Exploring the Effects of Vehicle Automation and Cooperative Messaging on Mixed Fleet Eco-Drive Interactions
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Summary
This study investigates the impact of vehicle automation and cooperative driving automation (CDA) on driver behavior and perceptions within mixed fleets, specifically when following a lead vehicle employing eco-driving strategies. As automated vehicles become more common, understanding how human drivers interact with vehicles that optimize speed profiles to reduce emissions and idling is critical for safety and infrastructure integration. The research addresses the challenge that eco-driving behaviors, such as early deceleration at signalized intersections, may appear inefficient or frustrating to conventional drivers who lack access to the underlying signal timing data. The study aims to determine if adaptive cruise control (ACC) and CDA messages—conveying either vehicle-to-infrastructure (V2I) signal status or vehicle-to-vehicle (V2V) lead vehicle intent—can facilitate smoother interactions and increase driver trust. The research comprised two phases: a preliminary study to select effective CDA message designs and a field study to evaluate driver behavior. The preliminary study involved 24 participants who reviewed various V2I and V2V message designs via virtual conferencing to identify clear and understandable visual cues. The subsequent field study utilized a research vehicle to record speed, braking variability, and following distance while participants drove under four conditions: no automation or messaging, ACC only, CDA messages only, or both ACC and CDA messages. Participants followed a lead vehicle simulating Level 3 cooperative eco-driving. The study analyzed speed fluctuations, following distances, stopping patterns, and driver trust through statistical analysis and post-experiment questionnaires. Results indicated that drivers using ACC-enabled vehicles followed the eco-driving lead vehicle with greater ease and exhibited fewer speed fluctuations compared to those in conventional vehicles. The impact of CDA messages varied by vehicle type: V2V messages (sharing lead vehicle intent) were beneficial for drivers using ACC, while V2I messages (providing signal status) assisted conventional vehicle drivers to a lesser degree. Drivers in ACC vehicles maintained more consistent following distances. Regarding safety, the study found that ACC-enabled drivers without CDA messages were more likely to run red lights when following the eco-driving vehicle, suggesting that lack of context regarding the lead vehicle’s deceleration can lead to unsafe maneuvers. Trust ratings for the lead vehicle and CDA messages were generally high, though overwhelming acceptance of automation was not observed; however, participants reported slightly higher perceptions of safety gains than losses. The findings suggest that integrating ACC and targeted CDA messaging can mitigate the friction caused by eco-driving behaviors in mixed fleets. Specifically, providing intent-sharing information helps automated vehicles maintain safe following distances, while signal status information aids conventional drivers in understanding unexpected deceleration. These results imply that for the successful deployment of eco-driving technologies, infrastructure and vehicle systems must provide contextual information to surrounding drivers to prevent confusion and ensure safety. The study highlights the importance of designing CDA messages that are intuitive and tailored to the specific automation capabilities of the following vehicle.
Key finding
Drivers in ACC-enabled vehicles followed eco-driving lead vehicles with more ease and fewer speed fluctuations than drivers in conventional vehicles, while CDA messages provided differential benefits based on vehicle automation status.
Methodology
field_study
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.
- ehmi external hmi
- eco driving
- automation surprise
- v2x connected vehicle
- situational awareness
- vru facing ehmi
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).
- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: computational model