Abhishek Mukhopadhyay
Pradipta Biswas
ABSTRACT
With semi-automated vehicles, drivers are still required to be ready to intervene upon a takeover request (TOR) and face the difficulty of achieving their optimal performance level directly after a passive phase. In this work, we examine the effects of using Extended Reality (XR) interface to assist drivers for taking over and in the first seconds of controlling the vehicle. We focus on developing a lane detection algorithm to keep the vehicles follow the ego lanes. We present a prototype of an Augmented Reality (AR) and Mixed Reality (MR) assistance system realized on a simulated environment. In a user study, we compare mixed reality with augmented reality display and present results on response time to take over request and found AR is significantly better than MR interfaces.
Supplemental Material
- SAE International (2018). Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (Standard No. J3016)Google Scholar
- Pavlo Bazilinskyy, Alexander Eriksson, Bastiaan Petermeijer, and Joost de Winter. 2017. Usefulness and satisfaction of take-over requests for highly automated driving. Road Safety & Simulation International Conference (RSS 2017), the Hague, Netherlands.Google Scholar
- Roderick McCall, Fintan McGee, Alexander Meschtscherjakov, Nicolas Louveton, and Thomas Engel. 2016. Towards A Taxonomy of Autonomous Vehicle Handover Situations. In Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (Automotive'UI 16). Association for Computing Machinery, New York, NY, USA, 193–200. https://doi.org/10.1145/3003715.3005456.Google ScholarDigital Library
- Stephen M. Casner, Edwin L. Hutchins, and Don Norman. 2016. The challenges of partially automated driving. Commun. ACM 59, 5 (May 2016), 70–77. https://doi.org/10.1145/2830565.Google ScholarDigital Library
- Remo M.A. van der Heiden, Shamsi T. Iqbal, and Christian P. Janssen. 2017. Priming Drivers before Handover in Semi-Autonomous Cars. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, NY, USA, 392–404. https://doi.org/10.1145/3025453.3025507.Google ScholarDigital Library
- Brian Mok, Mishel Johns, Key Jung Lee, Hillary Ive, David Miller, and Wendy Ju. 2015. Timing of unstructured transitions of control in automated driving. In 2015 IEEE Intelligent Vehicles Symposium (IV), 1167–1172.Google ScholarCross Ref
- Matthias Kuehn, Tobias Vogelpohl, and Mark Vollrath. 2017. Takeover Times in Highly Automated Driving (Level 3). In 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration. Unfallvorschung der Versicherer (UDV), Wilhelmstraße 43 / 43G; 10117 Berlin, 11.Google Scholar
Index Terms
- Exploring the Use of XR Interfaces for Driver Assistance in Take Over Request
Recommendations
Exploring the Use of Augmented Reality Interfaces for Driver Assistance in Short-Notice Takeovers
2019 IEEE Intelligent Vehicles Symposium (IV)With conditionally automated vehicles (Level 3), drivers are still required to be ready to intervene upon a takeover request (TOR) and face the difficulty of achieving their optimal performance level directly after a passive phase. In this work, we ...
Toward overtrust-free advanced driver assistance systems
Special Issue on Human-automation CoagencyAvoiding human overtrust in machines is a vital issue to establish a socially acceptable advanced driver assistance system (ADAS). However, research has not clarified the effective way of designing an ADAS to prevent driver overtrust in the system. It ...
Video-Based Driver Assistance—From Basic Functions to Applications
Image sequences recorded with cameras mounted in a moving vehicle provide information about the vehicle's environment which has to be analysed in order to really support the driver in actual traffic situations. One type of information is the lane ...
Comments