Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification

By Chiyomi Miyajima, Y. Nishiwaki, K. Ozawa, T. Wakita, K. Itou, Kazuya Takeda, F. Itakura. 2007

332 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Ekim Yurtsever
2. Jacob Lambert
3. Alexander Carballo
4. Kazuya Takeda
5. Chiyomi Miyajima

In Building and Construction

1. Ekim Yurtsever
2. Jacob Lambert
3. Kazuya Takeda
4. Alexander Carballo
5. Stephen P. Boyd

In Control and Systems Engineering

1. Jure Leskovec
2. Kazuya Takeda
3. Chiyomi Miyajima
4. Stephen P. Boyd
5. Alexander Carballo
6. Ekim Yurtsever
7. David Hallac
8. Jacob Lambert

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “GMM based driver model was used to identify individual drivers with success in [241].”
• Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (2017, background): “Many applications, ranging from automobiles [32] to financial markets [35] and wearable sensors [34], generate large amounts of time series data.”
• International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road (2011, background): “There are a vast number of potential applications, including driver identification [23], driver distraction detection [24]–[26], emotion and stress detection/estimation [27], [28], mining potentially hazardous situations [29], and route recognition [27].”