Presenter Information

Nicolas Dapzol, INRETS, FranceFollow

Location

Stevenson, Washington

Date

10-7-2007

Session

Session 4 – Posters

Abstract

In this study, we propose to use statistical modelling to analyze, model, and categorize driving activity. To achieve this objective, we develop a new statistical model by adding a weight feature to the classic Semi Hidden Markov Model (SHMM) framework. Then, to assess its capacity, we conduct an experiment that allows us to record 718 driving sequences categorized in 36 situations. We then used our modelling to identify the driver's aim and the driving situation he's in. Furthermore, we adapted the ascendant hierarchic classification technique to this modelling. It allows us to understand which situations are close and to define partitions of whole driving situations. Finally, on these sequences, our modelling choice allows us to predict the driver’s situation with, on average, an 85% success rate. These results show the HMM effectiveness to manage temporal and multidimensional data by modelling predicting drivers’ behavior.

Rights

Copyright © 2007 the author(s)

DC Citation

Proceedings of the Fourth International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, July 9-12, 2007, Stevenson, Washington. Iowa City, IA: Public Policy Center, University of Iowa, 2007: 84-90.

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Jul 10th, 12:00 AM

Weight Semi Hidden Markov Model and Driving Situation Classification for Driver Behavior Diagnostic

Stevenson, Washington

In this study, we propose to use statistical modelling to analyze, model, and categorize driving activity. To achieve this objective, we develop a new statistical model by adding a weight feature to the classic Semi Hidden Markov Model (SHMM) framework. Then, to assess its capacity, we conduct an experiment that allows us to record 718 driving sequences categorized in 36 situations. We then used our modelling to identify the driver's aim and the driving situation he's in. Furthermore, we adapted the ascendant hierarchic classification technique to this modelling. It allows us to understand which situations are close and to define partitions of whole driving situations. Finally, on these sequences, our modelling choice allows us to predict the driver’s situation with, on average, an 85% success rate. These results show the HMM effectiveness to manage temporal and multidimensional data by modelling predicting drivers’ behavior.