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Automation is often problematic because people fail to rely upon it appropriately. Because people respond to technology socially, trust influences reliance on automation. In particular, trust guides reliance when complexity and unanticipated situations make a complete understanding of the automation impractical. This review considers trust from the(More)
Rear-end collisions account for almost 30% of automotive crashes. Rear-end collision avoidance systems (RECASs) may offer a promising approach to help drivers avoid these crashes. Two experiments performed using a high-fidelity motion-based driving simulator examined driver responses to evaluate the efficacy of a RECAS. The first experiment showed that(More)
A driving simulator study was conducted to assess whether real-time feedback on a driver's state can influence the driver's interaction with in-vehicle information systems (IVIS). Previous studies have shown that IVIS tasks can undermine driver safety by increasing driver distraction. Thus, mitigating driver distraction using a feedback mechanism appears(More)
Collision warning systems offer a promising approach to mitigate rear-end collisions, but substantial uncertainty exists regarding the joint performance of the driver and the collision warning algorithms. A simple deterministic model of driver performance was used to examine kinematics-based and perceptual-based rear-end collision avoidance algorithms over(More)
Car crashes rank among the leading causes of death in the United States. Acknowledgements We acknowledge the assistance of Founded in 1947, the AAA Foundation in Washington, D.C. is a not-for-profit, publicly supported charitable research and education organization dedicated to saving lives by preventing traffic crashes and reducing injuries when crashes(More)
As use of in-vehicle information systems (IVISs) such as cell phones, navigation systems, and satellite radios has increased, driver distraction has become an important and growing safety concern. A promising way to overcome this problem is to detect driver distraction and adapt in-vehicle systems accordingly to mitigate such distractions. To realize this(More)
As computers and other information technology move into cars and trucks, distraction-related crashes are likely to become an important problem. This paper begins to address this problem by examining how alert strategy (graded and single-stage) and alert modality (haptic and auditory) affect how well collision warning systems mitigate distraction and direct(More)
OBJECTIVE In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. BACKGROUND Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash(More)