Modeling the decision-making in human driver overtaking

Stefansson, Elis; Jiang, Frank J.; Nekouei, Ehsan; Nilsson, Håkan; Johansson, Karl Henrik · 2020 · OpenAlex-citations

DOI: 10.1016/j.ifacol.2020.12.2346

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Summary

This paper addresses the challenge of modeling the decision-making processes of human drivers during overtaking maneuvers, a critical scenario in autonomous driving and traffic safety research. The authors aim to mathematically formalize overtaking as a decision problem characterized by perceptual uncertainty. The primary motivation is to develop models that can accurately judge whether an overtaking action is desirable or not, thereby capturing the nuances of human risk-taking behavior. The methodology involves proposing two distinct decision models: a risk-agnostic model and a risk-aware model. These models are designed to evaluate overtaking scenarios by incorporating perceptual uncertainty into the decision framework. The authors numerically analyze these models to demonstrate their ability to assess the desirability of overtaking actions. A key finding is that human risk-taking behavior in this context can be primarily characterized through two specific model parameters: time and confidence level. These parameters collectively represent the driver's decision-making process, allowing the models to simulate how drivers weigh risks and opportunities. To validate the proposed models, the authors detail an experimental testbed designed for evaluating the decision-making process in overtaking scenarios. This testbed facilitates the collection of data from human drivers to assess the accuracy of the models. The paper presents preliminary experimental results derived from two human drivers. These initial findings support the hypothesis that the time and confidence parameters effectively capture the essence of human overtaking decisions. The results indicate that the models can distinguish between desirable and undesirable overtaking actions based on the drivers' actual behavior. The significance of this work lies in its contribution to the understanding of human driver behavior, which is essential for the development of safe and realistic autonomous vehicle systems. By providing a mathematical framework that accounts for perceptual uncertainty and risk awareness, the paper offers a tool for simulating and predicting driver decisions. This approach can enhance the design of autonomous systems that interact with human drivers, ensuring that such systems can anticipate and respond appropriately to human overtaking maneuvers. The identification of time and confidence as key parameters provides a simplified yet effective way to model complex human behaviors, paving the way for further research and refinement in the field of autonomous driving and traffic safety.

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Topics

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

• decision making risk perception
• risk taking
• gap acceptance
• mental model of traffic
• situational awareness
• anticipation

Information type

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• Empirical Findings: behavioral performance data
• Theoretical Contribution: computational model, theory or model