Center for Automated Vehicle Systems

The Center for Automated Vehicle Systems (CAVS) presents an interdisciplinary approach to studying all aspects related to the automation life cycle in the field of transportation. CAVS conducts pragmatic research based on a scientific approach that emphasizes the importance of safety, security, reliability, and user acceptance. CAVS is anchored in applied research and strengthened by collaborations with national and international partners in vehicle automation, including groups involved in research, planning, policy, and the production of automated vehicles. Our goal is to strengthen the safety benefits of automation across all levels of the transportation industry.

Shane McLaughlin
Center Director

Head-Up Displays and Distraction Potential

Automotive Head-Up Displays (HUD) project vehicle information, such as speed in or near a driver’s field of view (typically in the lower portion of the windshield). HUD technology presents many opportunities for mitigating driver distraction, improving driver comfort, and engaging drivers with their vehicles; however, it may also create new challenges related to driver distraction. HUDs can minimize the amount of time required to view a display relative to a traditional Head-Down Display (HDD), but viewing HUDs while driving may prevent drivers from perceiving events in the environment. There is also a concern that if drivers perceive HUDs to be safer than HDDs, they may not regulate the length of time they spend looking at the HUD. The HUD may therefore negatively alter drivers’ visual scanning behavior. Read More

Head-Up Displays and Distraction Potential

Automotive Head-Up Displays (HUD) project vehicle information, such as speed in or near a driver’s field of view (typically in the lower portion of the windshield). HUD technology presents many opportunities for mitigating driver distraction, improving driver comfort, and engaging drivers with their vehicles; however, it may also create new challenges related to driver distraction. HUDs can minimize the amount of time required to view a display relative to a traditional Head-Down Display (HDD), but viewing HUDs while driving may prevent drivers from perceiving events in the environment. There is also a concern that if drivers perceive HUDs to be safer than HDDs, they may not regulate the length of time they spend looking at the HUD. The HUD may therefore negatively alter drivers’ visual scanning behavior.

The approach of this work is to evaluate driver behavior while using HUDs by designing and conducting experiments within a framework that addresses the distraction potential of HUD use. This project will assess general driving while on public roads as well as more complex scenarios on the Virginia Smart Road. Measures of vehicle control as well as visual behavior will be analyzed to determine if there are negative consequences associated with HUD use.

This National Highway Traffic Safety Administration (NHTSA)-sponsored research into the distraction potential of HUDs in light vehicles began in September 2014 and will conclude in 2016.

Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts

Automation has the potential to improve highway safety by supporting or supplementing the driver, providing precise vehicle control during normal driving, and by maintaining appropriate driver attention to traffic and roadway conditions. This project aims to answer some of the most fundamental human factors research questions focused on the issue of drivers transitioning into and out of automated driving states enabled by Level 2 and Level 3 automated vehicles. Collaboration with General Motors, Google, Southwest Research Institute, Battelle Memorial Institute, and Bishop Consulting will ensure that the issues addressed by this project are those resulting from emerging real-world applications and system concepts. This effort will both help identify the fundamental human factors research questions related to automated driving, and further the community’s understanding of the education, development, deployment, and assessment needs of automated vehicle systems.

Evaluation of Heavy Vehicle Collision Warning Interfaces

Collision warning systems (CWSs; also referred to as collision avoidance systems [CASs] or pre-crash systems) for heavy vehicles have been commercially available for more than 15 years. CWSs include features such as forward collision and lane-departure warnings and/or mitigation systems. Integrated systems are also emerging in the marketplace and are becoming available for both heavy trucks and motorcoaches. CWSs use a variety of sensor technologies (e.g., radar, Light Detection and Ranging (LIDAR), and machine vision) to determine the risk of a collision. CWSs then warn the driver to take action to avoid or mitigate a potential crash. Truck and motorcoach original equipment manufacturers (OEMs) are currently working to increase the safety benefits of these systems by directly controlling the vehicle in advance of a potential collision (i.e., reducing engine power, engaging the brakes or collision-mitigating brakes, or inducing a steering action). Read More

Evaluation of Heavy Vehicle Collision Warning Interfaces

Collision warning systems (CWSs; also referred to as collision avoidance systems [CASs] or pre-crash systems) for heavy vehicles have been commercially available for more than 15 years. CWSs include features such as forward collision and lane-departure warnings and/or mitigation systems. Integrated systems are also emerging in the marketplace and are becoming available for both heavy trucks and motorcoaches. CWSs use a variety of sensor technologies (e.g., radar, Light Detection and Ranging (LIDAR), and machine vision) to determine the risk of a collision. CWSs then warn the driver to take action to avoid or mitigate a potential crash. Truck and motorcoach original equipment manufacturers (OEMs) are currently working to increase the safety benefits of these systems by directly controlling the vehicle in advance of a potential collision (i.e., reducing engine power, engaging the brakes or collision-mitigating brakes, or inducing a steering action).

