Self-Driving Electric Vehicles for Smart and Sustainable Mobility: Evaluation and Feasibility Study for Educational and Medical Campuses
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Summary
This study evaluates the technical feasibility, public acceptance, regulatory requirements, and economic viability of deploying low-speed, self-driving electric shuttles, specifically the Olli vehicle, within educational and medical campuses. Motivated by the potential of automated vehicles (AVs) to enhance transportation safety, increase system capacity, and solve first- and last-mile connectivity issues, the research was conducted by the University at Buffalo and partners for the New York State Energy Research and Development Authority (NYSERDA) and the New York State Department of Transportation. The project aimed to determine if such technology could be safely and sustainably integrated into controlled environments like the Buffalo-Niagara Medical Campus (BNMC). The methodology comprised three interrelated components. First, the Olli shuttle underwent rigorous safety testing on the University at Buffalo’s Proving Grounds for Connected and Automated Vehicles. This involved twelve specific scenarios assessing driving behavior, including turn performance, stopping distances, pedestrian identification (stationary and moving), vehicle following, object detection, and obstruction handling. Additional tests evaluated performance in inclement weather and power consumption. Second, public acceptance was analyzed through surveys of Olli riders, a 2016 web survey, and a 2019 AV forum, utilizing binary and ordered choice analyses to identify factors influencing public perception. Third, the study addressed regulatory barriers by reviewing existing state and federal legislation, leading to the development of the “Buffalo Principles” for AV deployment. Finally, a simulation model and business case analysis were conducted to estimate mobility demand and financial viability for a fleet serving BNMC employees under public, private, and public-private partnership models. Key findings indicated that Olli performed reliably in most controlled scenarios, successfully identifying pedestrians and navigating intersections, though it struggled with static obstructions like snowbanks that blocked its sensors. Public acceptance surveys revealed that direct experience with the shuttle significantly increased rider confidence and comfort, with many respondents reporting the ride felt comparable to human-driven shuttles. The regulatory analysis highlighted gaps in current Federal Motor Vehicle Safety Standards, which are designed for human drivers, and proposed the Buffalo Principles: testing on private roads, using slow speeds, recording data, and employing integrated simulation. The business case analysis demonstrated that deploying a small fleet of Olli shuttles for first- and last-mile transport at BNMC was financially viable, particularly under public-private partnership structures, offering a sustainable solution for campus mobility. The significance of this work lies in its comprehensive approach to AV deployment, bridging technical testing, public policy, and economic analysis. The study provides a framework for sustainable AV testing through the Buffalo Principles and suggested legislative language for New York State. By demonstrating the technical reliability of low-speed shuttles in controlled environments and validating their economic feasibility for specific use cases like medical campuses, the report offers actionable insights for policymakers and transit authorities. It underscores that while regulatory and technical hurdles remain, targeted deployments in confined areas can effectively address mobility challenges while fostering public acceptance and informing broader AV integration strategies.
Key finding
The Olli shuttle demonstrated safe and reliable operation in controlled safety tests, and public acceptance of automated vehicles increased significantly following direct exposure and educational interventions.
Methodology
mixed_methods
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.
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