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

Azevedo-Sa, Hebert; Jayaraman, Suresh Kumaar; Esterwood, Connor; Yang, Xi; Robert, Lionel; Tilbury, Dawn M.

doi:10.1145/3371382.3378371

Cited by 12 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This variability is due to the uncertainty represented by the random noise parameters u(t k ) and w(t k ), and the width of the bound bands is related to the computed covariances σ 2 u and w . Both lower values for σ 2 u and higher values for w entries would imply a narrower band, meaning that the estimator would have less variability (and therefore could be slower on tracking trust self-reports). Meanwhile, higher σ 2 u and lower values of w entries would imply, respectively, a less accurate process model and on observations considered more reliable.…”

Section: Trust Estimation Resultsmentioning

confidence: 99%

“…On the other hand, when the system does not identify the existence of a hazard and no alarm is raised, a miss occurs. These different error types influence system users differently [2,27,28,54], and also have distinct impacts on trust. The influence of false alarms and misses on operators' behaviors was investigated by Dixon et al [13], who has established a relationship with users compliance and reliance behaviors.…”

Section: System Malfunctions and Trustmentioning

confidence: 99%

See 1 more Smart Citation

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

Azevedo-Sa

Jayaraman

Esterwood

et al. 2020

Int J of Soc Robotics

Self Cite

View full text Add to dashboard Cite

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 filter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers’ performance on a non-driving-related task. We conducted a study ($$n=80$$ 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 identification 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.

show abstract

Section: Trust Estimation Resultsmentioning

confidence: 99%

Section: System Malfunctions and Trustmentioning

confidence: 99%

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

Azevedo-Sa

Jayaraman

Esterwood

et al. 2020

Int J of Soc Robotics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed real-time trust calibration method was inspired by the relationships among situation awareness, risk perception and trust. Previous works reported on the effec-tiveness of situation awareness and perceived risk to impact drivers' trust in AVs [25]- [28]. We applied different communications styles and messages in an attempt to vary drivers' situation awareness and risk perception in real time.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, enhancing drivers' situation awareness facilitates increased trust in AVs [24], [25]. On the other hand, increasing drivers' perception of risk reduces their trust in AVs [26]- [28]. Our framework takes advantage of these studies' results and seeks to influence trust by varying situation awareness and risk perception through verbal communications from the AV to the driver.…”

Section: A Related Work: Trust In Automated Systems Trust Estimatiomentioning

confidence: 99%

Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

Azevedo-Sa

Jayaraman

Yang

et al. 2020

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…AVs are expected to become ubiquitous in the future as they promise to improve fuel efficiency and reduce traffic accidents. Trust in AVs is one of the main factors that influence AV adoption [3,8]. AVs have specific characteristics that make the study of trust in driver-AV interaction challenging.…”

Section: Driver-av Interaction Particularitiesmentioning

confidence: 99%

Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

2021

View full text Add to dashboard Cite

Advances in perception and artificial intelligence technology are expected to lead to seamless interaction between humans and robots. Trust in robots has been evolving from the theory on trust in automation, with a fundamental difference: unlike traditional automation, robots could adjust their behaviors depending on how their human counterparts appear to be trusting them or how humans appear to be trustworthy. In this extended abstract I present my research on methods for processing trust in the particular context of interactions between a driver and an automated vehicle, which has the goal of achieving higher safety and performance standards for the team formed by those human and robotic agents. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models.

show abstract

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

Cited by 12 publications

References 15 publications

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

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

Context-Adaptive Management of Drivers’ Trust in Automated Vehicles

Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

Contact Info

Product

Resources

About