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
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;
Conference_Titel :
American Control Conference (ACC), 2012
Conference_Location :
Montreal, QC
Print_ISBN :
978-1-4577-1095-7
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2012.6315104