Title
Naturalistic lane-keeping based on human driver data
Abstract
Autonomous lane keeping is a well studied problem with several good solutions that can be found in the literature. However, naturalistic lane keeping mechanisms, in the sense of imitating human car steering, are not so common. Based on existing knowledge of human driving, this paper analyses several controllers prone to generate human-like lane keeping behavior. Using systems identification, we compare how well the control models fit with real human steering data gathered in a simulator. Experimental results points towards a parsimonious control mechanism where angles relative to the direction of the road are directly used by the driver to steer the car and keep it on the lane. This result can be used to design human like autonomous lane keeping mechanisms or to improve the design of ADAS systems adapting them to individual drivers.
Year
DOI
Venue
2013
10.1109/IVS.2013.6629492
Intelligent Vehicles Symposium
Keywords
Field
DocType
automobiles,identification,ADAS systems,human car steering,human driver data,human like autonomous lane keeping mechanisms,naturalistic lane keeping mechanisms,parsimonious control mechanism,real human steering data,systems identification
Simulation,Human–computer interaction,Engineering
Conference
ISSN
ISBN
Citations 
1931-0587
978-1-4673-2754-1
2
PageRank 
References 
Authors
0.36
6
3
Name
Order
Citations
PageRank
Iñaki Rañó130.73
Edelbrunner, H.220.36
Gregor Schöner352.17