Computer vision research involving street and road detection methods usually focuses on driving assistance and autonomous vehicle systems. In this context, street segmentation occurs in real-time, based on images centered on the street. This work, on the other hand, uses street segmentation for urban planning research to classify pavement types of a city or region, which is particularly important for developing countries. For this application, it is needed a dataset with images from various locations for each street. These images are (...) not necessarily centered on the street and include challenges that are not common in street segmentation datasets, such as mixed pavement types and the presence of faults and holes on the street. We implemented a multi-class version of a state-of-the-art segmentation algorithm and adapted it to perform street pavement classification, handling navigation along streets and angle variation to increase the accuracy of the classification. A data augmentation approach is also proposed to use preliminary results from the test instances as new ground truth and increase the amount of training data. A dataset with more than 300,000 images from 773 streets from a Brazilian city was built. Our approach achieved a precision of 0.93, showing the feasibility of the proposed application. (shrink)

Recent simulations by Scanlon et al. showed seemingly spectacular results for the Waymo self-driving vehicle in simulations of real accident situations. In this paper, it is argued that the selection criteria for accident situations must be modified in accordance with the relevant policy alternatives. While Scanlon et al. compare Waymo with old human-driven vehicles, it is argued here that the relevant policy question is whether we ought to use self-driven vehicles or human-driven vehicles in the future, which means that we (...) need to consider whether other technological solutions, which are available but not broadly used in human-driven vehicles, could result in human-driven vehicles managing to avoid the same accidents. In this article, a proposal for a new standard of selection criteria is made. (shrink)

We address the considerations of the European Commission Expert Group on the ethics of connected and automated vehicles regarding data provision in the event of collisions. While human drivers’ appropriate post-collision behavior is clearly defined, regulations for automated driving do not provide for collision detection. We agree it is important to systematically incorporate citizens’ intuitions into the discourse on the ethics of automated vehicles. Therefore, we investigate whether people expect automated vehicles to behave like humans after an accident, even if (...) this behavior does not directly affect the consequences of the accident. We find that appropriate post-collision behavior substantially influences people’s evaluation of the underlying crash scenario. Moreover, people clearly think that automated vehicles can and should record the accident, stop at the site, and call the police. They are even willing to pay for technological features that enable post-collision behavior. Our study might begin a research program on post-collision behavior, enriching the empirically informed study of automated driving ethics that so far exclusively focuses on pre-collision behavior. (shrink)

Crashes involving self-driving cars at least superficially resemble trolley dilemmas. This article discusses what lessons machine ethicists working on the ethics of self-driving cars can learn from trolleyology. The article proceeds by providing an account of the trolley problem as a paradox and by distinguishing two types of solutions to the trolley problem. According to an optimistic solution, our case intuitions about trolley dilemmas are responding to morally relevant differences. The pessimistic solution denies that this is the case. An optimistic (...) solution would yield first-order moral insights for the ethics of self-driving cars, but such a solution is difficult to come by. More plausible is the pessimistic solution, and it teaches us a methodological lesson. The lesson is that machine ethicists should discount case intuitions and instead rely on intuitions and judgments at a higher level of generality. (shrink)

Autonomous vehicles (AV) offer promising benefits to society in terms of safety, environmental impact and increased mobility. However, acute challenges persist with any novel technology, inlcuding the perceived risks and trust underlying public acceptance. While research examining the current state of AV public perceptions and future challenges related to both societal and individual barriers to trust and risk perceptions is emerging, it is highly fragmented across disciplines. To address this research gap, by using the Web of Science database, our study (...) undertakes a bibliometric and performance analysis to identify the conceptual and intellectual structures of trust and risk narratives within the AV research field by investigating engineering, social sciences, marketing, and business and infrastructure domains to offer an interdisciplinary approach. Our analysis provides an overview of the key research area across the search categories of ‘trust’ and ‘risk’. Our results show three main clusters with regard to trust and risk, namely, behavioural aspects of AV interaction; uptake and acceptance; and modelling human–automation interaction. The synthesis of the literature allows a better understanding of the public perception of AV and its historical conception and development. It further offers a robust model of public perception in AV, outlining the key themes found in the literature and, in turn, offers critical directions for future research. (shrink)

This article is about the role of factual uncertainty for moral decision-making as it concerns the ethics of machine decision-making. The view that is defended here is that factual uncertainties require a normative evaluation and that ethics of machine decision faces a triple-edged problem, which concerns what a machine ought to do, given its technical constraints, what decisional uncertainty is acceptable, and what trade-offs are acceptable to decrease the decisional uncertainty.

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of (...) classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems. (shrink)