Abstract

Many auto manufacturers are now producing hybrid and electric vehicles with an additive noise component to signal vehicle presence in the same way that internal combustion engine vehicles signal their presence through engine noise. The Virginia Tech Transportation Institute conducted an evaluation of quiet car detectability as part of a GM-funded project in 2015–2016. The internal combustion engine benchmark significantly outperformed the other three vehicles under a 10-km/h steady approach, but these differences largely disappeared at 20 km/h due to increased tire and road noise. Trends of improved detectability offered by the additive noise signals were observed but did not demonstrate a significant advantage over an electric vehicle with no additional noise component. Since that original project, NHTSA has released their final version of Federal Motor Vehicle Safety Standard (FMVSS) 141, outlining “Minimum Sound Requirements for Hybrid and Electric Vehicles.” This project aimed to demonstrate differences in detectability by replicating the previous study but with newer FMVSS 141-compliant sounds. The proposed additive sounds examined drastically improved detectability compared to the production variants included in the first round of testing. At 10 km/h, the additive sound conditions outperformed the no-sound condition by magnitudes ranging from 3.4 to 4.6, each eliciting mean detection distances well above the NHTSA minimum detection criteria. At 20 km/h, detectability also improved dramatically over the earlier production variants, achieving a similar magnitude advantage over no-sound as observed at 10 km/h. Increasing background noise resulted in a measurable impact on mean detection distances. The average reduction across all conditions was approximately 33% and 28% for approach speeds of 10 km/h and 20 km/h, respectively. In terms of accurately recognizing a stopped vehicle in a 20 to 0 km/h scenario, all sound conditions significantly outperformed the no-sound condition across both background noise conditions.

Abstract

The Vehicle Identification Number (VIN) standard currently tracks data elements such as manufacturing details, vehicle descriptors, vehicle security codes, and serial numbers. To date, the VIN standard does not include information on safety features or vehicle equipment. Expanding the standard to indicate the presence of automated driving systems and advanced driving assistance systems could allow the efficacy and impact of these technologies on crash avoidance and crash mitigation to be more accurately determined. The research team held a series of meetings to solicit stakeholder feedback on a expanding the VIN and a number of Vehicle Information Reporting System (VIRS) alternatives. The six alternatives involved two methods of implementing a modified VIN, barcodes, optical chips, event data recorders, and a write-in option for a “participant-specified” VIRS alternative. Qualitative data of the feedback meetings was obtained from transcriptions and was further analyzed to identify preferences for VIRS alternative options. Quantitative data was obtained using a Likert-type survey adapted from the System Usability Scale. Unfortunately, due to time and budget constraints, no original equipment manufacturers participated in this project. Thus, this feasibility study may offer an incomplete view without this feedback.

Abstract

Due to the rapid development of the ridesharing industry, there is very limited data available for researchers and practitioners to draw a comprehensive conclusion regarding resultant changes in vehicle miles traveled (VMT). Current research on ridesharing is inconclusive and conflicting. This report summarizes our findings on the impacts of ridesharing on VMT, focusing on optimization and pairing modeling, the relationship between ridesharing and public transit, induced trips, and car ownership.

Abstract

For the potential safety benefits of driving automation systems to be fully realized, drivers must understand the capabilities and limitations of these systems. This study explored a range of materials that drivers may access when trying to learn about driving automation system safety features: owner’s manual only, owner’s manual and OEM website video or animation, and owner’s manual and MyCarDoesWhat.org videos. Three vehicles, a 2015 Infiniti Q50, a 2016 Honda CR-V, and a 2015 Chevy Tahoe, were selected to represent a range of vehicle types, sizes, technologies, and price points. Each training condition was tested with each vehicle for a total of nine possible testing scenarios. Thirty-six participants were recruited for the study, with an equal number of males and females from two age groups, 25–39 and 40–54 years old. Participants were balanced across the nine possible testing scenarios. A two-part study was conducted to assess participants’ ability to learn from existing training materials and to determine how well participants were able to retain what they learned. When taking into consideration participants’ self-reported learning styles, average scores across all technologies were fairly comparable across style, and participants were able gain at least a rudimentary understanding of the operation and purposes of driving automation system technologies. However, participants were less sure of the specifics associated with the technologies (i.e., activation, alerts or warnings, and appropriate use). Those in the multimedia testing conditions reported feeling more familiar with the technologies than those in the owner’s manual only condition. Participants found the videos to be an entertaining and easier-to-understand alternative to the manual. Several indicated that they would refer to the video first to see how the technology worked and then refer to the manual to gain a more in-depth understanding. Videos with sound and additional details were preferred to the simpler animations.

Abstract

Safety research and modeling has traditionally been divided into two separate realms – that of the vehicle and that of the road. Because crashes are caused by a variety of factors – the driver, the vehicle, and the road – and their interactions, it is ideal to look at the larger picture of transportation safety. Modeling is a useful test bed for new technologies and improvements; and greater fidelity in safety modeling will allow designers to see the potential impact of an infrastructure or vehicular change before implementing it. To make the greatest impact, civil and mechanical engineers must work together to capture the entirety of the transportation system, tying together elements of traffic flow, driver behavior, and vehicle dynamics. For this project, made possible by a University of Virginia Seed grant, an interdisciplinary model for was developed testing safety technology by linking VISSIM, a civil engineering traffic flow simulator, and MADYMO, a mechanical engineering vehicle dynamics simulator. Traffic conflicts were chosen as a surrogate for crashes due to the limitations of simulation and the rareness of crashes. The outputs of the model are the number and type of conflicts along with severity metrics and the probabilities of different injury types. Only rear-end crashes have been studied so far, but the model could be extended to cover other crash types and other explanatory variables. The model was validated on crash data from Route 50 in northern Virginia before being used for a demonstration on testing the efficacy of forward collision warning, pre-crash braking, and autonomous pre-crash braking. It is demonstrated that existing models can be tied together to examine transportation safety in terms of the roadway, vehicles, and driver and test new technologies and infrastructure improvements.