Development of Tools to Model Driver Behavior in a Cooperative and Driverless Vehicle Mixed Roadway Environment

Hunter, Michael; Guin, Angshuman; Saroj, Abhilasha; Bae, Jong In · 2022 · ROSA P / Georgia. Department of Transportation. Office of Performance-Based Management & Research

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 gap in current autonomous vehicle (AV) research regarding the impact of aggressive human-driven vehicles (AHDVs) in mixed-traffic environments. While most existing literature assumes cooperative interactions between AVs and human drivers, this research investigates how human drivers might exploit AV collision-avoidance features to perform aggressive maneuvers, such as abrupt merging and tailgating. The primary objective was to develop and test simulation models of these aggressive behaviors to determine their effect on traffic performance, specifically travel time and roadway capacity. The research employed two distinct methodological approaches. First, the team utilized the open-source microscopic traffic simulation software SUMO to model a freeway exit ramp merge scenario involving three vehicle classes: AVs, standard human-driven vehicles (HDVs), and AHDVs. Two specific aggressive merging behaviors were modeled: "maximum advancement," where AHDVs target the farthest reachable AV to merge immediately in front of it regardless of gap size, and "zipper," where AHDVs target downstream AVs but avoid targeting the same AV simultaneously. Four experiments were conducted to analyze speed changes, travel times under varying traffic demands, and capacity impacts. Second, the researchers developed an Excel-based Simplified Capacity Analysis Tool (SCAT) to evaluate the impact of AVs on departure capacity at signalized intersections, incorporating literature-based saturation flow rates and observed headway data collected via drone video at two Georgia intersections. The simulation results demonstrated that aggressive merging by AHDVs yielded travel-time gains for the aggressive drivers at the direct expense of AVs and cooperative HDVs. These adverse effects were most pronounced in high-congestion scenarios with queuing on deceleration lanes, whereas impacts were muted in low-congestion conditions due to larger vehicle headways. Crucially, Experiment 4 revealed that while overall system capacity remained relatively stable in some scenarios, the interaction between cooperative AVs and aggressive HDVs significantly reduced capacity when cooperative vehicles were positioned to be targeted. Furthermore, increased flow fluctuations were observed, suggesting potential negative impacts on upstream safety and operations. The SCAT analysis highlighted that capacity predictions vary widely depending on AV headway assumptions and platooning characteristics. The study concludes that the potential benefits of AVs, particularly regarding traffic efficiency and safety, may be negated by human aggressive behavior, especially in congested conditions where AV benefits are most needed. The authors identify three critical leading indicators for future AV integration: the prevalence of aggressive interactions, the headways adopted by AV manufacturers, and the spacing and length characteristics of AV platoons. These findings suggest that transportation agencies must monitor these behavioral metrics to inform policy decisions, such as queue management and lane usage restrictions, rather than relying solely on optimistic cooperative assumptions.

Key finding

Aggressive merging behaviors by human drivers targeting cooperative autonomous vehicles negatively impact overall traffic capacity and increase travel times for non-aggressive vehicles, particularly in congested scenarios.

Methodology

simulator

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 (6 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.

• traffic density
• following distance
• situational awareness
• work zones

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