General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging

Samir H. A. Mohammad; Wouter Mooi; Arkady Zgonnikov

cs.AIMar 11, 2026

General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging

Authors: Samir H. A. Mohammad, Wouter Mooi, Arkady Zgonnikov

Organizations: Department of Transport and Planning, Delft University of Technology · Department of Cognitive Robotics, Delft University of Technology, The Netherlands

Abstract

Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet current models face a trade-off between interpretability and flexibility. General-purpose large language models (LLMs) offer a promising alternative: a single model potentially deployable without parameter fitting across diverse scenarios. However, what LLMs can and cannot capture about human driving behavior remains poorly understood. We address this gap by embedding two general-purpose LLMs (OpenAI o3 and Google Gemini 2.5 Pro) as standalone, closed-loop driver agents in a simplified one-dimensional merging scenario and comparing their behavior against human data using quantitative and qualitative analyses. Both models reproduce human-like intermittent operational control and tactical dependencies on spatial cues. However, neither consistently captures the human response to dynamic velocity cues, and safety performance diverges sharply between models. A systematic prompt ablation study reveals that prompt components act as model-specific inductive biases that do not transfer across LLMs. These findings suggest that general-purpose LLMs could potentially serve as standalone, ready-to-use human behavior models in AV evaluation pipelines, but future research is needed to better understand their failure modes and ensure their validity as models of human driving behavior.

Figures & tables

Fig. 1: (A) The one-dimensional (1-D) merging task in the same simulated environment from [ 7 ] . Two vehicles emerge from tunnels on the branches of an inverse-Y track and approach a common merge point with no assigned right of way. Initial speeds ( $v_{L}$ , $v_{R}$ ) and projected headway at the merge are varied across 11 kinematic conditions, matching the human dataset of [ 19 ] . (B) Closed-loop LLM driver-agent framework. At each prompt time, the simulation state (panel A) is formatted into a structured prompt (components C0–C5, Table II ) and passed to a general-purpose LLM, which returns a tactical decision, an acceleration plan, and a natural-language rationale. The plan is executed under a receding horizon, after which the system re-queries the LLM with updated state. Both vehicles are controlled in parallel by independent LLM instances with mirrored observations.

Plan horizon $T$	$4.0$ s	Memory window $T_{m}$	$2.0$ s
Prompt rate $f_{p}$	$1$ Hz	Temperature $\gamma$	$1.0$ [-]
Step $\Delta t$	$0.2$ s	Vehicle length $l_{veh}$	4.5 m

TABLE I: Model and simulation parameters.

ID	Description (baseline)
C0	Observations and task context (required): road type (merge vs. straight), vehicle length, relative distance/headway, ego and other velocities, and distance-to-merge when applicable.
C1	History ( $T_{m}$ ): past ego accelerations and past observed other-vehicle velocities.
C2	Previous acceleration plan : acceleration plan returned at the previous prompt, included to encourage plan continuity under re-planning.
C3	Behavioral constraints : collision avoidance, human-likeness, conservative distance keeping, and periodic reassessment.
C4	Tactical plan : explicit merge-first vs. yield choice before generating the continuous control plan (merging scenarios only).
C5	Output schema (required): optional M/Y token, 2–3 sentence rationale, and a length- $N$ normalized acceleration array $\hat{\mathbf{u}}\in[-1,1]^{N}$ returned in a Python code block (array only).

TABLE II: Prompt components (C) and single-factor ablations (A).

Fig. 2: Baseline behavioral comparison between humans and LLM agents (collisions excluded). (A–B) Left-vehicle velocity time series after exiting the tunnel ( $t=0$ ), pooled across kinematic conditions, separated by whether the left vehicle merges first (A) or second (B) , illustrating piecewise-linear velocity profiles characteristic of intermittent acceleration control. (C) Probability that the left vehicle merges first as a function of projected headway advantage, shown separately for each initial relative-velocity condition $\Delta v\in\{-0.8,0.0,+0.8\}$ m/s. (D) Mean gap at the merge point versus projected headway advantage, per $\Delta v$ condition. (E) Mean per-vehicle RMSE of velocity deviation from the initial velocity versus projected headway advantage, per $\Delta v$ condition (each trial contributes two RMSE values: left and right vehicle). Error bars indicate 95% confidence intervals ( C : Wilson intervals for proportions; D–E : t-based intervals for means).

	b	SE	Z	p
Intercept - H	-0.30	0.135	-2.26	0.024
Intercept - o3	0.44	0.340	1.29	0.198
Intercept - G	0.08	0.301	0.26	0.792
Projected headway - H	1.11	0.075	14.89	$<.001$
Projected headway - o3	1.11	0.243	4.56	$<.001$
Projected headway - G	0.50	0.125	3.97	$<.001$

TABLE III: Logistic regression models of which driver merged first. Human (H): n=962; o3: n=109; Gemini 2.5 Pro (G): n=82.

