A Human Factors Approach to Validating Driver Models for Interaction-aware Automated Vehicles
DOI: 10.1145/3538705
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the critical challenge of validating driver models used in interaction-aware controllers (IACs) for autonomous vehicles (AVs). While IACs aim to facilitate safe and smooth traffic interactions by predicting human driver responses, their underlying driver models are rarely validated against naturalistic human behavior. The authors argue that relying solely on simplified simulations or abstract test environments is insufficient for ensuring real-world generalization and safety. Consequently, they propose a human-factors-based workflow to validate these models using empirical data from real-world traffic, distinguishing between tactical behavior (maneuver selection) and operational behavior (execution dynamics). The proposed validation workflow consists of three steps: selecting suitable naturalistic datasets, validating tactical behavior, and validating operational behavior. To demonstrate this approach, the authors conducted a case study on an inverse reinforcement learning (IRL)-based driver model replicated from prior research. They utilized the HighD dataset, which contains high-precision trajectory data recorded via drones on German highways. The study focused specifically on lane-changing maneuvers to the left, as these align with the collision-avoidance features of the IRL model’s reward function. The model was trained on 3,279 trajectories, with parameters optimized to maximize the likelihood of observed human demonstrations. The results of the case study revealed significant discrepancies between the model’s predictions and actual human driving behavior. In the tactical validation stage, the model exhibited the correct tactical behavior (i.e., performing a lane change when humans did) in only 40% of the predictions. Furthermore, the operational validation showed that the model’s execution of maneuvers was inconsistent with observed human behavior, particularly regarding safety margins and dynamics. These findings indicate that the IRL-based model fails to generalize to real-world data, highlighting the limitations of current validation practices that rely on simulated environments. The significance of this work lies in establishing a principled framework for evaluating driver models before their integration into AV controllers. By validating models against naturalistic data, researchers can identify inaccuracies that could lead to unsafe or annoying AV behaviors in real traffic. The authors conclude that while some controllers may be robust to prediction errors, validating driver models is essential for ensuring safety, improving model reusability, and providing a basis for future improvements. This workflow supports the development of more reliable interaction-aware controllers by ensuring that the human behavior models they rely on accurately reflect naturalistic driving patterns.
Key finding
An inverse reinforcement learning-based driver model replicated from existing literature demonstrated correct tactical behavior in only 40% of predictions and exhibited operational behavior inconsistent with naturalistic human driving data.
Methodology
simulation_modeling
Sample size: 3279
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 openalex_abstract on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | openalex | — | — | 5 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | openalex | — | — | 2 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-07 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Methodological Resource: validation psychometrics
- Theoretical Contribution: computational model, theory or model