DocumentCode
574519
Title
Driving course prediction for vehicle handling maneuvers
Author
Ruoqian Liu ; Hai Yu ; McGee, Ryan ; Murphey, Yi L.
Author_Institution
Dept. of Electr. Eng., Univ. of Michigan-Dearborn, Dearborn, MI, USA
fYear
2012
fDate
27-29 June 2012
Firstpage
2096
Lastpage
2101
Abstract
This paper aims at predicting the future driving course, which we define as a combination of two bifurcating channels - future speed and steering action that in turn derive a future driving trajectory during a curve. In defining the relation of these two channels, human factors, such as the stressfulness, comfort level, and skillfulness of the driver, are paid particular attention to. While the modeling and forecast of speed and steering angle are to some extent separated, a hidden Markov model (HMM) that´s designed to mimic driver´s intention integrates them by making subjective corrections. The proposed algorithm has been proved effective on realistic driving data based on a prototype vehicle at Ford.
Keywords
hidden Markov models; steering systems; vehicle dynamics; bifurcating channel; comfort level; driving course prediction; future driving trajectory; hidden Markov model; prototype vehicle; realistic driving data; skillfulness; steering action; steering angle; vehicle handling maneuver; Acceleration; Biological system modeling; Hidden Markov models; Predictive models; Roads; Time series analysis; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference (ACC), 2012
Conference_Location
Montreal, QC
ISSN
0743-1619
Print_ISBN
978-1-4577-1095-7
Electronic_ISBN
0743-1619
Type
conf
DOI
10.1109/ACC.2012.6315104
Filename
6315104
Link To Document