Advancing eco-driving strategies for drivers and automated vehicles traveling within intersection vicinities : final report.
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This study addresses the significant contribution of light-duty vehicles to U.S. carbon dioxide and other harmful emissions, particularly within intersection vicinities where frequent stops and accelerations occur. The research aims to develop and evaluate real-time eco-driving strategies for both human drivers and automated vehicles. Specifically, it seeks to determine the most effective advising strategies for drivers regarding the type (audio vs. visual) and frequency of suggestions, and to assess the potential emission mitigations for automated vehicles under varying traffic conditions. The researchers developed distinct real-time eco-driving models for drivers and automated vehicles based on real-time data such as signal timing, distance to the stop bar, and queue length. For human drivers, the model was implemented in a high-fidelity driving simulator with thirty-one participants. The study tested different advising strategies, varying the output modality (audio or visual) and the frequency of suggestions. For automated vehicles, the model was applied within the VISSIM traffic simulation platform. This model classified driving behaviors into six specific situations, such as cruising, accelerating to pass, or decelerating due to queues, allowing vehicles to adjust their behavior second-by-second. Simulations were conducted under different traffic conditions, with volume-to-capacity ratios ranging from 0.3 to 0.8. Vehicle emissions for both experimental setups were estimated using the MOVES method. The results indicate that all tested eco-driving scenarios for human drivers were effective in reducing emissions. Among the strategies, the audio-based eco-driving advice delivered at a 10-second interval proved to be the most effective in minimizing emissions. However, the study noted that these eco-driving scenarios resulted in increased travel time compared to baseline driving. For automated vehicles, the implementation of real-time eco-driving suggestions led to a 20% reduction in CO2 emissions. The findings also highlighted that emission levels for automated vehicles were dependent on prevailing traffic conditions. The significance of this work lies in providing actionable algorithms for the automotive industry and in-vehicle device manufacturers. The study offers specific recommendations for designing human-machine interfaces that are both effective in reducing emissions and acceptable to drivers, identifying audio feedback at moderate intervals as optimal. Furthermore, it demonstrates the substantial potential for automated vehicles to mitigate emissions through precise, real-time behavioral adjustments, contributing to the development of connected and autonomous vehicle technologies that prioritize environmental sustainability.
Key finding
Audio eco-driving advice with a 10-second interval was the most effective strategy for reducing driver emissions, while automated vehicles achieved a 20% CO2 reduction through real-time eco-driving guidance.
Methodology
mixed_methods
Sample size: 31
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. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Methodological Resource: tool software
- Theoretical Contribution: computational model