Deployment Scenarios for Vehicles with Higher-Order Automation
DOI: 10.1007/978-3-662-48847-8_10
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Summary
This paper analyzes three distinct deployment scenarios for vehicles with higher-order automation (conditional, high, or full automation), aiming to determine the realism of delegating driving tasks to computers. Motivated by the potential for automation to drastically reduce traffic fatalities and improve mobility efficiency, the author compares development trends driven by different industry players: established automakers, non-automotive technology companies, and start-ups. The study utilizes the SAE International J3016 taxonomy to define automation levels and extrapolates publicly available knowledge regarding technological, economic, and infrastructural factors to project future implementation paths. The first scenario, termed "Evolutionary," is pursued by the traditional auto industry through the continuous improvement of driver assistance systems. This approach involves a gradual shift from partial automation (e.g., traffic jam assistants) to higher-order automation, prioritizing safety and maintaining an unrestricted geographical range. However, widespread adoption is expected to be slow, potentially taking 15–20 years for technologies to become standard, meaning most vehicles will likely require driver interaction in the near future. The second scenario, "Revolutionary," is driven by IT companies using artificial intelligence and learning algorithms to achieve a disruptive leap to fully automated driving. These companies aim to redesign personal mobility, potentially deploying automated taxis or delivery services in limited regions before expanding globally, leveraging their core business in online services. The third scenario, "Transformative," involves start-ups and service providers merging personal mobility with public transportation through Automated Mobility on Demand (AMOD) systems. These low-speed, limited-area vehicles target urban congestion and "first/last mile" connectivity, offering a more immediate implementation path than the other two scenarios. The paper compares these scenarios across systemic, technical, regulatory, and corporate dimensions. Systemically, the evolutionary scenario offers limited automation over unlimited ranges, while the revolutionary and transformative scenarios offer high automation over restricted ranges. Technically, the evolutionary approach requires highly failsafe, low-maintenance, and cost-efficient components for mass-market reliability, whereas the transformative scenario favors specialized, high-accuracy systems supported by professional supervision and robust communication infrastructure. Regulatory challenges vary significantly; the evolutionary scenario faces complex public road regulations, while the transformative scenario may operate under special rules in restricted areas like amusement parks. Corporate strategies also differ, with automakers proceeding cautiously to protect brand reputation, IT companies pursuing long-term revenue models through connected services, and start-ups focusing on new business models with lower labor costs. The significance of this analysis lies in identifying that while full automation on public roads remains a long-term goal, limited deployments in restricted areas (transformative) or specific services (revolutionary) are more likely to occur in the near term. These limited implementations provide critical data on vehicle-public interactions and safety measures, which can inform the broader evolutionary rollout. The paper concludes that the field will likely see a mix of these approaches, with transformative and revolutionary scenarios serving as testing grounds that pave the way for the eventual integration of higher-order automation into general traffic.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
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- driverless ads
- acceptance adoption
- situational awareness
- automation surprise
- last mile delivery
- automation
Information type
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- Theoretical Contribution: conceptual framework