Light My Way: Developing and Exploring a Multimodal Interface to Assist People With Visual Impairments to Exit Highly Automated Vehicles
Luca-Maxim Meinhardt, Lina Madlin Weilke, Maryam Elhaidary, Julia von Abel, Paul D. S. Fink, Michael Rietzler, Mark Colley, Enrico Rukzio
Abstract
The introduction of Highly Automated Vehicles (HAVs) has the potential to increase the independence of blind and visually impaired people (BVIPs). However, ensuring safety and situation awareness when exiting these vehicles in unfamiliar environments remains challenging. To address this, we conducted an interactive workshop with N=5 BVIPs to identify their information needs when exiting an HAV and evaluated three prior-developed low-fidelity prototypes. The insights from this workshop guided the development of PathFinder, a multimodal interface combining visual, auditory, and tactile modalities tailored to BVIP's unique needs. In a three-factorial within-between-subject study with N=16 BVIPs, we evaluated PathFinder against an auditory-only baseline in urban and rural scenarios. PathFinder significantly reduced mental demand and maintained high perceived safety in both scenarios, while the auditory baseline led to lower perceived safety in the urban scenario compared to the rural one. Qualitative feedback further supported PathFinder's effectiveness in providing spatial orientation during exiting.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext a2a0ab9c-8ce3-4fbe-8e85-176639a8e014Cited by top-tier papers1
Ask how each one uses itBuilds on7
- Effects of Semantic Segmentation Visualization on Trust, Situation Awareness, and Cognitive Load in Highly Automated VehiclesMark Colley, Benjamin Eder, Jan Ole Rixen, Enrico RukzioCHI 2021 · 100 citations
- A Longitudinal Video Study on Communicating Status and Intent for Self-Driving Vehicle - Pedestrian InteractionStefanie M. Faas, Andrea C. Kao, Martin BaumannCHI 2020 · 80 citations
- ImageExplorer: Multi-Layered Touch Exploration to Encourage Skepticism Towards Imperfect AI-Generated Image CaptionsJaewook Lee, Jaylin Herskovitz, Yi-Hao Peng, Anhong GuoCHI 2022 · 55 citations
- Autonomous is Not Enough: Designing Multisensory Mid-Air Gestures for Vehicle Interactions Among People with Visual ImpairmentsPaul D. S. Fink, Velin D. Dimitrov, Hiroshi Yasuda, Tiffany L. Chen et al.CHI 2023 · 28 citations
- Shaping Textile Sliders: An Evaluation of Form Factors and Tick Marks for Textile SlidersOliver Nowak, René Schäfer, Anke Brocker, Philipp Wacker et al.CHI 2022 · 19 citations
Related papers
- Hey, What's Going On?: Conveying Traffic Information to People with Visual Impairments in Highly Automated Vehicles: Introducing OnBoardLuca-Maxim Meinhardt, Maximilian Rück, Julian Zähnle, Maryam Elhaidary et al.UbiComp 2024 · 13 citations
- PathFinder: Designing a Map-less Navigation System for Blind People in Unfamiliar BuildingsMasaki Kuribayashi, Tatsuya Ishihara, Daisuke Sato, Jayakorn Vongkulbhisal et al.CHI 2023 · 44 citations
- Improving External Communication of Automated Vehicles Using Bayesian OptimizationMark Colley, Pascal Jansen, Mugdha Keskar, Enrico RukzioCHI 2025 · 7 citations
- Towards Inclusive External Human-Machine Interface: Exploring the Effects of Visual and Auditory eHMI for Deaf and Hard-of-Hearing PeopleWenge Xu, Foroogh Hajiseyedjavadi, Kurtis Weir, Chukwuemeka Eze et al.CHI 2026 · 1 citation
- Guiding Blind Pedestrians in Public Spaces by Understanding Walking Behavior of Nearby PedestriansSeita Kayukawa, Tatsuya Ishihara, Hironobu Takagi, Shigeo Morishima et al.UbiComp 2020 · 48 citations
