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 early warnings helped distracted drivers react more quickly---and thereby avoid more collisions---than did late warnings or no warnings. Compared with the no-warning condition, an early RECAS warning reduced the number of collisions by 80.7%. Assuming collision severity is proportional to kinetic energy, the early warning reduced collision severity by 96.5%. In contrast, the late warning reduced collisions by 50.0 % and the corresponding severity by 87.5%. The second experiment showed that RECAS benefits even undistracted drivers. Analysis of the braking process showed that warnings provide a potential safety benefit by reducing the time required for drivers to release the accelerator. Warnings do not, however, speed application of the brake, increase maximum deceleration, or affect mean deceleration. These results provide the basis for a computational model of driver performance that was used to extrapolate the findings and identify the most promising parameter settings. Potential applications of these results include methods for evaluating collision warning systems, algorithm design guidance, and driver performance model input.

Current evidence suggests that car crashes in cognitively impaired older drivers often occur because of failure to notice other drivers at intersections. We tested whether licensed drivers with mild to moderate cognitive impairment due to Alzheimer disease (AD) are at greater risk for intersection crashes. In this experiment, 30 participants drove on a virtual highway in a simulator scenario where the approach to within 3.6 seconds of an intersection triggered an illegal incursion by another vehicle. To avoid collision with the incurring vehicle, the driver had to perceive, attend to, and interpret the roadway situation; formulate an evasive plan; and then exert appropriate action on the accelerator, brake, or steering controls, all under pressure of time. The results showed that 6 of 18 drivers with AD (33%) experienced crashes versus none of 12 nondemented drivers of similar age. Use of a visual tool that plots control over steering wheel position, brake and accelerator pedals, vehicle speed, and vehicle position during the 5 seconds preceding a crash event showed inattention and control responses that were either inappropriate or too slow. The findings were combined with those in another recent study of collision avoidance in drivers with AD that focused on potential rear end collisions. Predictors of crashes in the combined studies included visuospatial impairment, disordered attention, reduced processing of visual motion cues, and overall cognitive decline. The results help to specify the linkage between decline in certain cognitive domains and increased crash risk in AD and also support the use of high-fidelity simulation and neuropsychologic assessment in an effort to standardize the assessment of fitness to drive in persons with medical impairments.

High-fidelity driving simulation provides a unique new source of performance parameters to standardize the assessment of driver fitness. Detailed observations of crashes and other safety errors provide unbiased evidence to aid in the difficult clinical decision of whether older or medically impaired individuals should continue to drive. The findings are complementary to evidence currently being gathered using techniques from epidemiology and cognitive neuroscience.

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Despite an abundant use of the term "Out of the loop" (OOTL) in the context of automated driving and human factors research, there is currently a lack of consensus on its precise definition, how it can be measured, and the practical implications of being in or out of the loop during automated driving. The main objective of this paper is to consider the above issues, with the goal of achieving a shared understanding of the OOTL concept between academics and practitioners. To this end, the paper reviews existing definitions of OOTL and outlines a set of concepts, which, based on the human factors and driver behaviour literature, could serve as the basis for a commonly-agreed definition. Following a series of working group meetings between representatives from academia, research institutions and industrial partners across Europe, North America, and Japan, we suggest a precise definition of being in, out, and on the loop in the driving context. These definitions are linked directly to whether or not the driver is in physical control of the vehicle, and also the degree of situation monitoring required and afforded by the driver. A consideration of how this definition can be operationalized and measured in empirical studies is then provided, and the paper concludes with a short overview of the implications of this definition for the development of automated driving functions.