Handing over the Keys: A Qualitative Study of the Experience of Automation in Driving

Linehan, Conor; Murphy, Gillian; Hicks, Kieran; Gerling, Kathrin; Morrissey, Kellie

doi:10.1080/10447318.2019.1565482

Cited by 7 publications

(2 citation statements)

References 37 publications

(55 reference statements)

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“…Overall, a high level of automation is perceived as most beneficial for safety [26], with trust and social presence as important mediating factors. Furthermore, the automation of vehicles leads to a feeling of loss of control [36], which drivers perceive as negative [28].…”

Section: Trust Risk and Controlmentioning

confidence: 99%

Trust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis

Keller¹,

Zimmermann²,

Zibuschka³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Autonomous vehicles are continually being developed and are the focus of many research projects. While the focus so far has been primarily on technical features and the implementation of autonomous vehicles, we are turning to the consumer side to analyze the preferences of potential users regarding teleoperable robotaxis. After all, user acceptance is a necessary condition for the success of a new technology. Using a choice-based conjoint analysis, we investigate preferences for three different control dependent attributes and the price. We observe price to be the most important attribute, followed by the possibility of intervention, pilot, and interior monitoring-in this order. However, respondents' preferences turn out to be very heterogeneous. Within the framework of a cluster analysis, we look at the results in segments and analyze possible moderating effects using an analysis of variance.

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Section: Trust Risk and Controlmentioning

confidence: 99%

Trust is Good, Control is Better - Customer Preferences Regarding Control in Teleoperated and Autonomous Taxis

Keller¹,

Zimmermann²,

Zibuschka³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…As a numerical example, we consider an online problem that arises in the online optimal design of the cruise speed v c (t) of a platoon of vehicles in which a compromise between a target speed for a vehicle p i (t) (often coming from road conditions, vehicle type, and fuel/aerodynamic consideration) and a user's "desired" speed v i must be found. This problem and alike are becoming more relevant in the context of user acceptance of technology and automation, especially in view of automated driving [67].…”

Section: Numerical Examplementioning

confidence: 99%

Distributed Personalized Gradient Tracking with Convex Parametric Models

Notarnicola¹,

Simonetto²,

Farina³

et al. 2020

Preprint

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We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (userspecific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure a priori, whose parameters are to be learned along with the evolution of the algorithm. The algorithm is composed of two intertwined components: (i) a dynamic gradient tracking scheme for finding local solution estimates and (ii) a recursive least squares scheme for estimating the unknown parameters via user's noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis.

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