Center for Public Policy, Partnerships, and Outreach
The Center for Public Policy, Partnerships, and Outreach (C3PO) has several areas of focus. The Center assists with the needed models of rules and regulations for advanced vehicles (e.g., driver assistance systems, connected and/or automated vehicles), provides research to ensure state and federal policies are based on relevant data, develops partnerships to assist in the development of new systems, and enhances VTTIâs research areas and sponsorship diversity. The Center works with stakeholders whose interests are affected by governmental decisions on federal, state, local, or international levels in the development and implementation of automated vehicle systems. The nature of this work is intended to inform policymakers as well as original equipment manufacturers and automotive suppliers on a wide range of issues related to transformational transportation technologies, including those associated with connected and automated vehicle systems, shared mobility networks, and the development of Smart City solutions, including the resiliency of these systems and networks. Outreach efforts – "VTTI Research in Motion" – are focused on performing applied research and ensuring the results are provided in a timely fashion to those who can benefit.
Assessment, Evaluation, and Approaches to Technical Translations of FMVSS and Test Procedures That May Impact Compliance of Innovative New Vehicle Designs Associated with Automated Driving Systems
Many Federal Motor Vehicle Safety Standards (FMVSS) were created with the underlying assumption that vehicles include standard equipment such as steering wheels, brake pedals, and driverâs seats. However, innovative new vehicle designs are tailored for higher levels of automated driving systems which may not need physical steering wheels or brake pedals to operate, and there may be no designated âdriverâsâ seat. Under a new NHTSA contract, C3PO will lead a team comprised of experts from the automotive, legal, and research sectors to examine the current FMVSS â particularly the crashworthiness, crash avoidance, and low-speed standards â in developing technical translations and the related testing procedure approaches for emerging innovative and non-traditional vehicle designs. Read More
NCHRP 20-102(07) Implications of Automation for Motor Vehicle Codes
Existing motor vehicle codes have been developed based on implicit assumptions about drivers maintaining continuous involvement in the driving task and continuous responsibility for managing traffic safety hazards. Automated driving systems significantly reduce the role of the driver, which means that some of these codes will need to be reconsidered. The incorporation of driving behavior into in-vehicle software also generates pressure to harmonize the rules of the road across jurisdictions. Read More
Automated Vehicle Crash Rate Comparison Using Naturalistic Data
Self-driving cars are quickly moving from prototype to everyday reality. During this transition, the question that is first and foremost on the mind of the public and policy-makers is whether or not self-driving cars are more prone to crashes. Existing comparisons based on current data is problematic: collection methodologies in each states differ, with inconsistent requirements on what incidents are reported as crashes. Many crashes also go unreported. The research in this report "Automated Vehicle Crash Rate Comparison Using Naturalistic Data", which was performed by the Virginia Tech Transportation Institute (VTTI), and commissioned by Google, sheds light on these issues. It examines both national crash data and data from naturalistic driving studies that closely analyzes the behavior of 3,300 vehicles driving more than 34 million vehicle miles, to better estimate existing crash rates, and then compares the results to data from Google's Self-Driving Car program. Read More
Naturalistic Study of Level 2 Driving Automation Functions
The objective of this project was to investigate, through a naturalistic driving study (NDS), real-world driver interaction with commercially available driving automation systems. Ten vehicles equipped with both lateral and longitudinal automated features were instrumented and loaned to participants for a 4-week period. A total of 120 drivers were recruited over a 14-month data collection period. Participants drove 216,585 miles, with 70,384 miles driven with both lateral and longitudinal control features active. Drivers were observed engaging in non-driving tasks, but these were not related to feature use. Driver behavior was consistent with active driving/supervision of the automated features; drivers were receptive to Request to Intervene (RTI) alerts. No RTIs were associated with any Safety-Critical Events (SCEs; Crashes and Near-Crashes). In total, 5 minor crashes (no injury or visible damage) and 66 near-crashes were observed across the entire data set. No statistical relationship was observed between SCE rates and feature activation level. A sub-study specifically focused on longer drives was also conducted. Results observed in the substudy were similar to those observed in the broader NDS. Implications per the study research questions are presented herein. Comparisons to related research studies and limitations of the current research effort are also discussed. Read More
Consumer Active Safety Education
Active safety features, especially those associated with forward collision detection and avoidance technologies, have the potential to greatly reduce the number of serious accidents and fatal crashes in the coming years. However, for this potential to be met, consumers will need to be educated on the purpose, benefits, limitations, and proper use of these active safety technologies in order to prevent potential misuse and/or abuse. This study, which is sponsored by the National Center for Surface Transportation Safety and Research (NCSTSR), is evaluating the most effective strategies for providing information on Level 2 automation technologies that are currently or soon to be on the market, such as: emergency brake assist, low-speed obstacle detection with automatic braking, and adaptive cruise control with automatic braking. The research team is building upon existing knowledge, particularly that which relates to active safety technologies and driver training and online engagement pedagogies, to develop and recommend effective strategies for providing information related to these active safety features to consumers.