research-article

Cooperative Overtaking: Overcoming Automated Vehicles' Obstructed Sensor Range via Driver Help

Authors:

Martin Baumann,

Michael WeberAuthors Info & Claims

AutomotiveUI '19: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 144 - 155

https://doi.org/10.1145/3342197.3344531

Published: 21 September 2019 Publication History

Abstract

Automated vehicles will eventually operate safely without the need of human supervision and fallback, nevertheless, scenarios will remain that are managed more efficiently by a human driver. A common approach to overcome such weaknesses is to shift control to the driver. Control transitions are challenging due to human factor issues like post-automation behavior changes. We thus investigated cooperative overtaking wherein driver and vehicle complement each other: drivers support the vehicle to perceive the traffic scene and decide when to execute a maneuver whereas the system steers. We explored two maneuver approval and cancel techniques on touchscreens, and show that cooperative overtaking is feasible, both interaction techniques provide good usability and were preferred over manual maneuver execution. However, participants disregarded rear traffic in more complex situations. Consequently, system weaknesses can be overcome with cooperation, but drivers should be assisted by an adaptive system.

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Cited By

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Merritt TPapachristos EBarsted VSabir HHoldensgaard P(2024)The Final 100 Meters: Touch and Joystick Controls for Guiding Autonomous VehiclesProceedings of the Second International Symposium on Trustworthy Autonomous Systems10.1145/3686038.3686046(1-6)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3686038.3686046
Das DChakraborty SMitra B(2024)DriveR: Towards Generating a Dynamic Road Safety Map with Causal ContextsProceedings of the ACM on Human-Computer Interaction10.1145/36764988:MHCI(1-35)Online publication date: 24-Sep-2024
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Index Terms

Cooperative Overtaking: Overcoming Automated Vehicles' Obstructed Sensor Range via Driver Help
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

AutomotiveUI '19: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 2019

402 pages

ISBN:9781450368841

DOI:10.1145/3342197

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 21 September 2019

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AutomotiveUI '19

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AutomotiveUI '19: 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

September 21 - 25, 2019

Utrecht, Netherlands

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AutomotiveUI '19 Paper Acceptance Rate 34 of 119 submissions, 29%;

Overall Acceptance Rate 248 of 566 submissions, 44%

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Cited By

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https://doi.org/10.3390/mti8030016
Merritt TPapachristos EBarsted VSabir HHoldensgaard P(2024)The Final 100 Meters: Touch and Joystick Controls for Guiding Autonomous VehiclesProceedings of the Second International Symposium on Trustworthy Autonomous Systems10.1145/3686038.3686046(1-6)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3686038.3686046
Das DChakraborty SMitra B(2024)DriveR: Towards Generating a Dynamic Road Safety Map with Causal ContextsProceedings of the ACM on Human-Computer Interaction10.1145/36764988:MHCI(1-35)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676498
Bu FLi SGoedicke DColley MSharma GJu W(2024)Portobello: Extending Driving Simulation from the Lab to the RoadProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642341(1-13)Online publication date: 11-May-2024
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Peintner JManger CRiener A(2024)Increasing system transparency through confidence information in cooperative, automated drivingBehaviour & Information Technology10.1080/0144929X.2024.2407018(1-18)Online publication date: 30-Sep-2024
https://doi.org/10.1080/0144929X.2024.2407018
Wang JCui XFu QWang YYou F(2024)Intelligent Information Design Based on Human-Machine Collaboration in Lane Change Overtaking ScenariosHuman Interface and the Management of Information10.1007/978-3-031-60107-1_7(81-96)Online publication date: 29-Jun-2024
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Kuribayashi ATakeuchi ECarballo AIshiguro YTakeda K(2023)Recognition Assistance Interface for Human-Automation Cooperation in Pedestrian Risk PredictionSAE International Journal of Connected and Automated Vehicles10.4271/12-06-03-00236:3Online publication date: 6-Jun-2023
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Lee CAn D(2023)Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning ApproachApplied Sciences10.3390/app13221225813:22(12258)Online publication date: 13-Nov-2023
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