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Advanced Crash Avoidance Technologies (ACATs) such as Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) have been developed for light passenger vehicles (LPVs) to avoid and mitigate collisions with other road users and objects. However, the number of motorcycle (MC) crashes, injuries, and fatalities in the United States has remained relatively constant. To fully realize potential safety benefits, advanced driver assistance systems and future automated vehicle technologies also need to be effective in avoiding collisions with motorcycles. Toward this goal the Honda-DRI ACAT Safety Impact Methodology (SIM), which was previously developed to evaluate LPV ACAT system effectiveness in avoiding and mitigating collisions with fixed objects, other LPVs, and pedestrians, is being extended to also evaluate the effectiveness of ACATs in avoiding and mitigating LPV-MC collisions.

An understanding of the independent effects of vehicle weight and size on overall vehicle safety is necessary in order to assess the risks and benefits of vehicle weight reduction. This paper describes the results of statistical analyses of 1985 to 1998 model year passenger cars and 1985 to 1997 model year light trucks and vans (LTVs) involved in traffic accidents in the US from 1995 to 1999 to quantify these effects. The analyses involved aggregate linear regression and logistic regression of US FARS fatal accident data, state accident data, and vehicle registration data, using methods based on or adapted from methods described in published NHTSA Technical Reports.

An updated evaluation of the effects on predicted injuries of an example crush protective device (CPD) proposed for application to All-Terrain Vehicles (ATVs) is described. As in previous evaluations, this involved extending and applying the test and analysis methods defined in ISO 13232 (2005) for motorcycle impacts, to evaluate the effects of the example CPD in a sample of simulated ATV overturn events. Updated modeling refinements included lowering the energy levels of the simulated overturn events; accounting for potential mechanical/ traumatic (compressive) asphyxia mechanisms; refining and calibrating the force-deflection characteristics of helmet, head, legs and soil so as to reduce potential over-prediction of head and leg injuries; and calibrating the simulation against aggregated injury distributions from actual accidents.