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Register a new userUsing Trust in Automation to Enhance Driver-(Semi)Autonomous Vehicle Interaction and Improve Team Performance
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Added by
Hebert Azevedo-Sa
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Trust in robots has been gathering attention from multiple directions, as it has special relevance in the theoretical descriptions of human-robot interactions. It is essential for reaching high acceptance and usage rates of robotic technologies in society, as well as for enabling effective human-robot teaming. Researchers have been trying to model the development of trust in robots to improve the overall rapport between humans and robots. Unfortunately, the miscalibration of trust in automation is a common issue that jeopardizes the effectiveness of automation use. It happens when a users trust levels are not appropriate to the capabilities of the automation being used. Users can be: under-trusting the automation -- when they do not use the functionalities that the machine can perform correctly because of a lack of trust; or over-trusting the automation -- when, due to an excess of trust, they use the machine in situations where its capabilities are not adequate. The main objective of this work is to examine drivers trust development in the ADS. We aim to model how risk factors (e.g.: false alarms and misses from the ADS) and the short-term interactions associated with these risk factors influence the dynamics of drivers trust in the ADS. The driving context facilitates the instrumentation to measure trusting behaviors, such as drivers eye movements and usage time of the automated features. Our findings indicate that a reliable characterization of drivers trusting behaviors and a consequent estimation of trust levels is possible. We expect that these techniques will permit the design of ADSs able to adapt their behaviors to attempt to adjust drivers trust levels. This capability could avoid under- and over-trusting, which could harm their safety or their performance.
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A total 19% of generation capacity in California is offered by PV units and over some months, more than 10% of this energy is curtailed. In this research, a novel approach to reduce renewable generation curtailments and increasing system flexibility by means of electric vehicles charging coordination is represented. The presented problem is a sequential decision making process, and is solved by fitted Q-iteration algorithm which unlike other reinforcement learning methods, needs fewer episodes of learning. Three case studies are presented to validate the effectiveness of the proposed approach. These cases include aggregator load following, ramp service and utilization of non-deterministic PV generation. The results suggest that through this framework, EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times, as well as solar power generation are unknown beforehand.
Trust is a multilayered concept with critical relevance when it comes to introducing new technologies. Understanding how humans will interact with complex vehicle systems and preparing for the functional, societal and psychological aspects of autonomous vehicles entry into our cities is a pressing concern. Design tools can help calibrate the adequate and affordable level of trust needed for a safe and positive experience. This study focuses on passenger interactions capable of enhancing the system trustworthiness and data accuracy in future shared public transportation.
Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments. Such a task requires communication between autonomous vehicles and other road users in order to resolve various traffic ambiguities. The interaction between road users is a form of negotiation in which the parties involved have to share their attention regarding a common objective or a goal (e.g. crossing an intersection), and coordinate their actions in order to accomplish it. In this literature review we aim to address the interaction problem between pedestrians and drivers (or vehicles) from joint attention point of view. More specifically, we will discuss the theoretical background behind joint attention, its application to traffic interaction and practical approaches to implementing joint attention for autonomous vehicles.
Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. The major components of this system are (i) an ontology of network-element types that represents the main threats to and vulnerabilities of anonymous communication over Tor, (ii) a formal language that allows users to naturally express trust beliefs about network elements, and (iii) a conversion procedure that takes the ontology, public information about the network, and user beliefs written in the trust language and produce a Bayesian Belief Network that represents the probability distribution in a way that is concise and easily sampleable. We also present preliminary experimental results that show the distribution produced by our system can improve security when employed by users; further improvement is seen when the system is employed by both users and services.
Objective: We examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Background: Most existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. Method: Seventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. Results: Outcome bias and contrast effect significantly influence human operators trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him-/her-self. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. Conclusion: Human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. Application: Understanding the trust adjustment process enables accurate prediction of the operators moment-to-moment trust in automation and informs the design of trust-aware adaptive automation.
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