Car drivers can employ a number of strategies to negotiate curves. The tangent point strategy proposes the use of the angle between the tangent point of the inner lane markings and the car's current heading direction, which is proportional to the required steering angle. The gaze-sampling strategy proposes to fixate points on the future path and measure the curvature of optic flow vectors which can inform the drivers whether they over- or under-steer. Nine subjects drove repeatedly on the four loops of a motorway junction for which street parameters were available, while eye-movements, steering parameters and relations of the car to the lane were recorded. In the first half of the trials, we observed which strategy drivers normally use, whereas in the second half, we instructed subjects to use exclusively either the tangent-point or the gaze-sampling strategy and observed their steering behavior. Our results confirm that subjects normally look at the tangent point whereas they do not use gaze sampling of their own accord. Further, subjects drive more smoothly in terms of position on the lane and steering stability in the tangent-point condition.

No abstract

On winding roads, car drivers have to control speed and steering angle in order to keep the car in an optimal lane position. Among the strategies proposed for steering regulation are the use of the tangent point, a geometrical method, and gaze sampling, in which retinal flow lines obtained by tracking a spot on the future road need to be assessed. Previous studies used a variety of scenarios (real-road vs. simulator) and different road designs (closed vs. open bends, different curvatures) and found results speaking in favor of either strategy. Here, we investigate what effects the openness of the bend, i.e. the sight distance of the driver, has on the percentage with which drivers use the tangent point. Six drivers drove a test car repeatedly through a series of twelve bends on real roads while their eye-movements were recorded. Results show that the reliance on the tangent point is generally high and increases with the closedness (shorter sight distances) of the bend and higher curvature. In open bends they alternatively look far into the straight road segments adjacent to the bend, but do not use gaze sampling.

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.Index Terms-Advanced individualized driver-assistance system, driving, imitation learning, independently moving object (IMO), real-time system. A DVANCED driver-assistance systems (ADASs) that adapt to the individual driver have high potential in the car industry since they can reduce the risk of accidents while providing a high degree of comfort. Conventional systems are based on a general moment-to-moment assessment of road and driving parameters. To arrive at a judgment of the current Manuscript