Abstract
In this paper, we present our recently developed stochastic driver-behavior model based on Gaussian mixture model (GMM) framework. The proposed driver-behavior modeling is employed to anticipate car-following behavior in terms of pedal control operations in response to the observable driving signals, such as the own vehicle velocity and the following distance to the leading vehicle. In addition, the proposed driver modeling allows adaptation scheme to enhance the model capability to better represent particular driving characteristics of interest (i.e., individual driving style) from the observed driving data themselves. Validation and comparison of the proposed driver-behavior models on realistic car-following data of several drivers showed the promising results. Furthermore, the adapted driver models showed consistent improvement over the unadapted driver models in both short-term and long-term predictions.
Keywords
Computer scienceAdaptation (eye)Advanced driver assistance systemsCar modelVehicle dynamicsData modelingDriving simulatorScheme (mathematics)Mixture modelTerm (time)SimulationAutomotive engineeringArtificial intelligenceEngineering
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Publication Info
- Year
- 2011
- Type
- article
- Pages
- 814-819
- Citations
- 65
- Access
- Closed
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Cite This
Pongtep Angkititrakul,
Chiyomi Miyajima,
Kazuya Takeda
(2011).
Modeling and adaptation of stochastic driver-behavior model with application to car following.
, 814-819.
https://doi.org/10.1109/ivs.2011.5940464
Identifiers
- DOI
- 10.1109/ivs.2011.5940464