Crash avoidance potential of four passenger vehicle technologies

The objective was to update estimates of maximum potential crash reductions in the United States associated with each of four crash avoidance technologies: side view assist, forward collision warning/mitigation, lane departure warning/prevention, and adaptive headlights. Compared with previous estimates (Farmer, 2008), estimates in this study attempted to account for known limitations of current systems.

Crash records were extracted from the 2004-08 files of the National Automotive Sampling System General Estimates System (NASS GES) and the Fatality Analysis Reporting System (FARS). Crash descriptors such as vehicle damage location, road characteristics, time of day, and precrash maneuvers were reviewed to determine whether the information or action provided by each technology potentially could have prevented or mitigated the crash.

Of the four crash avoidance technologies, forward collision warning/mitigation had the greatest potential for preventing crashes of any severity; the technology is potentially applicable to 1.2 million crashes in the United States each year, including 66,000 serious and moderate injury crashes and 879 fatal crashes. Lane departure warning/prevention systems appeared relevant to 179,000 crashes per year. Side view assist and adaptive headlights could prevent 395,000 and 142,000 crashes per year, respectively. Lane departure warning/prevention was relevant to the most fatal crashes, up to 7500 fatal crashes per year. A combination of all four current technologies potentially could prevent or mitigate (without double counting) up to 1,866,000 crashes each year, including 149,000 serious and moderate injury crashes and 10,238 fatal crashes. If forward collision warning were extended to detect objects, pedestrians, and bicyclists, it would be relevant to an additional 3868 unique fatal crashes.

There is great potential effectiveness for vehicle-based crash avoidance systems. However, it is yet to be determined how drivers will interact with the systems. The actual effectiveness of these systems will not be known until sufficient real-world experience has been gained.