Comparing the Effects of False Alarms and Misses on Humans’ Trust in (Semi)Autonomous Vehicles

Trust in automated driving systems is crucial for effective driver- (semi)autonomous vehicles interaction. Drivers that do not trust the system appropriately are not able to leverage its benefits. This study presents a mixed design user experiment where participants conducted a non-driving task while traveling in a simulated semi-autonomous vehicle with forward collision alarm and emergency braking functions. Occasionally, the system missed obstacles or provided false alarms.We varied these system error types as well as road shapes, and measured the effects of these variations on trust development. Results reveal that misses are more harmful to trust development than false alarms, and that these effects are strengthened by operation on risky roads. Our findings provide additional insight into the development of trust in automated driving systems, and are useful for the design of such technologies.Automotive Research Center at the University of Michigan, through the U.S. Army CCDC/GVSCPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153524/1/Azevedo-Sa et al. 2020.pdfDescription of Azevedo-Sa et al. 2020.pdf : Mainfil

Real-Time Estimation of Drivers' Trust in Automated Driving Systems

Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of techniques for measuring drivers' trust in the automated driving system during real-time applications execution. One possible approach for measuring trust is through modeling its dynamics and subsequently applying classical state estimation methods. This paper proposes a framework for modeling the dynamics of drivers' trust in automated driving systems and also for estimating these varying trust levels. The estimation method integrates sensed behaviors (from the driver) through a Kalman lter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers' performance on a non-driving-related task (NDRT). We conducted a study (n = 80) with a simulated SAE level 3 automated driving system, and analyzed the factors that impacted drivers' trust in the system. Data from the user study were also used for the identi cation of the trust model parameters. Results show that the proposed approach was successful in computing trust estimates over successive interactions between the driver and the automated driving system. These results encourage the use of strategies for modeling and estimating trust in automated driving systems. Such trust measurement technique paves a path for the design of trust-aware automated driving systems capable of changing their behaviors to control drivers' trust levels to mitigate both undertrust and overtrust.National Science FoundationBrazilian Army's Department of Science and TechnologyAutomotive Research Center (ARC) at the University of MichiganU.S. Army CCDC/GVSC (government contract DoD-DoA W56HZV14-2-0001).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162572/1/Azevedo Sa et al. 2020.pdfSEL

Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

Automated vehicles (AVs) that intelligently interact with drivers must build a trustworthy relationship with them. A calibrated level of trust is fundamental for the AV and the driver to collaborate as a team. Techniques that allow AVs to perceive drivers’ trust from drivers’ behaviors and react accordingly are, therefore, needed for context-aware systems designed to avoid trust miscalibrations. This letter proposes a framework for the management of drivers’ trust in AVs. The framework is based on the identification of trust miscalibrations (when drivers’ undertrust or overtrust the AV) and on the activation of different communication styles to encourage or warn the driver when deemed necessary. Our results show that the management framework is effective, increasing (decreasing) trust of undertrusting (overtrusting) drivers, and reducing the average trust miscalibration time periods by approximately 40%. The framework is applicable for the design of SAE Level 3 automated driving systems and has the potential to improve the performance and safety of driver–AV teams.U.S. Army CCDC/GVSCAutomotive Research CenterNational Science FoundationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162571/1/Azevedo-Sa et al. 2020 with doi.pdfSEL

Improving Collaboration Between Drivers and Automated Vehicles with Trust Processing Methods

