Fuzzy Logic-based Techniques for Human Driver Behavior Modelling during a Simple Lane-changing Task
DOI: 10.1016/j.ifacol.2024.07.406
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Summary
This paper addresses the challenge of modeling human driver behavior during simple lane-changing tasks, a critical component for developing advanced driver assistance systems and autonomous vehicles. The research is motivated by the need for accurate behavioral models that can approximate real-world driving data. To achieve this, the authors utilize fuzzy logic-based techniques, which are well-suited for handling the non-linear and uncertain nature of human decision-making in driving scenarios. The study employs an experimental design involving a self-developed car driving simulator. Data representing human driver behavior were acquired under defined conditions during simple lane-changing maneuvers. The core of the research involves the development and evaluation of three distinct fuzzy-logic-based models. These include a fuzzy Hammerstein model, a fuzzy PD controller with a fixed structure (drawn as an analogy to linear PD controllers), and a fuzzy PD controller with an optimized structure. The experimental setup allows for the collection of precise input-output data pairs, capturing the driver's responses to specific traffic situations within the simulated environment. The effectiveness of these three modeling approaches was evaluated by comparing the model outputs against the measured data from the simulator. A specific criterion representing the fitness between the model predictions and the actual measured driver behavior was used to assess performance. The results indicate that the fuzzy logic-based models successfully approximate the acquired data, demonstrating their capability to capture the nuances of human lane-changing behavior. The comparison highlights the relative strengths of the fixed versus optimized structures in the fuzzy PD controllers, as well as the performance of the Hammerstein model, providing empirical evidence on which architectural choices yield better fidelity to human driving patterns. The significance of this work lies in its contribution to the field of intelligent transportation systems. By validating fuzzy logic models against real human driving data, the paper provides a foundation for more realistic and robust driver behavior models. These models are essential for improving the safety and efficiency of automated driving systems, as they allow for better prediction of human actions in mixed traffic environments. The findings suggest that fuzzy logic techniques, particularly with optimized structures, offer a viable and effective method for simulating human driver behavior, thereby supporting the development of more adaptive and responsive autonomous vehicle technologies.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-17 |
| archive | success | openalex | — | — | 5 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-25 |
| clean | success | clean | — | — | 1 | 2026-06-18 |
| chunk | success | chunk | — | — | 1 | 2026-06-18 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-18 |
| promote | success | — | — | — | 1 | 2026-06-17 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-25 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-18 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-25; verification: verified.
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