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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.

In order to help avoid or mitigate rear-end collisions a Pre-Collision System (PCS) was developed. The purpose of this study is to clarify the Time-to-Collision (TTC) distribution when approaching a lead vehicle under normal driving condition. To enhance the effectiveness of PCS, warnings and/or automatic brake activation should happen as early as possible, however, if too early there is a high possibility of false warnings or activations, which is not desirable. If the distribution of distance to a lead vehicle under normal driving conditions is quantified, an approach limit can be estimated. In this study, we try to clarify a TTC distribution that is approximated by a log-normal distribution. Then, we investigate the Enhanced Time-to-Collision (ETTC) that is the secondary predictive value of TTC. And, we clarify the log-normal distribution of ETTC is a more stable approximator of normal driving than a log-normal distribution of TTC.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.