Research & Data

Crash Warning Systems

NHTSA’s work in crash warning systems continues, and the agency recently launched a large, 2,800 vehicle field study involving production crash warning systems from leading vehicle manufacturers. These vehicles are equipped with both FCW and LDW systems and with varying driver warning methodologies (or modalities). NHTSA will monitor the operation and driver use of these systems for a 1-year period to help better understand safety potential, driver acceptance, possible adaptation and reliance behavior, and overall technology reliability issues. NHTSA has also recently launched a smaller companion field study of production-level crash warning systems using very highly instrumented vehicles (including video recording of driver and their surroundings) to augment the larger field study.

NHTSA is also evaluating Blind Spot Monitoring (BSM) systems being implemented in some production vehicles. We are testing such systems under controlled test track conditions to determine performance under a variety of kinematic conditions.

NHTSA recognizes that the safety benefits of such warning systems is directly related to the effectiveness of the crash warning interface to draw the attention of the driver to the crash- imminent situation, and to illicit the appropriate response. NHTSA is there­fore engaged in considerable research related to evaluating effectiveness of alterative collision warning inter­face designs and implementations, as well as procedures for gauging such effectiveness. This work is collectively referred to as the Collision Warning Interface Metrics (CWIM) program, and is discussed in more detail in the Human Factor Research section.

 

DOT HS 812 247 Large-Scale Field Test of Forward Collision Alert and Lane Departure Warning Systems February 2016

This report covers a field study of vehicle crash warning technologies using an innovative large-scale data collection technique for gathering information about the crash avoidance systems and how drivers respond to them. Although the specific system studied was the General Motors camera-based forward collision alert and lane departure warning system, this technique could be applied to other emerging active safety crash avoidance systems.

The study team found that this data collection technique has several strengths including cost, sample size, and naturalistic testing by having drivers using their own vehicles where they can adjust system settings or even turn systems off. The technique allowed researchers to study possible long-term changes in how drivers adapt to such systems, and to acquire “rapid-turnaround” large-scale results in an efficient manner.