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Crash scenarios are prioritized for crash-imminent braking and advanced restraint applications aimed at reducing injuries when the crash becomes unavoidable using forward-looking pre-crash sensors. Characteristics of priority crash scenarios will form a basis for the development of performance specifications and test procedures for these two applications. Target crashes involve at least one light vehicle (passenger car, van or minivan, light pickup truck, or sport utility vehicle) of model year 1998 or higher with frontal damage from the first impact. Crash prioritization is based on the number of fatalities and functional years lost. Data sources include the Fatality Analysis Reporting System, General Estimates System, and Crashworthiness Data System. Crash scenarios are broken down by vehicle-object and vehicle-vehicle crashes. Pedestrian, pole, or tree impacts prevail in vehicle-object crashes.

This paper presents results from an exploratory analysis of pre-crash sensing countermeasures. This analysis consists of a technology review, development of a methodology to estimate safety benefits based on the total harm concept, identification of crashworthiness scenarios and their harm units, and estimation of safety benefits for brake assist and driver seat position adjustment. Using 1996-2003 Crashworthiness Data System databases, crashworthiness scenarios and harm units of passenger cars are identified from a crash analysis of all single event frontal impacts by combining codes from six variables: frontal impact offset, air bag deployment, seat belt use, driver weight, seat track position, and Delta V. Preliminary results show that brake assist and driver seat position adjustment have the potential to reduce the total harm of passenger cars involved in rear-end crashes.

This paper presents a driver performance map of braking and steering in response to three driving scenarios that lead to rear-end crashes. This map encompasses low risk, conflict, near-crash, and crash imminent driving states that correspond to advisory warning, crash imminent warning, and crash mitigation functionalities for intelligent vehicle rear-end crash countermeasures. Specifically, this paper models driver response to a lead vehicle decelerating by building upon prior research that estimated the state boundaries for driver response to lead vehicle stopped or moving at slower constant speed. In addition, this paper compares braking performance to steering performance in the lead vehicle-decelerating scenario using plots of range and range-rate that roughly quantify the boundaries between the driving conflict states. Driver performance is also discussed among the three rear-end crash scenarios.

This paper presents a framework to characterize the capability of an automotive rear-end crash avoidance system that integrates forward crash warning and adaptive cruise control functionalities. This system characterization describes the operational performance of the system and its main components in the driving environment, based on data to be collected from instrumented vehicles driven by volunteer subjects as their own vehicles under real-world conditions. This characterization is pursuing a number of objectives dealing with the capability of system components including the forward-looking sensor suite, alert logic, automatic vehicle controls, and driver-vehicle interface. A number of subobjectives and concomitant measures are delineated. Examples are provided to illustrate the analysis process of this framework based on data recently collected from system verification tests.

This paper presents recent results of on-going research to build new maps of driver performance in car-following situations. The novel performance map is comprised of four driving states: low risk, conflict, near crash, and crash imminent - which correspond to advisory warning, crash imminent warning, and crash mitigation countermeasures. The paper addresses two questions dealing with the approach to quantify the boundaries between the driving states: (1) Do the quantified boundaries strongly depend on the dynamic scenario encountered in the driving environment? and (2) Do the quantified boundaries vary between steering and braking driver responses? Specifically, braking and steering driver performances are examined in two car-following scenarios: lead vehicle stopped and lead vehicle moving at lower constant speed.

This paper describes an algorithm that identifies the state of traffic ahead of a moving vehicle using onboard sensors. This algorithm approximates the level of service as defined in the Highway Capacity Manual, which portrays a range of traffic conditions on a particular type of roadway facility. The traffic state forms an independent variable in an evaluation plan to assess the benefits and capability of an automotive rear-end crash avoidance system in a field operational test. The algorithm utilizes inputs from vehicle sensors, onboard radar, global positioning system, and digital map to classify the traffic ahead into light, medium, and heavy states. Basically, the algorithm segregates the roadway into four different categories based on the road type (freeway or non-freeway), posted speed limit, and traffic flow conditions.

