Comparing Merging Behaviors of Drivers with Vehicles Equipped with Level 3 Automation and Connected Messaging when Merging in a Mixed Vehicle Fleet Environment of Various Traffic Densities

Calvo, Jose; Svancara, Austin; Chao, Szu-Fu; Lee, Yi-Ching; Jannat, Mafruhatul; Arnold, Michelle · 2025 · ROSA P / United States. Federal Highway Administration. Office of Safety and Operations Research and Development

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Summary

This study investigates how drivers respond to merging scenarios in mixed-traffic environments containing vehicles equipped with Level 3 Automated Driving Systems (ADS) and Cooperative Driving Automation (CDA). Merging is a high-risk maneuver often causing congestion and crashes, yet research on ADS performance in these specific contexts, particularly regarding driver trust and interaction with automated platoons, remains limited. The research aims to determine if Level 3 automation and CDA messaging improve safety and efficiency during onramp merges, and how factors like platoon size influence driver behavior and system acceptance. The researchers conducted a driving simulator experiment with 96 licensed drivers using a mixed-factor design. Participants were assigned to either drive a conventional vehicle or a Level 3 ADS-equipped vehicle. Additionally, half of the participants received CDA alerts from approaching automated platoons, which communicated platoon size and advised merging behind the group, while the other half received no such alerts. Each participant experienced four merge events involving platoons of one, three, five, or seven vehicles. The study measured objective driving metrics, including gap acceptance, braking, speed variability, acceleration, steering angle, and collision likelihood, as well as eye-tracking data to assess visual attention. Post-drive questionnaires evaluated participant trust in the ADS and awareness of system features. Results indicated that Level 3 automation and CDA messaging influenced driver safety behaviors, particularly as platoon sizes increased. Drivers using the Level 3 system showed distinct patterns in automation disengagement, with takeover likelihood affected by trust levels, platoon size, and age. Eye-tracking data revealed changes in fixation duration and frequency on the automated platoon and instrument cluster depending on the presence of CDA alerts. However, subjective measures highlighted significant issues with user understanding; participants were often unclear about which features were active, leading to incongruities between their expectations and the vehicle’s actual behavior. This confusion impacted trust and comfort levels, suggesting that while the technology can alter driving dynamics, user interface clarity is a critical barrier to effective adoption. The findings suggest that while Level 3 ADS and CDA technologies have the potential to enhance merging safety and efficiency, their effectiveness is moderated by driver trust and system transparency. The study implies that for these technologies to be successfully integrated into mixed fleets, designers must address the gap between user expectations and system capabilities. Clear communication of active features and improved user education are necessary to prevent mistrust and ensure that drivers can safely interact with automated systems during complex maneuvers like merging. These insights are valuable for transportation engineers and policymakers developing safety standards for cooperative driving automation.

Key finding

Driver merging safety behaviors were influenced by Level 3 automation and cooperative driving automation messaging, with effects becoming more pronounced as the size of the autonomous vehicle platoon increased.

Methodology

simulator

Sample size: 96

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• traffic density
• situational awareness
• automation surprise
• automation

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: conceptual framework, computational model