Analysis of Potential Co-Benefits for Bicyclist Crash Imminent Braking Systems

In the US, the number of traffic fatalities has had a long term downward trend as a result of advances in the crash worthiness of vehicles. However, these improvements in crash worthiness do little to protect other vulnerable road users such as pedestrians or bicyclists. Several manufacturers have developed a new generation of crash avoidance systems that attempt to recognize and mitigate imminent crashes with non-motorists. While the focus of these systems has been on pedestrians where they can make meaningful contributions to improved safety [1], recent designs of these systems have recognized mitigating bicyclist crashes as a potential co-benefit. This paper evaluates the performance of one system that is currently available for consumer purchase. Because the vehicle manufacturer does not claim effectiveness for their system under all crash geometries, we focus our attention on the crash scenario that has the highest social cost in the US: the cyclist and vehicle on parallel paths being struck from behind. Our analysis of co benefits examines the ability to reduce three measures: number of crashes, fatalities, and a comprehensive measure for social cost that incorporates morbidity and mortality. Test track simulations under realistic circumstances with a realistic surrogate bicyclist target are conducted. Empirical models are developed for system performance and potential benefits for injury and fatality reduction. These models identify three key variables in the analysis: vehicle speed, cyclist speed and cyclist age as key determinants of potential co-benefits. We find that the evaluated system offers only limited benefits for any but the oldest bicycle riders for our tested scenario

Pumping up the [N I] nebular lines

The optical [N I] doublet near 5200 {\AA} is anomalously strong in a variety of emission-line objects. We compute a detailed photoionization model and use it to show that pumping by far-ultraviolet (FUV) stellar radiation previously posited as a general explanation applies to the Orion Nebula (M42) and its companion M43; but, it is unlikely to explain planetary nebulae and supernova remnants. Our models establish that the observed nearly constant equivalent width of [N I] with respect to the dust-scattered stellar continuum depends primarily on three factors: the FUV to visual-band flux ratio of the stellar population; the optical properties of the dust; and the line broadening where the pumping occurs. In contrast, the intensity ratio [N I]/H{\beta} depends primarily on the FUV to extreme-ultraviolet ratio, which varies strongly with the spectral type of the exciting star. This is consistent with the observed difference of a factor of five between M42 and M43, which are excited by an O7 and B0.5 star respectively. We derive a non-thermal broadening of order 5 km/s for the [N I] pumping zone and show that the broadening mechanism must be different from the large-scale turbulent motions that have been suggested to explain the line-widths in this H II region. A mechanism is required that operates at scales of a few astronomical units, which may be driven by thermal instabilities of neutral gas in the range 1000 to 3000 K. In an appendix, we describe how collisional and radiative processes are treated in the detailed model N I atom now included in the Cloudy plasma code.Comment: ApJ in press. 8 pages of main paper plus 11 pages of appendices, with 13 figures and 12 table

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance