Understanding first responders’ familiarity and attitudes toward connected and automated vehicles (CAVs): insights from latent class analysis

Connected and Automated Vehicles (CAVs) are emerging technologies with the potential to address critical highway transportation challenges, including safety, mobility, and environmental impacts. Extensive research has examined and anticipated the implications of these technologies on road users such as vehicle occupants, pedestrians, and cyclists. However, their impacts on first responders—such as police, firefighters, and towing operators—who play critical roles in highway transportation systems, remain underexplored. Understanding first responders’ perceptions of CAVs is essential because they are often the first to interact with these technologies during traffic incidents, emergencies, or routine enforcement activities. Their readiness, adaptability, and trust in these technologies can significantly influence their effectiveness in ensuring public safety and responding to emergencies. It is hypothesized that their perceptions are influenced by their knowledge or familiarity with these technologies. Therefore, the objective of this study is to explore first responders’ familiarity with and attitudes toward CAVs through a national survey. The collected survey data were analyzed to derive meaningful insights and inform strategies to support first responders in their interactions with CAVs. To capture the unobserved heterogeneity in first responders’ familiarity with CAVs, this study employed latent class analysis (LCA). Two latent classes were identified: CAV-familiar and non-CAV-familiar. The membership model indicated that first responders working for towing agencies and Departments of Transportation (DOTs), those from FEMA Region 10, and those with near-miss or struck-by incident experiences were more likely to belong to the CAV-familiar class (familiar with CAVs). To further analyze the first responders’ attitudes toward CAVs, three chi-squared tests were conducted to compare responders from the two latent classes. The results indicated that responders in the CAV-familiar class exhibited higher levels of trust in CAVs, both as vehicle users and as professionals, compared to those in the non-CAV-familiar class. Despite these differences, responders from both classes emphasized the importance of CAV-related training and standardization to enhance their safety on roadways involving CAVs. The findings of this study reveal two distinct latent classes of first responders—CAV-familiar and non-CAV-familiar—and demonstrate that agency type, geographic region, and prior incident experience significantly influence class membership. Responders in the CAV-familiar class showed higher levels of trust in CAVs, though overall trust remained low. Across both classes, respondents emphasized the urgent need for CAV-related training and standardized operational protocols to ensure safe and effective responses in mixed traffic environments.