Author:

Keywords:

Advanced individualized driver-assistance system, driving, imitation learning, independently moving object, IMO, real-time system, Science & Technology, Technology, Engineering, Civil, Engineering, Electrical & Electronic, Transportation Science & Technology, Engineering, Transportation, independently moving object (IMO), TRACKING, 0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services, Logistics & Transportation, 3509 Transportation, logistics and supply chains, 4602 Artificial intelligence, 4603 Computer vision and multimedia computation

Abstract:

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.