Trust has gained attention in the Human-Robot Interaction (HRI) field, as it is considered an antecedent of people's reliance on machines. In general, people are likely to rely on and use machines they trust, and to refrain from using machines they do not trust. Recent advances in robotic perception technologies open paths for the development of machines that can be aware of people's trust by observing their human behaviors. This dissertation explores the role of trust in the interactions between humans and robots, particularly Automated Vehicles (AVs). Novel methods and models are proposed for perceiving and processing drivers' trust in AVs and for determining both humans' natural trust and robots' artificial trust. Two high-level problems are addressed in this dissertation: (1) the problem of avoiding or reducing miscalibrations of drivers' trust in AVs, and (2) the problem of how trust can be used to dynamically allocate tasks between a human and a robot that collaborate. A complete solution is proposed for the problem of avoiding or reducing trust miscalibrations. This solution combines methods for estimating and influencing drivers' trust through interactions with the AV. Three main contributions stem from that solution: (i) the characterization of risk factors that affect drivers’ trust in AVs, which provided theoretical evidence for the development of a linear model for driver trust in AVs; (ii) the development of a new method for real-time trust estimation, which leveraged the trust linear model mentioned above for the implementation of a Kalman-filter-based approach, able to provide numerical estimates from the processing of drivers' behavioral measurements; and (iii) the development of a new method for trust calibration, which identifies trust miscalibration instances from comparisons between drivers' trust in the AV and that AV's capabilities, and triggers messages from the AV to the driver. These messages are effective for encouraging or warning drivers that are undertrusting or overtrusting the AV capabilities respectively as shown by the obtained results. Although the development of a trust-based solution for dynamically allocating tasks between a human and a robot (i.e., the second high-level problem addressed in this dissertation) remains an open problem, we take a step forward in that direction. The fourth contribution of this dissertation is the development of a unified bi-directional model for predicting natural and artificial trust. This trust model is based on mathematical representations of both the trustee agent's capabilities and the required capabilities for the execution of a task. Trust emerges from comparisons between the agent capabilities and task requirements, roughly replicating the following logic: if a trustee agent's capabilities exceed the requirements for executing a certain task, then the agent can be highly trusted (to execute that task); conversely, if that trustee agent's capabilities fall short of that task requirements, trust should be low. In this trust model, the agent's capabilities are represented by random variables that are dynamically updated over interactions between the trustor and the trustee whenever the trustee is successful or fails in the execution of a task. These capability representations allow for the numerical computation of human's trust or robot's trust, which is represented by the probability of a given trustee agent to execute a given task successfully.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169615/1/azevedo_1.pd

Using Trust in Automation to Enhance Driver-(Semi)Autonomous Vehicle Interaction and Improve Team Performance

Trust in robots has been gathering attention from multiple directions, as it has a 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, miscalibration of trust in automation is a common issue that jeopardizes the effectiveness of automation use. It happens when a user’s 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 driver’s 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 driver’s trust levels. This capability could avoid under- and over trusting, which could harm their safety or their performance.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167861/1/ISTDM-2021-Extended-Abstract-0118.pdfDescription of ISTDM-2021-Extended-Abstract-0118.pdf : PaperSEL

The Effect of Task Load, Automation Reliability, and Environment Complexity on UAV Supervisory Control Performance

