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Professional drivers’ perceptions of automated vehicles and willingness to train for AV integration: a latent profile analysis

Year: 2026

Type: article

Abstract: The adoption of automated vehicles (AVs) is poised to transform driving jobs across the transportation sector. While drivers are aware of AV developments, their attitudes toward AVs and perceptions of AV impacts on their industry vary. It is crucial to understand whether drivers’ perceptions of AVs inform their willingness to train to match drivers’ needs and characteristics with effective workforce development programs for mitigating job displacement and supporting a smooth labor transition. This study analyzed data from a nationwide survey of truck, gig (ride-hailing and delivery services), and taxi drivers to investigate the relationship between drivers’ perceptions of AVs and willingness to train given AV adoption. Using latent profile analysis, we identified three driver profiles (Reluctant Acceptors, Enthusiasts, and Cautious Endorsers) based on their views of AVs, perceptions of how driver responsibilities would change with AVs, predictions of the AV adoption timeline, and anticipated changes in their industry’s workforce size if AVs are adopted. Enthusiasts reported a higher income than Cautious Endorsers. Significant differences were found in drivers’ willingness to participate in three types of training (paid on-the-job, free outside of the company, self-paid) across the three profiles. Enthusiasts were more willing to self-pay for training, while Reluctant Acceptors were more willing to participate in paid-on-the-job training. These findings provide insights into nuances in workers’ willingness to train based on who is paying for training and their perceptions of AVs.

Source: Transportation Research Part A Policy and Practice

DOI: 10.1016/j.tra.2026.105131

Authors: Shubham Agrawal, Jenna A. Van Fossen, Amy M. Schuster, Elizabeth A. Mack, Shelia R. Cotten, Chu-Hsiang Chang

Institutions: Clemson University, Michigan State University

Language: English

Topic: Human-Automation Interaction and Safety

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Estimated APC paid: $3,270