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Development of a Lane Change Assistance System Adapting to Driver's Uncertainty During Decision-Making

Authors:

Fei Yan,

Mark Eilers,

Martin Baumann,

Andreas LuedtkeAuthors Info & Claims

AutomotiveUI '16 Adjunct: Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Pages 93 - 98

https://doi.org/10.1145/3004323.3004334

Published: 24 October 2016 Publication History

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Abstract

This paper presents the development of a lane change assistance system adapting to both criticality and driver's uncertainty during decision-making in overtaking scenarios on simulated two-lane highways. Based on information about the traffic situation, the proposed system relies on a probabilistic model of driver's uncertainty to classify whether a driver is unsure or not and on a safety analysis based on driver's preferences to suggest appropriate overtaking actions, which together trigger corresponding advices on the Human Machine Interface. Different to existing lane change assistance systems using traffic light colors to encode criticality and warn accordingly, the proposed system uses colored abstract faces with emotional expressions encoding both criticality and driver's uncertainty to provide suggestions to either overtake or decelerate. The proposed system has been implemented in a driving simulator. The qualitative results of a not yet analyzed evaluation study with 20 participants show that the proposed system is more accepted and trusted than reference systems that do not consider driver's uncertainty.

References

[1]

M. Beggiato, M. Pereira, T. Petzoldt and J. Krems. 2015. Learning and development of trust, acceptance and the mental model of ACC. A longitudinal on-road study. Transportation research part F: traffic psychology and behavior 35, 75--84.

Google Scholar

[2]

B. Beresford and P. Sloper. 2008. Understanding the dynamics of decision-making and choice: A scoping study of key psychological theories to inform the design and analysis of the Panel Study. York: Social Policy Research Unit, University of York.

Google Scholar

[3]

V. A. Butakov and P. Ioannou. 2015. Personalized driver/vehicle lane change models for ADAS. IEEE Transactions on Vehicular Technology 64, 10, 4422--4431.

Crossref

Google Scholar

[4]

D. D. Clarke, P. J. Ward and J. Jones. 1998. Overtaking road-accidents: Differences in manoeuvre as a function of driver age. Accident Analysis & Prevention 30, 4, 455--467.

Crossref

Google Scholar

[5]

A. Frischen, J. D. Eastwood and D. Smilek. 2008. Visual search for faces with emotional expressions. Psychological bulletin 134, 5, 662.

Google Scholar

[6]

F. Guo, B. M. Wotring and J. F. Antin. 2010. Evaluation of Lane Change Collision Avoidance Systems Using the National Advanced Driving Simulator (No. HS-811 332).

Google Scholar

[7]

S. A. Huettel, A.W. Song and G. McCarthy. 2005. Decisions under uncertainty: probabilistic context influences activation of prefrontal and parietal cortices. The Journal of neuroscience 25, 13, 3304--3311.

Crossref

Google Scholar

[8]

T. Petzoldt and J. F. Krems. 2014. How does a lower predictability of lane changes affect performance in the Lane Change Task?. Applied ergonomics 45, 4, 1218--1224.

Google Scholar

[9]

C. M. Rudin-Brown. 2010. 'Intelligent'in-vehicle intelligent transport systems: Limiting behavioural adaptation through adaptive design. IET intelligent transport systems 4, 4, 252--261.

Google Scholar

[10]

B. Sen, J. D. Smith and W. G. Najm. 2003. Analysis of lane change crashes (No. HS-809 571).

Google Scholar

[11]

H. Schittenhelm. 2011. Driver Assistance Systems in Oncoming Traffic Situations. In 22nd International Technical Conference on the Enhanced Safety of Vehicles (ESV), 11--398.

Google Scholar

[12]

T. Yamagishi, K. S. Cook and M. Watabe. 1998. Uncertainty, trust, and commitment formation in the United States and Japan 1. American Journal of Sociology 104, 1, 165--194.

