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As Advanced Crash Avoidance Technologies (ACATs) such as Forward Collision Warning (FCW), Crash Imminent Braking Systems and other advanced technologies continue to be developed, the need for full-scale test methodologies which can minimize hazards to test personnel and damage to equipment has rapidly increased. The challenge of evaluating such ACAT systems is twofold. First, the evaluation system must be able to deliver a potential Collision Partner (CP) reliably and precisely along a trajectory which would ultimately result in a crash in a variety of configurations, such as rear-ends, head-ons, crossing paths, and sideswipes. Second, and more importantly, the collision partner must not pose a substantial physical risk to the test driver, other test personnel, equipment, or to test vehicles in the event that the collision is not avoided.

The Advanced Crash Avoidance Technologies (ACAT) program initiated by the National Highway Traffic Safety Administration had two major overall objectives. These were to develop a standardized Safety Impact Methodology (SIM) tool to evaluate the effectiveness of advanced technologies in avoiding and mitigating specific types of vehicle crashes; and to develop and demonstrate objective tests that are used in the SIM to verify the safety impact of a real system. Honda and Dynamic Research Inc. (DRI) had been developing and applying such SIMs for several years and had a Cooperative Agreement with NHTSA to further develop a SIM in order to determine the feasibility of developing estimates of effectiveness for specific not-yet-deployed safety technologies in the absence of data from real world or field operational tests, and linking it to the results from objective tests.

This paper reports on the interim progress of the Honda-DRI ACAT-II program initiated by the National Highway Traffic Safety Administration (NHTSA). The objectives of the ACAT-II program were further development of a formalized Safety Impact Methodology (SIM) for estimating the capability of advanced technology applications installed in vehicles to address specific types of motor vehicle crashes, and to evaluate driver acceptance of the technologies. This particular ACAT study extended earlier work by Honda and DRI in the NHTSA ACAT-I program by extending the SIM so as to be able to analyze head-on crashes more completely, and by using the extended SIM to evaluate of a pre-production version of a Honda Head-on Crash Avoidance Assist System (HCAAS).

Surrogate targets are used throughout the automotive industry to safely and repeatably test Advanced Driver Assistance Systems (ADAS) and will likely find similar applications in tests of Automated Driving Systems. For those test results to be applicable to real-world scenarios, the surrogate targets must be representative of the real-world objects that they emulate. Early target development efforts were generally divided into those that relied on sophisticated radar measurement facilities and those that relied on ad-hoc measurements using automotive grade equipment. This situation made communication and interpretation of results between research groups, target developers and target users difficult. SAE J3122, “Test Target Correlation - Radar Characteristics”, was developed by the SAE Active Safety Systems Standards Committee to address this and other challenges associated with target development and use. J3122 addresses four topics.

This paper describes the development and application of a newly developed metric for evaluating and quantifying the capability of a vehicle/controller (e.g., Automated Vehicle or human driver) to avoid collisions in nearly any potential scenario, including those involving multiple potential collision partners and roadside objects. At its core, this Collision Avoidance Capability (CAC) metric assesses the vehicle’s ability to avoid potential collisions at any point in time. It can also be evaluated at discrete points, or over time intervals. In addition, the CAC methodology potentially provides a real-time indication of courses of action that could be taken to avoid collisions. The CAC calculation evaluates all possible courses of action within a vehicle’s performance limitations, including combinations of braking, accelerating and steering.