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The injury outcome of a front-front two-vehicle crash will be a function of crash-specific, vehicle-specific, and occupant-specific parameters. This paper focuses on a preliminary methodology that was used to evaluate the potential for benefits in making vehicle-specific changes to improve the compatibility of light vehicles across the fleet. In particular, the effect on injury rates of matching vehicle frontal stiffness was estimated. The front-front crash data for belted drivers in the lighter vehicles in the crash from ten years of NASS-CDS data were examined. The frontal stiffness of each vehicle was calculated using data taken during full frontal rigid barrier tests for the U.S. New Car Assessment Program (NCAP), and only crashes coded in the CDS as “no override” were considered.

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 characterizes the capability of a rear-end crash avoidance system based on data collected from a field operational test. The system performs forward crash warning and adaptive cruise control functions. The test consists of 66 subjects who drove 10 equipped vehicles on public roads over 157,000 km. System characterization addresses the ability of the forward-looking sensor suite to maintain in-path target tracking and discern between in-path and out-of-path targets; the efficacy of the alert logic in warning the driver to driving conflicts that may lead to rear-end crashes; and the visibility, audibility, and readability of information displayed by the driver-vehicle interface.

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 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.

A systems modeling approach is presented for assessment of harm in the automotive accident environment. The methodology is presented in general form and then applied to evaluate vehicle aggressivity in frontal crashes. The methodology consists of parametric simulation of several controlled accident variables, with case results weighted by the relative frequency of each specific event. A hierarchy of models is proposed, consisting of a statistical model to define the accident environment and assign weighting factors for each crash situation case, and vehicle and occupant models for kinematic simulation of crash events. Head and chest injury results obtained from simulation are converted to harm vectors, in terms of probabilistic Abbreviated Injury Scale (AIS) distributions based on previously defined risk analyses. These harm vectors are weighted by each case’s probability as defined by the statistical model, and summed to obtain a total estimate of harm for the accident environment.

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.