The Driving School System: Learning Basic Driving Skills from a Teacher in a Real Car
Markelić, Irine × Kjær-Nielsen, Anders Pauwels, Karl Baunegaard With Jensen, Lars Chumerin, Nikolay Vidugiriene, Ausra Tamosiunaite, Minia Rotter, Alexander Van Hulle, Marc Krüger, Norbert Wörgötter, Florentin #
Institute of Electrical and Electronics Engineers
IEEE Transactions on Intelligent Transportation Systems vol:12 issue:99 pages:1135-1146
To offer increased security and comfort, advanced driver-assistance systems (ADASs) should consider individual driving styles. Here, we present a system that learns a human's basic driving behavior and demonstrate its use as ADAS by issuing alerts when detecting inconsistent driving behavior. In contrast to much other work in this area, which is based on or obtained from simulation, our system is implemented as a multithreaded parallel central processing unit (CPU)/graphics processing unit (GPU) architecture in a real car and trained with real driving data to generate steering and acceleration control for road following. It also implements a method for detecting independently moving objects (IMOs) for spotting obstacles. Both learning and IMO detection algorithms are data driven and thus improve above the limitations of model-based approaches. The system's ability to imitate the teacher's behavior is analyzed on known and unknown streets, and results suggest its use for steering assistance but limit the use of the acceleration signal to curve negotiation. We propose that this ability to adapt to the driver can lead to better acceptance of ADAS, which is an important sales argument.
Manuscript received March 12, 2010; revised November 5, 2010 and March 15, 2011; accepted April 6, 2011. The Associate Editor for this paper was S. Tang.
Color versions of one or more of the ﬁgures in this paper are available online at http://ieeexplore.ieee.org.
Digital Object Identiﬁer 10.1109/TITS.2011.215769