CityMobil
DOI: 10.3141/2110-01
archive: archived pipeline: cataloged verified
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Summary
This paper addresses human factors challenges associated with the introduction of dual-mode vehicles capable of switching between manual and highly automated driving, specifically within the context of the European CityMobil project. The study focuses on the "eLane" concept, a specially equipped lane designed to facilitate automated driving by combining road infrastructure with vehicle automation. Key human factors concerns identified include the transition of control between driver and system, potential loss of driving skill, and driver responses to system errors or unexpected events. The primary research question investigates how different human-machine interface (HMI) designs affect driver performance, awareness, and acceptance during these transitions and failure scenarios. To evaluate these issues, the authors conducted a driving simulator experiment involving 24 participants. The study employed a within-subject design comparing two interface modalities: an acoustic interface (using beeps and visual displays) and a vocal interface (using spoken words, beeps, and visual displays). Participants drove a simulated 17-km track featuring eLane segments where automation was simulated using the "Wizard of Oz" technique, meaning an experimenter manually controlled the vehicle while participants believed the system was automated. Drivers performed a primary driving task and a secondary distraction task involving an in-vehicle information system. The experiment tested driver reactions to entering and exiting eLanes, system failures, and infrastructure outages, measuring response times, driving performance, and subjective evaluations via questionnaires. The results indicated that both interfaces were generally usable, but the vocal modality yielded superior performance in specific areas. Drivers using the vocal interface maintained significantly lower average speeds during manual baseline driving (55 km/h vs. 61 km/h), suggesting higher situational awareness. In critical failure scenarios, the vocal interface resulted in significantly shorter reaction times for taking control of the vehicle, although the percentage of drivers who successfully took control was similar across both groups (85%). Subjectively, over 70% of participants preferred the vocal modality, finding it more comprehensible, soothing, and less distracting. However, the vocal interface showed a drawback in infrastructure outage scenarios, where a significantly higher number of drivers failed to recognize the system was out of work compared to the acoustic condition. Drivers generally trusted the system and preferred a mixed automatic-manual approach, reserving automation for straight, high-velocity roads and manual driving for complex urban or overtaking situations. The study concludes that HMI design significantly influences driver behavior and safety in automated transport systems. While the eLane concept is well-accepted, the vocal interface is recommended for critical transitions and fault events due to its ability to reduce reaction times and enhance comprehension. However, the design must be optimized to ensure drivers correctly interpret infrastructure status failures. The findings underscore the importance of tuning automation systems to human capabilities, ensuring clear communication during control transitions, and maintaining driver engagement to prevent skill loss and mode confusion. These insights provide a foundation for future research and development in urban automated transport, emphasizing the need for robust interfaces that support safe handovers between manual and automated modes.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-07 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-09 |
| extract | success | pdftotext | — | — | 2 | 2026-06-09 |
| clean | success | clean | — | — | 1 | 2026-06-09 |
| chunk | success | chunk | — | — | 1 | 2026-06-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-09 |
| promote | success | — | — | — | 1 | 2026-06-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-09 |
| tag | success | vector_similarity | — | — | 8 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-09 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-09; verification: verified.
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- Applied Guidance: design guidelines
- Theoretical Contribution: conceptual framework, computational model