From driverless dilemmas to more practical commonsense tests for automated vehicles.
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ABSTRACT:
SUBMITTER: De Freitas J
PROVIDER: S-EPMC7980438 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others' behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.
| S-EPMC8239464 | biostudies-literature
Project description:The development of artificial intelligence has led researchers to study the ethical principles that should guide machine behavior. The challenge in building machine morality based on people's moral decisions, however, is accounting for the biases in human moral decision-making. In seven studies, this paper investigates how people's personal perspectives and decision-making modes affect their decisions in the moral dilemmas faced by autonomous vehicles. Moreover, it determines the variations in people's moral decisions that can be attributed to the situational factors of the dilemmas. The reported studies demonstrate that people's moral decisions, regardless of the presented dilemma, are biased by their decision-making mode and personal perspective. Under intuitive moral decisions, participants shift more towards a deontological doctrine by sacrificing the passenger instead of the pedestrian. In addition, once the personal perspective is made salient participants preserve the lives of that perspective, i.e. the passenger shifts towards sacrificing the pedestrian, and vice versa. These biases in people's moral decisions underline the social challenge in the design of a universal moral code for autonomous vehicles. We discuss the implications of our findings and provide directions for future research.
| S-EPMC6739396 | biostudies-literature
Project description:The ethical dilemma (ED) of whether autonomous vehicles (AVs) should protect the passengers or pedestrians when harm is unavoidable has been widely researched and debated. Several behavioral scientists have sought public opinion on this issue, based on the premise that EDs are critical to resolve for AV adoption. However, many scholars and industry participants have downplayed the importance of these edge cases. Policy makers also advocate a focus on higher level ethical principles rather than on a specific solution to EDs. But conspicuously absent from this debate is the view of the consumers or potential adopters, who will be instrumental to the success of AVs. The current research investigated this issue both from a theoretical standpoint and through empirical research. The literature on innovation adoption and risk perception suggests that EDs will be heavily weighted by potential adopters of AVs. Two studies conducted with a broad sample of consumers verified this assertion. The results from these studies showed that people associated EDs with the highest risk and considered EDs as the most important issue to address as compared to the other technical, legal and ethical issues facing AVs. As such, EDs need to be addressed to ensure robustness in the design of AVs and to assure consumers of the safety of this promising technology. Some preliminary evidence is provided about interventions to resolve the social dilemma in EDs and about the ethical preferences of prospective early adopters of AVs.Supplementary informationThe online version contains supplementary material available at 10.1007/s10676-021-09605-y.
| S-EPMC8253245 | biostudies-literature
Project description:Automated vehicles (AVs) have made huge strides toward large-scale deployment. Despite this progress, AVs continue to make mistakes, some resulting in death. Although some mistakes are avoidable, others are hard to avoid even by highly skilled drivers. As these mistakes continue to shape attitudes toward AVs, we need to understand whether people differentiate between them. We ask the following two questions. When an AV makes a mistake, does the perceived difficulty or novelty of the situation predict blame attributed to it? How does that blame attribution compare to a human driving a car? Through two studies, we find that the amount of blame people attribute to AVs and human drivers is sensitive to situation difficulty. However, while some situations could be more difficult for AVs and others for human drivers, people blamed AVs more, regardless. Our results provide novel insights in understanding psychological barriers influencing the public's view of AVs.
| S-EPMC7995526 | biostudies-literature
Project description:While traffic signals, signs, and road markings provide explicit guidelines for those operating in and around the roadways, some decisions, such as determinations of "who will go first," are made by implicit negotiations between road users. In such situations, pedestrians are today often dependent on cues in drivers' behavior such as eye contact, postures, and gestures. With the introduction of more automated functions and the transfer of control from the driver to the vehicle, pedestrians cannot rely on such non-verbal cues anymore. To study how the interaction between pedestrians and automated vehicles (AVs) might look like in the future, and how this might be affected if AVs were to communicate their intent to pedestrians, we designed an external vehicle interface called automated vehicle interaction principle (AVIP) that communicates vehicles' mode and intent to pedestrians. The interaction was explored in two experiments using a Wizard of Oz approach to simulate automated driving. The first experiment was carried out at a zebra crossing and involved nine pedestrians. While it focused mainly on assessing the usability of the interface, it also revealed initial indications related to pedestrians' emotions and perceived safety when encountering an AV with/without the interface. The second experiment was carried out in a parking lot and involved 24 pedestrians, which enabled a more detailed assessment of pedestrians' perceived safety when encountering an AV, both with and without the interface. For comparison purposes, these pedestrians also encountered a conventional vehicle. After a short training course, the interface was deemed easy for the pedestrians to interpret. The pedestrians stated that they felt significantly less safe when they encountered the AV without the interface, compared to the conventional vehicle and the AV with the interface. This suggests that the interface could contribute to a positive experience and improved perceived safety in pedestrian encounters with AVs - something that might be important for general acceptance of AVs. As such, this topic should be further investigated in future studies involving a larger sample and more dynamic conditions.
