Behavioral adaptation of drivers when driving among automated vehicles

Aramrattana, Maytheewat; Fu, Jiali; Selpi · 2022 · Crossref

DOI: 10.1108/jicv-07-2022-0031

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates whether human drivers adapt their behavior when driving among automated vehicles (AVs) compared to manually driven vehicles (MVs) in mixed-traffic scenarios. As AVs are deployed, understanding how human drivers react to them is critical for assessing safety and traffic flow impacts. The research specifically examines behavioral adaptation in two distinct driving contexts: driving on a main highway and merging from an on-ramp. Unlike previous studies that often used fixed AV formations like platoons, this study simulates "free flow" conditions where AVs regulate themselves dynamically. The researchers conducted a driving simulator experiment with 18 participants (average age 44) using the SimIV moving-based simulator at the Swedish National Road and Transport Research Institute. The experimental design employed a within-subject approach, where each participant experienced four scenarios: main highway and on-ramp merging, each with surrounding traffic consisting entirely of either MVs or AVs. MVs were modeled using a modified Intelligent Driver Model (H-IDM) calibrated to real-world data, while AVs were modeled using an Adaptive Cruise Control (ACC) model based on TransAID project parameters. Data collected included average time gap, number of lane changes, overall speed, and car-following speed. The results indicate that drivers do exhibit behavioral adaptation, but the nature of this adaptation depends heavily on the specific driving scenario rather than simply mirroring the surrounding traffic's characteristics. In the on-ramp scenario, participants reduced their average time gap from 2.3 seconds (among MVs) to 1.3 seconds (among AVs), despite AVs maintaining longer gaps. Conversely, on the main highway, participants increased their time gap from 3.0 to 3.5 seconds when driving among AVs. Across both scenarios, participants drove at lower overall speeds and performed fewer lane changes when surrounded by AVs. Statistical analysis revealed significant differences in behavior for over 90% of participants, though the direction of adaptation varied among individuals. The study concludes that driver adaptation is scenario-dependent and does not always align with the behavior of surrounding AVs. For instance, drivers accepted shorter gaps during merging despite AVs keeping longer distances, likely due to AVs occupying slower lanes and creating congestion that forced earlier merges. This finding contrasts with prior research suggesting drivers simply mimic AV time headways. The authors highlight limitations regarding short scenario durations and the lack of mixed traffic types in the simulation. They recommend future studies with longer observation periods, mixed AV/MV traffic compositions, and varied AV behavioral models to better understand long-term adaptation and complex interactions.

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.

Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• traffic density
• acceptance adoption
• following distance
• automation surprise
• speed choice
• situational awareness

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data
• Methodological Resource: tool software
• Theoretical Contribution: computational model