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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.

Advanced Crash Avoidance Technologies (ACATs) such as Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) have been developed for light passenger vehicles (LPVs) to avoid and mitigate collisions with other road users and objects. However, the number of motorcycle (MC) crashes, injuries, and fatalities in the United States has remained relatively constant. To fully realize potential safety benefits, advanced driver assistance systems and future automated vehicle technologies also need to be effective in avoiding collisions with motorcycles. Toward this goal the Honda-DRI ACAT Safety Impact Methodology (SIM), which was previously developed to evaluate LPV ACAT system effectiveness in avoiding and mitigating collisions with fixed objects, other LPVs, and pedestrians, is being extended to also evaluate the effectiveness of ACATs in avoiding and mitigating LPV-MC collisions.

In response to a growing need for a practical and technically valid method for measuring exhaust sound pressure levels (SPL) of on-highway motorcycles, the SAE Motorcycle Technical Steering Committee has developed Surface Vehicle Recommended Practice J28251, “Measurement of Exhaust Sound Pressure Levels of Stationary On-Highway Motorcycles,” which includes a new stationary sound test procedure and recommendations for limit values. Key goals of the development process included: minimal equipment requirements, ease of implementation by non-technical personnel, and consistency with the federal EPA requirements; in particular, vehicles compliant with the EPA requirements should not fail when assessed using J2825. Development of the recommended practice involved a comprehensive field study of 25 motorcycles and 76 different exhaust systems, ranging from relatively quiet OEM systems to unbaffled, aftermarket exhaust systems.