We present a system that learns basic vision based driving skills from a human teacher. In contrast to much other work in this area which is based on simulation, or data obtained from simulation, our system is implemented as a multi-threaded, parallel CPU/GPU architecture in a real car and trained with real driving data to generate steering and acceleration control for road following. In addition it uses a novel algorithm 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 the 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 has high potential for future intelligent driver assistance systems since it can serve to increase the driver’s security as well as the comfort, an important sales argument in the car industry.
Ieee Transactions on Intelligent Transportation Systems, 2011, Vol 12, Issue 4, p. 1135-1146