Pietro Lungaro
ABSTRACT
This paper presents a novel platform for supporting human-centric design of future on-board user interfaces. This is conceived to facilitate the interplay and information exchange among onboard digital information systems, autonomous AI agents and human passengers and drivers. Two Human-to-AI (H2AI) Augmented Reality (AR) interfaces, characterized by different degrees of immersivity, have been designed to provide passengers with intuitive visualization of information available in the AI modules controlling the car behavior. To validate the proposed user-centric paradigm, a novel testbed has been developed for assessing whether H2AI solutions can be effective in increasing human trust in self-driving cars. The results of our initial experimental studies, performed with several subjects, clearly showed that visualizing AI information brings a critical understanding of the autonomous driving processes, which in turn leads to a substantial increase of trust in the system.
- AB software development Alexander Blade. 2017. Scripthook V. http://www.dev-c.com/gtav/scripthookv/. (2017). Accessed on: 18-07-2017.Google Scholar
- Mary T Dzindolet, Scott A Peterson, Regina A Pomranky, Linda G Pierce, and Hall P Beck. 2003. The role of trust in automation reliance. International Journal of Human-Computer Studies 58, 6 (2003), 697--718. Google ScholarDigital Library
- Rockstar Games. 2017. Grand Theft Auto V. http://www.rockstargames.com/V/. (2017). Accessed on: 18-07-2017.Google Scholar
- Paul George, Indira Thouvenin, Vincent Fremont, and Véronique Cherfaoui. 2012. DAARIA: Driver assistance by augmented reality for intelligent automobile. In Intelligent Vehicles Symposium (IV), 2012 IEEE. IEEE, 1043--1048.Google ScholarCross Ref
- Will Knight. 2017. https://www.technologyreview.com/s/602317/self-driving-cars-can-learn-a-lot-by-playing-grand-theft-auto/. (2017). Accessed on: 07-07-2017.Google Scholar
- Jeamin Koo, Jungsuk Kwac, Wendy Ju, Martin Steinert, Larry Leifer, and Clifford Nass. 2015. Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing (IJIDeM) 9, 4 (2015), 269--275.Google ScholarCross Ref
- Jiin Lee, Naeun Kim, Chaerin Imm, Beomjun Kim, Kyongsu Yi, and Jinwoo Kim. 2016. A Question of Trust: An Ethnographic Study of Automated Cars on Real Roads. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, 201--208. Google ScholarDigital Library
- John D Lee and Katrina A See. 2004. Trust in automation: Designing for appropriate reliance. Human Factors: The Journal of the Human Factors and Ergonomics Society 46, 1 (2004), 50--80.Google ScholarCross Ref
- Andreas Löcken, Wilko Heuten, and Susanne Boll. 2016. AutoAmbiCar: Using Ambient Light to Inform Drivers About Intentions of Their Automated Cars. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct. ACM, 57--62. Google ScholarDigital Library
- Raja Parasuraman and Victor Riley. 1997. Humans and automation: Use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society 39, 2 (1997), 230--253.Google ScholarCross Ref
- Stephan R. Richter, Vibhav Vineet, Stefan Roth, and Vladlen Koltun. 2016. Playing for Data: Ground Truth from Computer Games. In European Conference on Computer Vision (ECCV) (LNCS), Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.), Vol. 9906. Springer International Publishing, 102--118.Google ScholarCross Ref
- Aitor Ruano. 2017. DeepGTAV: A plugin for GTAV that transforms it into a vision-based self-driving car research environment. https://github.com/ai-tor/DeepGTAV. (2017). Accessed on: 18-07-2017.Google Scholar
- Michelle L Rusch, Mark C Schall, Patrick Gavin, John D Lee, Jeffrey D Dawson, Shaun Vecera, and Matthew Rizzo. 2013. Directing driver attention with augmented reality cues. Transportation research part F: traffic psychology and behaviour 16 (2013), 127--137.Google Scholar
- Brandon Schoettle and Michael Sivak. 2014. A survey of public opinion about autonomous and self-driving vehicles in the US, the UK, and Australia. (2014).Google Scholar
- Erin Stepp. 2016. Three-Quarters of Americans "Afraid" to Ride in a Self-Driving Vehicle. (2016). http://www.newsroom.aaa.com/2016/03/three-quarters-of-americans-afraid-to-ride-in-a-self-driving-vehicle/.Google Scholar
Index Terms
- Human-to-AI Interfaces for Enabling Future Onboard Experiences
Recommendations
Demonstration of a low-cost hyper-realistic testbed for designing future onboard experiences
AutomotiveUI '18: Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsThis demo presents DriverSense, a novel experimental platform for designing and validating onboard user interfaces for self-driving and remotely controlled vehicles. Most of currently existing vehicular testbeds and simulators are designed to reproduce ...
DriverSense: a hyper-realistic testbed for the design and evaluation of novel user interfaces in self-driving vehicles
AutomotiveUI '18: Adjunct Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsThis paper presents DriverSense, a novel experimental platform for designing and validating onboard user interfaces for self-driving and remotely controlled vehicles. Most of currently existing academic and industrial testbeds and vehicular simulators ...
Research on the Influence of Vehicle Head-Up Display Warning Design on Driver Experience with Different Driving Styles
HCI in Mobility, Transport, and Automotive SystemsAbstractCompared with manual driving, the advanced driver assistance system reduces the pressure on the driver, and at the same time, the driver does not have to pay too much attention to driving safety issues. However, there are also potential conditions ...
Comments