Search Results

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.

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.