This paper describes a driver distraction detection scenario which is important to enhance driving safety. We employ data obtained by a GPS to reproduce the driver behavior. Gaussian Mixture model (GMM) is used to capture the sequence of driving characteristics according to the reconstructed vehicle's information and it is also used as a classifier to assign the driving behavior to normal or distraction category. In our work, we consider using a low cost 1Hz GPS receiver as the vehicle data acquisition equipment instead of the costly sensors (steering angle sensor, throttle/brake position sensor, etc). The nonlinear extended 2-wheel vehicle dynamic model is adopted in this study. Firstly, two states, i.e. the sideslip angle and the yaw rate are calculated since they are not available from GPS measurements. Secondly, a piecewise optimization scheme is proposed to reconstruct the driving behaviors which include the steering angle and the longitude force. Finally, a GMM classifier is applied to identify whether the driver is under distraction.
[1]
Marita Irmscher,et al.
Driver Classification Using ve-DYNA Advanced Driver
,
2004
.
[2]
William F. Milliken,et al.
Race Car Vehicle Dynamics
,
1994
.
[3]
Hans B. Pacejka,et al.
A New Tire Model with an Application in Vehicle Dynamics Studies
,
1989
.
[4]
Raja Sengupta,et al.
Vehicle-to-vehicle safety messaging in DSRC
,
2004,
VANET '04.
[5]
Eckhard Freund,et al.
Nonlinear path control in automated vehicle guidance
,
1997,
IEEE Trans. Robotics Autom..
[6]
A. F. Smith,et al.
Statistical analysis of finite mixture distributions
,
1986
.
[7]
Manfred Plöchl,et al.
Driver models in automobile dynamics application
,
2007
.
[8]
Geoffrey J. McLachlan,et al.
Finite Mixture Models
,
2019,
Annual Review of Statistics and Its Application.
[9]
Arthur A. Carter.
The Status of Vehicle-to-Vehicle Communications as a Means of Improving Crash Prevention Performance
,
2005
.