Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions

Cunneen, Martin; Mullins, Martin; Murphy, Finbarr

doi:10.1080/08839514.2019.1600301

Cited by 74 publications

(39 citation statements)

References 39 publications

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“…Regulatory needs. The need for legal regulation of AD has been confirmed in various research articles, e.g., Cunneen et al [25] and Baumann et al [26]. Both stated that there cannot be a general regulation for AI-based development as it will be always related to its application context.…”

Section: State-of-the-artmentioning

confidence: 99%

A Moral Consensus Mechanism for Autonomous Driving: Towards a Law-compliant Basis of Logic Programming

Wanner¹,

Herm²,

Langer³

et al. 2020

WI2020 Zentrale Tracks

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Research into autonomous vehicles is making progress. While implementation is progressing through machine learning and efficient sensor technology, one key challenge remains dealing with moral disputes. In general, traffic requires for moral decisions that might even decide on the life or death of participants. While people make intuitive decisions in accidents, a decision of an autonomous vehicle is made already at the programming stage. Thus, a concrete handling for implementation is needed. Due to a lack of legislation, this is still missing and prevents car manufacturers from a practical solution. The paper at hand addresses this problem by presenting a consensus mechanism, combining moral convictions, legislation, and programming guidelines. Based on a study of dilemma situations, moral principles of the 'correct action' of autonomous vehicles are derived. Of four principles, we confirm one, reject two, and propose one for further research investigation to form a basis for jurisdictions.

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Section: State-of-the-artmentioning

confidence: 99%

A Moral Consensus Mechanism for Autonomous Driving: Towards a Law-compliant Basis of Logic Programming

Wanner¹,

Herm²,

Langer³

et al. 2020

WI2020 Zentrale Tracks

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“…Besides the significant achievements of DL in AVs, a large limitation of DL-based perception systems is the inadequate feedback of uncertainty. Cunneen et al [180] reviewed the challenges of AI based decision making, its risks, and societal benefits. Bayesian DL is the bridge between DL and Bayesian probability that offers principled risks analysis within DL.…”

Section: A) Energy-friendly Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Muhammad

Ullah

Lloret

et al. 2021

IEEE Trans. Intell. Transport. Syst.

262

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Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-theart strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

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“…Eventually, this mass of information acts as an input to the driving-related decision making of autonomous cars, hence aiming to replicate or even exceed the precision of human driver actions. One of the ultimate goals is to significantly reduce car accidents in traffic, however there are concerns regarding the ethical aspects (e. g. the Trolley Problem) and the trustworthiness of AI systems (Cunneen et al 2019, Nascimento et al 2019, OECD 2019. AI is currently in an infant state, providing researchers and experts with a multitude of questions and challenges to consider (OECD 2019).…”

Section: The Fourth Industrial Revolution: the Growing Use Of Digitalmentioning

confidence: 99%

“…AI is currently in an infant state, providing researchers and experts with a multitude of questions and challenges to consider (OECD 2019). However, the expectation is that AI will ultimately add significant value to society and economy and offer a great potential, hence the research and deep level understanding of AI bears great significance (Cunneen et al 2019).…”

Section: The Fourth Industrial Revolution: the Growing Use Of Digitalmentioning

confidence: 99%

Autonomous cars and responsible innovation

Lukovics¹,

Zuti²,

Fisher³

et al. 2020

The Challenges of Analyzing Social and Economic Processes in the 21st Century

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Digitalization, a dominant megatrend in today’s global world, offers numerous intriguing technological possibilities. Out of these novelties, self-driving cars have rapidly come to be a primary focus; the literature categorizes them as a radical innovation due to the possibility that the mass adoption of self-driving cars would not only radically change everyday life for members of industrialized societies, but calls into question the infrastructural, legal, and social ordering of towns and numerous aspects of transportation in the societies that adopt them. Meanwhile, the results of several international surveys with large samples show that public opinion of self-driving cars is ambivalent, indicating parallel signals of enthusiasm and concern. The aim of this paper is to develop key components of a general strategy for addressing the societal challenges associated with self-driving cars as identified in international surveys and relevant literature and using the framework of responsible innovation.

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Autonomous Vehicles and Embedded Artificial Intelligence: The Challenges of Framing Machine Driving Decisions

Cited by 74 publications

References 39 publications

A Moral Consensus Mechanism for Autonomous Driving: Towards a Law-compliant Basis of Logic Programming

A Moral Consensus Mechanism for Autonomous Driving: Towards a Law-compliant Basis of Logic Programming

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

Autonomous cars and responsible innovation

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