	Est.	SE	t	p
Intercept - H	3.54	0.146	24.30	$<.001$
Intercept - o3	8.14	0.483	16.86	$<.001$
Intercept - G	2.73	0.335	8.16	$<.001$
Abs. projected headway - H	0.17	0.034	5.00	$<.001$
Abs. projected headway - o3	0.46	0.114	4.03	$<.001$
Abs. projected headway - G	0.40	0.082	4.95	$<.001$

TABLE IV: Linear regression models of the gap that drivers keep between the vehicles at the merge point. Human (H): n=962; o3: n=109; Gemini 2.5 Pro (G): n=82.

Indicator	H	o3	Gemini 2.5 Pro
Qualitative
The probability of merging first increases with projected headway advantage.	$\checkmark$	$\checkmark$	$\checkmark$
The probability of merging first decreases with relative velocity advantage.	$\checkmark$	X	X
The safety gap size is moderately positively affected by projected headway.	$\checkmark$	$\checkmark$	$\checkmark$
The safety gap size is not affected by initial relative velocity difference.	$\checkmark$	$\checkmark$	$\checkmark$
Drivers resolve the merging conflict with intermittent, piecewise-linear velocity profiles.	$\checkmark$	$\checkmark$	$\checkmark$

TABLE V: Selected qualitative and quantitative indicators of human (H) behavior during merging conflicts.

	o3						Gemini 2.5 Pro
Setting	I1	I2	I3	I4	I5	S	I1	I2	I3	I4	I5	S
Baseline	✓	X	✓	✓	✓	4/5	✓	X	✓	✓	✓	4/5
A1 (-History)	✓	X	✓	✓	✓	4/5	✓	X	X	✓	✓	3/5
A2 (-Prev. acc. plan)	✓	✓	✓	✓	✓	5/5	✓	X	X	✓	X	2/5
A3 (-Safety)	✓	X	✓	✓	✓	4/5	✓	X	✓	✓	✓	4/5
A4 (-Human)	✓	X	✓	✓	✓	4/5	✓	X	✓	✓	✓	4/5

TABLE VI: Alignment of model behavior with qualitative indicators (I) of human behavior across seven prompt ablations. I1: headway

\uparrow

merge-first; I2:

\Delta v\uparrow

merge-first

\downarrow

; I3: headway

\uparrow

gap; I4: gap

\perp\,\Delta v

; I5: piecewise-constant control. S: total score (out of 5).

	o3				Gemini 2.5 Pro
Setting	Coll. (%)	Gap (m)	RMSE	Joint (%)	Coll. (%)	Gap (m)	RMSE	Joint (%)
Baseline	0.00	9.28	1.34	94.50	25.45	3.84	0.70	42.68
A1 (-History)	2.73	9.29	1.30	95.33	38.18	4.82	0.85	76.47
A2 (-Prev. acc. plan)	2.73	10.63	1.50	100.00	16.36	5.14	0.86	47.83
A3 (-Safety)	3.64	9.06	1.24	98.11	30.91	3.68	0.63	47.37
A4 (-Human)	1.82	9.47	1.32	98.15	14.55	4.39	0.76	41.49

TABLE VII: Quantitative performance indicators of the models across prompt ablations. Human reference values: collision=2.83%, gap=3.85 m, RMSE=0.66 m/s, joint rate=53.0%.

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Large language models (LLMs) are promising for autonomous driving, but semantics-only decision policies can yield physically unsafe behavior in dynamic traffic. Existing methods either perform online language reasoning without explicit dynamics verification or use world models mainly in offline pipelines, leaving a gap between semantic intent and physical feasibility at decision time. We propose Reason--Imagine--Act (RIA), a closed-loop framework that couples an LLM reasoner with an action-conditioned world model for online safety verification. At each step, the LLM proposes an action template and candidate sub-actions, the world model performs short-horizon rollouts, and a safety scorer selects the safest executable action with feedback to the next reasoning step. Under a unified CARLA point-goal protocol (1000 episodes), RIA achieves 80.05% route completion, 51.10% arrival rate, and 0.20% collision rate. Under the same closed-loop interface, RIA consistently outperforms training-free baselines, including CARLA TM and MADA, on core closed-loop metrics. For reproducibility, code is available at https://github.com/pku-smart-city/source_code/tree/main/RIA.

Abstract

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