Crossref

Google Scholar

[13]

F. Yan, L. Weber and A. Luedtke. 2015. Classifying driver's uncertainty about the distance gap at lane changing for developing trustworthy assistance systems. In 2015 IEEE Intelligent Vehicles Symposium (IV), 1276--1281.

Google Scholar

[14]

F. Yan, M. Eilers, A. Luedtke and M. Baumann. 2016. Developing a Model of Driver's Uncertainty in Lane Change Situations for Trustworthy Lane Change Decision Aid Systems. In 2016 IEEE Intelligent Vehicles Symposium (IV), 406--411.

Google Scholar

Cited By

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Yan FEilers MBaumann M(2023)Investigating driver uncertainty about lane change decisionsTransportation Research Part F: Traffic Psychology and Behaviour10.1016/j.trf.2023.05.00195(369-379)Online publication date: May-2023
https://doi.org/10.1016/j.trf.2023.05.001
Zhao CZhao XLi ZZhang Q(2022)XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the HighwaySustainability10.3390/su1411682914:11(6829)Online publication date: 2-Jun-2022
https://doi.org/10.3390/su14116829
Stampf AColley MRukzio E(2022)Towards Implicit Interaction in Highly Automated Vehicles - A Systematic Literature ReviewProceedings of the ACM on Human-Computer Interaction10.1145/35467266:MHCI(1-21)Online publication date: 20-Sep-2022
https://dl.acm.org/doi/10.1145/3546726
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Development of a Lane Change Assistance System Adapting to Driver's Uncertainty During Decision-Making

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Published In

AutomotiveUI '16 Adjunct: Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

October 2016

245 pages

ISBN:9781450346542

DOI:10.1145/3004323

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction
University of Michigan: University of Michigan

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

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Work in progress
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Refereed limited

Funding Sources

Ministry of Science and Culture of Lower Saxony

Conference

AutomotiveUI'16

AutomotiveUI'16: 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

October 24 - 26, 2016

MI, Ann Arbor, USA

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Overall Acceptance Rate 248 of 566 submissions, 44%

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Cited By

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Yan FEilers MBaumann M(2023)Investigating driver uncertainty about lane change decisionsTransportation Research Part F: Traffic Psychology and Behaviour10.1016/j.trf.2023.05.00195(369-379)Online publication date: May-2023
https://doi.org/10.1016/j.trf.2023.05.001
Zhao CZhao XLi ZZhang Q(2022)XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the HighwaySustainability10.3390/su1411682914:11(6829)Online publication date: 2-Jun-2022
https://doi.org/10.3390/su14116829
Stampf AColley MRukzio E(2022)Towards Implicit Interaction in Highly Automated Vehicles - A Systematic Literature ReviewProceedings of the ACM on Human-Computer Interaction10.1145/35467266:MHCI(1-21)Online publication date: 20-Sep-2022
https://dl.acm.org/doi/10.1145/3546726
Jansen PColley MRukzio E(2022)A Design Space for Human Sensor and Actuator Focused In-Vehicle Interaction Based on a Systematic Literature ReviewProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35346176:2(1-51)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3534617
Löcken AYan FHeuten WBoll S(2019)Investigating driver gaze behavior during lane changes using two visual cues: ambient light and focal iconsJournal on Multimodal User Interfaces10.1007/s12193-019-00299-7Online publication date: 18-Mar-2019
https://doi.org/10.1007/s12193-019-00299-7
Damm WGalbas RStolle RScholz SBroy M(2018)Exploiting learning and scenario-based specification languages for the verification and validation of highly automated drivingProceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems10.1145/3194085.3194086(39-46)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3194085.3194086
Yan FEilers MLudtke ABaumann M(2017)Building driver's trust in lane change assistance systems by adapting to driver's uncertainty states2017 IEEE Intelligent Vehicles Symposium (IV)10.1109/IVS.2017.7995772(529-534)Online publication date: Jun-2017
https://doi.org/10.1109/IVS.2017.7995772

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