Over the last decade, military unmanned aerial vehicles (UAVs) have experienced exponential growth and now comprise over 40% of military aircraft. However, since most military UAVs require multiple operators (usually an air vehicle operator, payload operator, and mission commander), the proliferation of UAVs has created a manpower burden within the U.S. military. Fortunately, simultaneous advances in UAV automation have enabled a switch from direct control to supervisory control; future UAV operators will no longer directly control a single UAV subsystem but, rather, will control multiple advanced, highly autonomous UAVs. However, research is needed to better understand operator performance in a complex UAV supervisory control environment. The Naval Research Lab (NRL) developed SCOUT™ (Supervisory Control Operations User Testbed) to realistically simulate the supervisory control tasks that a future UAV operator will likely perform in a dynamic, uncertain setting under highly variable time constraints. The study reported herein used SCOUT to assess the effects of task load, environment complexity, and automation reliability on UAV operator performance and automation dependence. The effects of automation reliability on participants’ subjective trust ratings and the possible dissociation between task load and subjective workload ratings were also explored. Eighty-one Navy student pilots completed a 34:15 minute pre-scripted SCOUT scenario, during which they managed three helicopter UAVs. To meet mission goals, they decided how to best allocate the UAVs to locate targets while they maintained communications, updated UAV parameters, and monitored their sensor feeds and airspace. After completing training on SCOUT, participants were randomly sorted into low and high automation reliability groups. Within each group, task load (the number of messages and vehicle status updates that had to be made and the number of new targets that appeared) and environment complexity (the complexity of the payload monitoring task) were varied between low and high levels over the course of the scenario. Participants’ throughput, accuracy, and expected value in response to mission events were used to assess their performance. In addition, participants rated their subjective workload and fatigue using the Crew Status Survey. Finally, a four-item survey modeled after Lee and Moray’s validated (1994) scale was used to assess participants’ trust in the payload task automation and their self-confidence that they could have manually performed the payload task. This study contributed to the growing body of knowledge on operator performance within a UAV supervisory control setting. More specifically, it provided experimental evidence of the relationship between operator task load, task complexity, and automation reliability and their effects on operator performance, automation dependence, and operators’ subjective experiences of workload and fatigue. It also explored the relationship between automation reliability and operators’ subjective trust in said automation. The immediate goal of this research effort is to contribute to the development of a suite of domain-specific performance metrics to enable the development and/or testing and evaluation of future UAV ground control stations (GCS), particularly new work support tools and data visualizations. Long-term goals also include the potential augmentation of the current Aviation Selection Test Battery (ASTB) to better select future UAV operators and operational use of the metrics to determine mission-specific manpower requirements. In the far future, UAV-specific performance metrics could also contribute to the development of a dynamic task allocation algorithm for distributing control of UAVs amongst a group of operators

Moderators Of Trust And Reliance Across Multiple Decision Aids

The present work examines whether user\u27s trust of and reliance on automation, were affected by the manipulations of user\u27s perception of the responding agent. These manipulations included agent reliability, agent type, and failure salience. Previous work has shown that automation is not uniformly beneficial; problems can occur because operators fail to rely upon automation appropriately, by either misuse (overreliance) or disuse (underreliance). This is because operators often face difficulties in understanding how to combine their judgment with that of an automated aid. This difficulty is especially prevalent in complex tasks in which users rely heavily on automation to reduce their workload and improve task performance. However, when users rely on automation heavily they often fail to monitor the system effectively (i.e., they lose situation awareness - a form of misuse). However, if an operator realizes a system is imperfect and fails, they may subsequently lose trust in the system leading to underreliance. In the present studies, it was hypothesized that in a dual-aid environment poor reliability in one aid would impact trust and reliance levels in a companion better aid, but that this relationship is dependent upon the perceived aid type and the noticeability of the errors made. Simulations of a computer-based search-and-rescue scenario, employing uninhabited/unmanned ground vehicles (UGVs) searching a commercial office building for critical signals, were used to investigate these hypotheses. Results demonstrated that participants were able to adjust their reliance and trust on automated teammates depending on the teammate\u27s actual reliability levels. However, as hypothesized there was a biasing effect among mixed-reliability aids for trust and reliance. That is, when operators worked with two agents of mixed-reliability, their perception of how reliable and to what degree they relied on the aid was effected by the reliability of a current aid. Additionally, the magnitude and direction of how trust and reliance were biased was contingent upon agent type (i.e., \u27what\u27 the agents were: two humans, two similar robotic agents, or two dissimilar robot agents). Finally, the type of agent an operator believed they were operating with significantly impacted their temporal reliance (i.e., reliance following an automation failure). Such that, operators were less likely to agree with a recommendation from a human teammate, after that teammate had made an obvious error, than with a robotic agent that had made the same obvious error. These results demonstrate that people are able to distinguish when an agent is performing well but that there are genuine differences in how operators respond to agents of mixed or same abilities and to errors by fellow human observers or robotic teammates. The overall goal of this research was to develop a better understanding how the aforementioned factors affect users\u27 trust in automation so that system interfaces can be designed to facilitate users\u27 calibration of their trust in automated aids, thus leading to improved coordination of human-automation performance. These findings have significant implications to many real-world systems in which human operators monitor the recommendations of multiple other human and/or machine systems