers’ trust in and to manage interactions with autonomous vehicles. Research has focused on voice-agents, while few have explored robot-agents or the comparison between the two. The present study tested two variables - voice gender and agent embodiment, using conversational scripts. Twenty participants experienced autonomous driving using the simulator for four agent conditions and filled out subjective questionnaires for their perception of each agent. Results showed that the participants perceived the voice only female agent as more likeable, more comfortable, and more competent than other conditions. Their final preference ranking also favored this agent over the others. Interestingly, eye-tracking data showed that embodied agents did not add more visual distractions than the voice only agents. The results are discussed with the traditional gender stereotype, uncanny valley, and participants’ gender. This study can contribute to the design of in-vehicle agents in the autonomous vehicles and future studies are planned to further identify the underlying mechanisms of user perception on different agents.

scholarly journals Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3783
Author(s):  
Sumbal Malik ◽  
Manzoor Ahmed Khan ◽  
Hesham El-Sayed
Keyword(s):  
Autonomous Vehicles ◽  
Communication Technologies ◽  
Autonomous Driving ◽  
Use Cases ◽  
Cooperative Driving ◽  
Vehicle To Vehicle ◽  
Future Challenges ◽  
Vehicle To Infrastructure ◽  
High Level ◽  
Intersection Management

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

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