| S-EPMC6090516 | biostudies-literature
Project description:Summary Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protection, we propose an injury risk mitigation-based decision-making algorithm. The algorithm is guided by a real-time, data-driven human injury prediction model and is assessed using detailed first-hand information collected from real-world crashes. The results demonstrate that integrating injury prediction into decision-making is promising for reducing traffic casualties. Because safety decisions involve harm distribution for different participants, we further analyze the potential ethical issues quantitatively, providing a technically critical step closer to settling such dilemmas. This work demonstrates the feasibility of applying mining tools to identify the underlying mechanisms embedded in crash data accumulated over time and opens the way for future AVs to facilitate optimal road traffic safety. Graphical abstract Highlights • We propose an injury risk mitigation-based decision-making algorithm for AVs• A real-time, data-driven human injury prediction model was established• We applied mining tools to identify mechanisms embedded in accumulated crash data• We analyzed traffic ethical issues quantitatively, closer to feasible solutions Injury; Applied computing; Human-computer interaction; Engineering
| S-EPMC9287800 | biostudies-literature
Project description:Influential theories on moral judgments propose that they rely either on emotions or on innate moral principles. In contrast, the mental model theory postulates that moral judgments rely on reasoning, either intuition or deliberation. The theory allows for the possibility that intuitions lead to utilitarian judgments. This paper reports two experiments involving fifth-grade children, adolescents, and adults; the results revealed that children reason intuitively to resolve moral dilemmas in which action and inaction lead to different outcomes. In particular, the results showed female children to be more utilitarian than female adults in resolving classical moral dilemmas: they preferred an action that achieved a good outcome for a greater number of people. Within the mental model theory's framework there is no reason to expect that females and males differ in their ability to reason, but at the moment the results for females cannot be generalized to males who were not properly represented in the adults groups of the two experiments. The result revealing that (female) children are more utilitarian than (female) adults, which is hard to explain via many current theories, was predicted by the mental model theory.
| S-EPMC4562243 | biostudies-literature
Project description:The torque output accuracy of the IPMSM in electric vehicles using a state of the art MTPA strategy highly depends on the accuracy of machine parameters, thus, a torque estimation method is necessary for the safety of the vehicle. In this paper, a torque estimation method based on flux estimator with a modified low pass filter is presented. Moreover, by taking into account the non-ideal characteristic of the inverter, the torque estimation accuracy is improved significantly. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulation and experiment.
| S-EPMC4482678 | biostudies-literature
Project description:A set of five fire tests involving battery electric vehicles and conventional cars was performed in a tunnel. This data article provides the dataset of some of the determined parameters: air temperature, air velocity and heat release rate. The air temperature was measured at several locations and at different heights, distributed in the cross section of the tunnel. The velocity of the incoming air was also measured. The third parameter, the heat release rate (HRR), was calculated based on the enthalpy flow before and after the fire location. This parameter is important for characterizing the size of a vehicle fire. The data provide a reference for the evaluation of BEV fires and could be taken as reference for further fire studies. They might be also of interest to research groups dealing with simulation applications, like three-dimensional CFD simulations.
| S-EPMC9801077 | biostudies-literature
Project description:OBJECTIVE:In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians' crossing intentions. BACKGROUND:Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians' interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI. METHODS:Twenty-eight participants experienced an urban road environment from a pedestrian's perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and "feel-safe" percentages were calculated. RESULTS:For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs. CONCLUSION:An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous. APPLICATION:This research supports the development of automated vehicles that communicate with other road users.
| S-EPMC6820125 | biostudies-literature