Concerns on the Differences Between AI and System Safety Mindsets Impacting Autonomous Vehicles Safety

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

doi:10.1007/978-3-319-99229-7_42

Cited by 7 publications

(10 citation statements)

References 14 publications

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“…For example, only few studies were found about the three topics positioning AI as a potential source of negative impact on safety -Fault Forecasting, Ethics and Policies, and Dependability and Trust. When combined to the other findings reported by the present study, it confirms the impressions formed during an exploratory research of the literature [1]. It reinforces the perception that the field of AI and AV is not heavily influenced by the safety engineering culture yet.…”

Section: Final Remarkssupporting

confidence: 91%

“…It reinforces the perception that the field of AI and AV is not heavily influenced by the safety engineering culture yet. In fact, the studies published about this current topic seem to be more driven by computation-related domains, with no tradition regarding safety culture, than other fields that are much more connected to safety in critical systems [1].…”

Section: Final Remarksmentioning

confidence: 99%

“…This study is the first, as far as we are aware, to map and to organize the related literature and to provide a complete view of the aspects related to both visions, and to subsidize future studies. A preliminary study on the concerns about the differences between AI and system safety mindsets impacting AV safety was published in [1]. In this non-convergent context, this paper presents a systematic literature review (SLR) aiming to present a clear picture of the state of the art of the literature in AI on AVs safety.…”

Section: Introductionmentioning

confidence: 99%

“…Although safety is a mandatory characteristic to AV, and although the researchers seem to agree on the importance of AI This work was supported in part by the Research, Development and innovation Center, Ericsson Telecommunication S.A., Brazil. 1 Safety Analysis Group from Polytechnic School of University of São Paulo, São Paulo, Brazil (e-mail: alexandremoreiranascimento@gmail.com). applied to autonomous vehicles, they seem to disagree on the AIs impact on AV safety.…”

Section: Introductionmentioning

confidence: 99%

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

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.

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Section: Final Remarkssupporting

confidence: 91%

Section: Final Remarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Recent times have seen the growing deployment of machine learning models and deep neural networks in many mission-critical tasks such as medical image diagnosis [6], banking systems [2] and autonomous vehicles [14]. Most data available to models in the real world is often out of the distribution of the data on which the models were trained and tested.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data

Lakara¹,

Bhandari²,

Pratinav³

et al. 2021

Preprint

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Most machine learning models operate under the assumption that the training, testing and deployment data is independent and identically distributed (i.i.d.). This assumption doesn't generally hold true in a natural setting. Usually, the deployment data is subject to various types of distributional shifts. The magnitude of a model's performance is proportional to this shift in the distribution of the dataset. Thus it becomes necessary to evaluate a model's uncertainty and robustness to distributional shifts to get a realistic estimate of its expected performance on real-world data. Present methods to evaluate uncertainty and model's robustness are lacking and often fail to paint the full picture. Moreover, most analysis so far has primarily focused on classification tasks. In this paper, we propose more insightful metrics for general regression tasks using the Shifts Weather Prediction Dataset. We also present an evaluation of the baseline methods using these metrics.

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Evaluation of Autonomous Vehicle Control Strategies Using Resilience Engineering

Rojas,

Bradley,

Asher

2023

Machine Learning and Optimization Techniques for Automotive Cyber-Physical Systems

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Concerns on the Differences Between AI and System Safety Mindsets Impacting Autonomous Vehicles Safety

Cited by 7 publications

References 14 publications

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

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

Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data

Evaluation of Autonomous Vehicle Control Strategies Using Resilience Engineering

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