Scipediacontent: Scipediacontent moved page Draft Content 592009211 to Wang et al 2017g

2021-02-03T18:21:23Z

Scipediacontent moved page Draft Content 592009211 to Wang et al 2017g

Scipediacontent: Created page with " == Abstract == Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-bas..."

2021-02-03T18:21:18Z

Created page with " == Abstract == Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-bas..."

New page

== Abstract ==

Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-based driver models with fixed structures usually lack the flexibility to describe the uncertainties and high non-linearity of driver behaviors. In this paper, two kinds of learning-based car-following personalized driver models were developed using naturalistic driving data collected from the University of Michigan Safety Pilot Model Deployment program. One model is developed by combining the Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM), and the other one is developed by combining the Gaussian Mixture Model (GMM) and Probability Density Functions (PDF). Fitting results between the two approaches were analyzed with different model inputs and numbers of GMM components. Statistical analyses show that both models provide good performance of fitting while the GMM--PDF approach shows a higher potential to increase the model accuracy given a higher dimension of training data.

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1703.03534 http://arxiv.org/abs/1703.03534]

* [http://arxiv.org/pdf/1703.03534 http://arxiv.org/pdf/1703.03534]

* [http://xplorestaging.ieee.org/ielx7/7951530/7962914/07963105.pdf?arnumber=7963105 http://xplorestaging.ieee.org/ielx7/7951530/7962914/07963105.pdf?arnumber=7963105],
: [http://dx.doi.org/10.23919/acc.2017.7963105 http://dx.doi.org/10.23919/acc.2017.7963105]

* [https://dblp.uni-trier.de/db/journals/corr/corr1703.html#WangZXLH17 https://dblp.uni-trier.de/db/journals/corr/corr1703.html#WangZXLH17],
: [https://arxiv.org/abs/1703.03534 https://arxiv.org/abs/1703.03534],
: [https://ieeexplore.ieee.org/document/7963105 https://ieeexplore.ieee.org/document/7963105],
: [http://ieeexplore.ieee.org/document/7963105 http://ieeexplore.ieee.org/document/7963105],
: [https://academic.microsoft.com/#/detail/2605241557 https://academic.microsoft.com/#/detail/2605241557]

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Wang et al 2017g - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 592009211 to Wang et al 2017g

Scipediacontent: Created page with " == Abstract == Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-bas..."