What Do We Want from Autonomous Vehicles (AVs)? Using Participatory Planning and Scenario Analysis of Alternative Features to Identify Stakeholders’ Desired Outcomes from the Strategic Deployment of Emerging Transportation Technology

Garrick, Norman; Atkinson-Palombo, Carol · 2019 · ROSA P / University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education

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 need to reframe public discourse on autonomous vehicles (AVs) from a reactive stance of accommodation to a proactive inquiry into desired societal outcomes. The authors argue that current debates often treat AV adoption as inevitable, neglecting stakeholder agency and potential unintended consequences. To determine what outcomes society prefers, the research employs a participatory planning approach combined with scenario analysis, grounded in a Transportation Sustainability framework. This framework evaluates AV impacts across environmental, social, and economic domains, aiming to maximize benefits while minimizing costs such as inequality, job loss, and safety risks. The methodology involved a public workshop in Bloomfield, Connecticut, structured around three scenarios adapted from the National Issues Forum: (1) promoting human control behind the wheel, (2) preserving jobs and expanding employment, and (3) supporting rapid development of driverless vehicles for safety and traffic improvement. Participants included general citizens, and their perspectives were subsequently shared with Connecticut state legislators through structured interviews to inform ongoing legislation. The study utilized survey data and qualitative feedback to assess stakeholder preferences regarding regulation, infrastructure, and equity. The results indicate a strong preference for retaining human control, with 65% of respondents favoring laws requiring a licensed human operator at all times, even if roads are not safer. Participants valued human moral judgment and adaptability over machine reliability, particularly concerning weather conditions and complex driving environments. While they acknowledged benefits of autonomous features, they insisted on legal provisions allowing human override. There was significant support for using AVs as mass transit rather than personal vehicles, driven by concerns that private AV ownership would exacerbate existing transportation inequalities. Additionally, 50% of participants supported worker retraining programs to mitigate job losses in the transportation sector. Participants opposed loose regulations, with 70% rejecting policies that prioritize speed of development over safety and security. The significance of this work lies in its challenge to the dominant narrative of technological inevitability. By highlighting stakeholder priorities—specifically the demand for human oversight, equitable access via public transit, and job preservation—the study provides a roadmap for policymakers to craft legislation that aligns with public values. It underscores the tension between technological advancement and social equity, suggesting that strategic deployment of AVs must address liability, cybersecurity, and infrastructure costs to avoid reinforcing societal vulnerabilities. The findings advocate for a deliberative process where technology adoption is shaped by informed public inquiry rather than market forces alone.

Key finding

Participants strongly favored retaining human control behind the wheel, with 65% supporting laws requiring a licensed operator even if roads might not be safer, and 80% favoring the expansion of public transportation to reduce reliance on self-driving vehicles.

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 (7 acquisition events logged).

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.

• acceptance adoption
• driverless ads