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Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions.

The effectiveness of Forward Collision Warning (FCW) or similar crash warning/mitigation systems is highly dependent on driver acceptance. If a FCW system delivers the warning too early, it may distract or annoy the driver and cause them to deactivate the system. In order to design a system activation threshold that more closely matches driver expectations, system designers must understand when drivers would normally apply the brake. One of the most widely used metrics to establish FCW threshold is Time to Collision (TTC). One limitation of TTC is that it assumes constant vehicle velocity. Enhanced Time to Collision (ETTC) is potentially a more accurate metric of perceived collision risk due to its consideration of vehicle acceleration. This paper compares and contrasts the distribution of ETTC and TTC at brake onset in normal car-following situations, and presents probability models of TTC and ETTC values at braking across a range of vehicle speeds.

As part of active safety systems for reducing bicyclist fatalities and injuries, Bicyclist Pre-Collision System (BPCS), also known as Bicyclist Autonomous Emergency Braking System, is being studied currently by several vehicles manufactures. This paper describes the development of a surrogate bicyclist which includes a surrogate bicycle and a surrogate bicycle rider to support the development and evaluation of BPCS. The surrogate bicycle is designed to represent the visual and radar characteristics of real bicyclists in the United States. The size of bicycle surrogate mimics the 26 inch adult bicycle, which is the most popular adult bicycle sold in the US. The radar cross section (RCS) of the surrogate bicycle is designed based on RCS measurement of the real adult sized bicycles.

Pedestrian Automatic Emergency Braking (PAEB) for helping avoiding/mitigating pedestrian crashes has been equipped on some passenger vehicles. Since approximately 70% pedestrian crashes occur in dark conditions, one of the important components in the PAEB evaluation is the development of standard testing at night. The test facility should include representative low-illuminance environment to enable the examination of the sensing and control functions of different PAEB systems. The goal of this research is to characterize and model light source distributions and variations in the low-illuminance environment and determine possible ways to reconstruct such an environment for PAEB evaluation. This paper describes a general method to collect light sources and illuminance information by processing large amount of potential collision locations at night from naturalistic driving video data.

According to the U.S. National Highway Traffic Safety Administration, 743 pedal cyclists were killed and 48,000 were injured in motor vehicle crashes in 2013. As a novel active safety equipment to mitigate bicyclist crashes, bicyclist Pre-Collision Systems (PCSs) are being developed by many vehicle manufacturers. Therefore, developing equipment for evaluating bicyclist PCS is essential. This paper describes the development of a bicycle carrier for carrying the surrogate bicyclist in bicyclist PCS testing. An analysis on the United States national crash databases and videos from TASI 110 car naturalistic driving database was conducted to determine a set of most common crash scenarios, the motion speed and profile of bicycles. The bicycle carrier was designed to carry or pull the surrogate bicyclist for bicycle PCS evaluation. The carrier is a platform with a 4 wheel differential driving system.

There are over 4,500 fatal intersection crashes each year in the United States. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging active safety systems designed to detect an imminent intersection crash and either provide a warning or perform an automated evasive maneuver. The performance of an I-ADAS will depend on the ability of the onboard sensors to detect an imminent collision early enough for an I-ADAS to respond in a timely manner. One promising method for determining the earliest detection opportunity is through the reconstruction of real-world intersection crashes. After determining the earliest detection opportunity, the required sensor range, orientation, and field of view can then be determined through the simulation of these crashes as if the vehicles had been equipped with an I-ADAS.

To reduce the number and severity of accidents, automakers have invested in active safety systems to detect and track neighboring vehicles to prevent accidents. These systems often employ RADAR and LIDAR, which are not degraded by low lighting conditions. In this research effort, reflections from deer were measured using two sensors often employed in automotive active safety systems. Based on a total estimate of one million deer-vehicle collisions per year in the United States, the estimated cost is calculated to be $8,388,000,000 [1]. The majority of crashes occurs at dawn and dusk in the Fall and Spring [2]. The data includes tens of thousands of RADAR and LIDAR measurements of white-tail deer. The RADAR operates from 76.2 to 76.8 GHz. The LIDAR is a time-of-flight device operating at 905 nm. The measurements capture the deer in many aspects: standing alone, feeding, walking, running, does with fawns, deer grooming each other and gathered in large groups.

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.