A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento, Alexandre; Vismari, Lúcio F.; Molina, Caroline; Cugnasca, Paulo Sérgio; Camargo, João Batista; Almeida, Jorge Rady de; Inam, Rafia; Fersman, Elena; Marquezini, M. V.; Hata, Alberto Yukinobu

doi:10.1109/tits.2019.2949915

Cited by 71 publications

(13 citation statements)

References 66 publications

(275 reference statements)

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“…[82] (integrated network security for terrestrial and aerial transportation), ref. [83] (a systematic literature review on AI in UAV safety), etc. Security aspects are very broad and are not typically a part of the design optimization of the space or ground segments, as there are many security solutions that can be devised in the post-design stage, such as using multi-system multi-frequency receivers, using various encryption methods or authentication signals, etc.…”

Section: High-speed Scenarios Based On Leo Satellites For Future Auto...mentioning

confidence: 99%

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Çelikbilek

Saleem

Ferre

et al. 2022

Sensors

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Future autonomous transportation is one of the most demanding application areas in terms of connectivity, as it has to simultaneously meet stringent criteria that do not typically go hand in hand, such as high throughput, low latency, high coverage/availability, high positioning and sensing accuracies, high security and robustness to interferences, etc. In order to meet the future demands of challenging applications, such as applications relying on autonomous vehicles, terrestrial networks are no longer sufficient and are to be augmented in the future with satellite-based networks. Among the emerging satellite networks, Low Earth Orbit (LEO) networks are able to provide advantages over traditional Medium Earth Orbit (MEO) and Geo-Stationary Earth Orbit (GEO) networks in terms of signal latency, cost, and performance. Nevertheless, several challenges exist in LEO system design, which have not been fully addressed in the existing literature. In particular, the problem of LEO-system optimization of design parameters is a multi-dimensional problem with many aspects to be considered. This paper offers a comprehensive survey of the LEO-system design parameters, of the challenges in LEO system design process, and of the optimization methods for satellite communication, positioning, and sensing applications, as well as a summarizing discussion on the design considerations for LEO-based networks to support future autonomous transportation.

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Section: High-speed Scenarios Based On Leo Satellites For Future Auto...mentioning

confidence: 99%

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Çelikbilek

Saleem

Ferre

et al. 2022

Sensors

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“…The relationships between ML, security and privacy for ECAS vehicles is illustrated in Figure 9. Nascimento et al (2020) provide a systematic literature review to offer an overview of AI's impact on autonomous vehicle safety. The paper argues that the literature reviewed proposes conceptual models and frameworks that bring essential contributions, but they are mostly not tested in actual set-ups.…”

Section: Safety and Securitymentioning

confidence: 99%

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

Vermesan

John

Pype

et al. 2021

Front. Future Transp.

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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.

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“…The inclusion exclusion criteria are applied in two stages, a strategy adopted from [16]. First stage is coarse-grained inclusion exclusion stage, in which articles are excluded if even one of the exclusion criteria is satisfied by the title or abstract of that article.…”

Section: Inclusion Exclusion Criteriamentioning

confidence: 99%

Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review

Tahir

Alexander

2020

2020 IEEE International Conference on Artificial Intelligence Testing (AITest)

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Self-driving Autonomous Vehicles (SAVs) are gaining more interest each passing day by the industry as well as the general public. Tech and automobile companies are investing huge amounts of capital in research and development of SAVs to make sure they have a head start in the SAV market in the future. One of the major hurdles in the way of SAVs making it to the public roads is the lack of confidence of public in the safety aspect of SAVs. In order to assure safety and provide confidence to the public in the safety of SAVs, researchers around the world have used coverage-based testing for Verification and Validation (V&V) and safety assurance of SAVs. The objective of this paper is to investigate the coverage criteria proposed and coverage maximizing techniques used by researchers in the last decade up till now, to assure safety of SAVs. We conduct a Systematic Literature Review (SLR) for this investigation in our paper. We present a classification of existing research based on the coverage criteria used. Several research gaps and research directions are also provided in this SLR to enable further research in this domain. This paper provides a body of knowledge in the domain of safety assurance of SAVs. We believe the results of this SLR will be helpful in the progression of V&V and safety assurance of SAVs.

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A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Cited by 71 publications

References 66 publications

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation

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

Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review

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