Driver behavior analysis and route recognition by Hidden Markov Models
Paper in proceedings, 2008

In this investigation, driver behavior signals are modeled using Hidden Markov Models (HMM) in two different and complementary approaches. The first approach considers isolated maneuver recognition with model concatenation to construct a generic route (bottom-to-top), whereas the second approach models the entire route as a dasiaphrasepsila and refines the HMM to discover maneuvers and parses the route using finer discovered maneuvers (top-to-bottom). By applying these two approaches, a hierarchical framework to model driver behavior signals is proposed. It is believed that using the proposed approach, driver identification and distraction detection problems can be addressed in a more systematic and mathematically sound manner. We believe that this framework and the initial results will encourage more investigations into driver behavior signal analysis and related safety systems employing a partitioned sub-module strategy.

Safety

Wheels

Training

Speech recognition

Driver circuits

Hidden Markov models

Vehicles

Author

Amardeep Sathyanarayana

University of Texas at Dallas

Pinar Boyraz Baykas

Olycksanalys och prevention

John H.L. Hansen

University of Texas at Dallas

2008 IEEE International Conference on Vehicular Electronics and Safety

2008 IEEE International Conference on Vehicular Electronics and Safety
Columbus, OH, USA,

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Vehicle Engineering

Probability Theory and Statistics

DOI

10.1109/ICVES.2008.4640874

More information

Latest update

7/18/2019