CWSs are available both as options from OEMs and as aftermarket/retrofit devices. While there are certain functional and performance similarities between offerings within a particular CWS product class (e.g., forward collision warning [FCW]), there are also differences in how suppliers present collision warnings, including the design of visual and auditory displays. Typically, suppliers will use some combination of visual and audio modalities to convey a collision threat level. However, their implementations vary across factors such as the visual interface design, an auditory alert, and the salience of alerts. While CWS implementations change and evolve, it is likely that certain warning interfaces are more effective than others under certain conflict or crash-imminent situations. Further, different vehicle types may demand different collision warning interfaces due to special requirements (e.g., limiting passengers' exposure to certain warnings in the motorcoach environment). Unfortunately, these factors have not been extensively examined as they pertain to heavy vehicles.

The approach of this work is to implement a research strategy for evaluating the effectiveness of the CWS interface. This project will: 1) Examine the issues and current state of the art for CWS interface design, 2) Assess the potential safety improvements of interface design elements through an experimental approach, and 3) Summarize the project findings. Further, the results from this study will be captured in a set of design principles for CWS interfaces. The primary goal of this research is to improve the effectiveness of a CWS interface (i.e., an FCW system) for heavy trucks and motorcoaches by completing four major steps:

  1. Identify research gaps from the existing literature;
  2. Design and conduct experiments within a framework that addresses research gaps and needs through test-track studies and other research methods;
  3. Evaluate the relative effectiveness of various warning interface strategies for alerting the driver to pre-crash conditions; and
  4. Recommend comprehensive design principles for crash warning interfaces.

This National Highway Traffic Safety Administration (NHTSA)-sponsored project began in September 2011 and will conclude in 2015.

Field Study of Heavy-Vehicle Collision Avoidance Systems

Substantial advancements have been made in Collision Avoidance System (CAS) technology, including advanced radar sensors, better camera and vision technologies, improved object detection algorithms, and automatic braking. Heavy vehicles can now be equipped with the following CASs: 1) Forward Collision Warning (FCW) systems, which generate audible and visual alerts when a rear-end conflict emerges; 2) Collision Mitigation Braking (CMB) systems, which automatically decelerate the vehicle when a driver fails to respond to a rear-end conflict; and 3) Lane Departure Warning (LDW) systems, which alert the driver when the vehicle drifts past the lane markings. In an effort to evaluate the reliability of these systems, the National Highway Traffic Safety Administration (NHTSA) has contracted the Virginia Tech Transportation Institute (VTTI) to perform a field study of heavy-vehicle CASs. This field study is designed to evaluate CAS performance by measuring its operation on 150 trucks. Two CAS suppliers are involved in this study: Bendix and Meritor WABCO. These vendors will install their CASs on approximately 75 trucks each. Each truck will be equipped with the VTTI Mini Data Acquisition System (MiniDAS). The MiniDAS records video of the roadway and the driver along with parametric data from the CAS and vehicle network. Participating drivers will drive the instrumented vehicles for up to 12 months each. The participating fleets’ satisfaction and acceptance levels with the collision mitigation technologies will be assessed at the end of the test period. This study will generate an unprecedented amount of insight into CAS reliability, driver response performance when using the CASs, and fleet acceptance of the technology.

Gibran Ali

Research Associate

Jon Atwood

Research Associate

Tom Champagne

Project Assistant

Zack Crane

Senior Research Specialist

Scott Fritz

Project Associate

Robert McCall

Research Associate

Joshua Radlbeck

Senior Research Specialist

Cameron Rainey

Research Associate

Sheldon Russell

Senior Research Associate

Andy Schaudt

Project Director

Vicki Williams

Human Factors Engineer