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 VTTI’s 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 driver’s 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 “driver’s” 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 — particularly the crashworthiness, crash avoidance, and low-speed standards — in developing technical translations and the related testing procedure approaches for emerging innovative and non-traditional vehicle designs.
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. Research is needed to better understand and address the impact of automated driving systems on motor vehicle codes and other related domains. Research results will provide state departments of transportation (DOTs) and motor vehicle departments with guidance and resources to assist with the legal changes that will result from the roll out of connected and automated vehicles. This project is sponsored by the Transportation Research Board.
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
Automated vehicles are rapidly becoming a reality, and data are needed to understand the operation of various types of automated vehicles currently in use on public roads. The objective of the project is to investigate real-world driver interaction with market-ready mixed-function automation (MFA) through a naturalistic driving study (NDS). Vehicles with MFA have the capability to simultaneously activate Adaptive Cruise Control (ACC) and steering assist, allowing drivers to operate the vehicle with their hands off the steering wheel for several seconds. All systems generate alerts to notify drivers to regain control of the vehicle. The study will observe and evaluate how drivers operate five different commercially available vehicles equipped with MFA features. Drivers’ overall use of the MFA systems will be observed; additionally, specific types of interactions such as the sequence of events when regaining control and secondary task engagement will be observed. Interactions with MFA features will be observed in operation in mixed traffic under a variety of roadway types, driving conditions, and speeds. Vehicle data relevant to MFA functions will also be collected. This project will support the identification and/or refinement of human factors requirements to encourage the safe operation of highly automated vehicles.
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.