Connectivity and Automation as Enablers for Energy-Efficient Driving and Road Traffic Management
DOI: 10.1007/978-3-031-84483-6_128
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Summary
This presentation by Nicholas Chase of the U.S. Energy Information Administration (EIA) examines the potential energy implications of autonomous vehicles (AVs) within the context of the Annual Energy Outlook 2018 (AEO2018). The study is motivated by the significant role of on-road vehicles, which accounted for 31% of U.S. delivered energy consumption in 2017. The paper outlines the dual nature of AV impacts: while automation offers benefits such as improved safety, route harmonization, and reduced congestion, it also faces obstacles including consumer acceptance, cybersecurity, and legal frameworks. Crucially, the energy impact is uncertain, potentially ranging from a 75% decrease to a 200% increase in light-duty vehicle travel demand, depending on factors like ridesharing adoption, empty miles, and mode switching. To quantify these uncertainties, the EIA modeled three scenarios: a Reference case and two alternative cases focusing on widespread AV adoption. In the Reference case, AVs comprise only 1% of new light-duty vehicle sales by 2050, are powered exclusively by conventional gasoline internal combustion engines, and are used intensively (65,000 miles/year). The two alternative cases assume AVs reach 31% of new sales by 2050, with increased usage for both fleet and household vehicles. These cases differ by powertrain: the Autonomous Battery Electric Vehicle (BEV) case assumes 96% of fleet and 82% of household AVs are battery electric by 2050, while the Autonomous Hybrid Electric Vehicle (HEV) case assumes 96% of fleet and 71% of household AVs are hybrid electric. Both alternative cases also include automation-enabled platooning for long-haul commercial trucks and shifts in mass transit usage, specifically increased commuter rail use and decreased transit rail use. The results indicate that while total transportation energy consumption is higher in the alternative cases compared to the Reference case, it remains lower than 2017 levels. Light-duty vehicle miles traveled are projected to be 14% above the Reference case in 2050, representing a 35% increase over 2017 levels. However, the shift toward electric and hybrid powertrains significantly improves fuel economy. Consequently, light-duty vehicle energy consumption in 2050 is only 7% to 10% above the Reference case, yet 23% to 29% lower than 2017 consumption. The analysis highlights that while automation increases travel demand, the adoption of efficient powertrains mitigates overall energy use, demonstrating that technology assumptions critically influence long-term energy projections.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
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| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | failed | — | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| 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.
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