Integrated multimodal systems is one promising direction to improve human-vehicle interaction. In order to create intelligent human-vehicle interfaces and reduce visual load during secondary tasks, combining a haptic rotary device and a graphic display will provide one practical solution. However, in literature, the proper display position for the haptic rotary device is not fully investigated. In this paper, one experimental infotainment system is studied (including a haptic rotary control device and a graphic display) to evaluate the proper display position. Measurements used include task completion time, reaction to road events, lane/velocity keeping during secondary tasks, and user preference. Three display positions are considered: high mounted position, cluster position, and center stack position. The results show that, with increased on-road and off-road visual loads, the cluster display position can reduce lane position deviation significantly compared to high mounted and center stack positions. In addition, the high mounted and cluster display positions are better toward two different road events, including strong wind gust and extreme deceleration of the lead car.

Driver distraction detection and intervention are important for designing modern driver-assistance systems and for improving safety. The main research question of this paper is to investigate how the cumulative driver off-road glance duration can be controlled to reduce the probability of occurrences of crash and near-crash events. Based on the available data sets from the Virginia Tech Transportation Institute (VTTI) 100-car study, the conditional probability is calculated to study the chance of crash and near-crash events when the given cumulative off-road glance duration in 6 s has been reached. Different off-road eye-glance locations and traffic density levels are also evaluated. The results show that one linear relationship can be obtained between the cumulative off-road eye-glance duration in 6 s and the risk of occurrences of crash and near-crash events, which varies for different off-road eye-glance locations. In addition, the traffic density level is found to be one significant moderator to this linear relationship. Detailed comparisons are made for different traffic density levels, and one nonlinear equation is obtained to predict the probability of occurrences of crash and near-crash events by considering both cumulative off-road glance duration and traffic density levels.

We present an automotive-grade, real-time, vision-based driver state monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction. This information is used to warn the driver and to modulate the actions of other safety systems. The purpose of this monitor is to increase road safety by preventing drivers from falling asleep or from being overly distracted, and to improve the effectiveness of other safety systems.

This article shows cutting-edge computer vision methods employed in advanced vision sensing technologies for medical, safety and security applications, where the human eye represents the object of interest for both the imager and the computer. As the eye scans the environment, or focuses on particular objects in the scene, the processor simultaneously localizes the eye position, tracks its position and movement over time, and infers counter measures such as fatigue level, attention level, and gaze direction in real-time and automatically. The focus of this demonstration is placed on four different algorithms: auto-initialization (RHED), eye position tracking (SIRAT), eye closure recognition (HRA), driver head pose categorization.