This paper analyzes off-roadway crash countermeasure systems in support of the United States (U.S.) Department of Transportation's Intelligent Vehicle Initiative. Off-roadway crashes transpire when a moving vehicle departs the travel roadway and then experiences its first harmful event. This paper defines off-roadway crashes and describes their pre-crash scenarios and crash contributing factors. This information is then utilized to develop countermeasure concepts and concomitant functional requirements to warn drivers of imminent road edge crossing or vehicle control loss on straight or curved roadways. A technology survey follows to assess the status of state-of-the-art technologies within the categories of vehicle-based, infrastructure-based, or cooperative vehicle-infrastructure systems. This paper concludes with forecasts of the progression of future countermeasure systems towards the realm of cooperative technologies.

This paper presents the results from an analysis of off-roadway crashes involving commercial vehicles (large trucks – medium and heavy trucks) in support of the roadway departure research area, part of the United States (US) Department of Transportation’s Intelligent Vehicle Initiative. This analysis deals with off-roadway crashes in which the first harmful event occurs off the roadway after a vehicle in transport departs the travel road due to loss of control or crossing the edge of the roadway. Approximately 136,000 such crashes were reported in the US over a 3-year period based on the 1996–1998 National Automotive Sampling System/General Estimates System crash databases.

This paper summarizes the results of an analysis of promising countermeasure systems for crossing path crashes, and thus provides a foundation for setting research priorities under the United States (U.S.) Department of Transportation’s Intelligent Vehicle Initiative. Crossing path crashes involve one moving vehicle cutting across the path of another, which amounted to 1.72 million police-reported crashes in the U.S. based on the 1998 General Estimates System crash database. Three basic countermeasure concepts and their functional requirements were developed to warn drivers of imminent collision caused by stop sign violation, red light violation, or insufficient gaps between vehicles at intersections or driveways. A survey was conducted to assess the technical viability of current systems and enabling technologies that could implement these concepts using infrastructure-based, vehicle-based, or cooperative vehicle-infrastructure systems.

A novel methodology is presented to estimate the safety benefits of intelligent vehicle safety systems in terms of reductions in the number of collisions and the number and severity of crash-related injuries. In addition, mathematical models and statistics are provided to support the estimation of the crash injury reduction factor in rear-end, lane change, and single vehicle roadway departure collisions. Simple models based on Newtonian mechanics are proposed to derive Δv, the change in speed that a vehicle undergoes as a consequence of crashing. Statistics on the distribution of vehicle types and weights in the United States are supplied, which are needed for Δv estimation. Moreover, mathematical equations are derived to estimate the average harm per collision. Finally, statistics on the average harm per occupant are obtained from the 1994 and 1995 Crashworthiness Data System crash databases.

The potential safety benefits of an Intelligent Cruise Control (ICC) system are assessed in terms of the number of rear-end crashes that might be avoided on U.S. freeways if all vehicles were equipped with such a system. This analysis utilizes naturalistic driving data collected from a field operational test that involved 108 volunteers who drove ten passenger cars for about 68 and 35 thousand miles in manual and ICC control modes, respectively. The effectiveness of the ICC system is estimated at about 17 percent based on computer simulations of two rear-end precrash scenarios that are distinguished by whether the following vehicle encounters a suddenly-decelerating or slow-moving lead vehicle. The ICC system has the potential to eliminate approximately 13 thousand policereported rear-end crashes on U.S. freeways, using 1996 national crash statistics.

Dynamically-distinct precrash scenarios in rear-end collisions were identified in a recent study conducted by the Volpe National Transportation Systems Center, of the United States Department of Transportation, Research and Special Programs Administration, in conjunction with the National Highway Traffic Safety Administration (NHTSA) using NHTSA's General Estimates System (GES) crash database from 1992 through 1996. Precrash scenarios represent vehicle dynamics immediately prior to a collision. This paper provides a statistical description of the five most frequently-occurring rear-end precrash scenarios in terms of vehicle and driver characteristics, using the 1996 GES database.