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Center for Automated Vehicle Systems

Automated vehicle

The Center for Automated Vehicle Systems uses a transdisciplinary approach to support the design of safe and high-quality automated vehicles and the infrastructure in which they will operate. The center conducts pragmatic research based on a scientific approach that emphasizes the importance of safety and usability of driver assistance systems through fully autonomous systems. The center applies a mixture of human factors methods, traditional engineering analyses, and data analytics to support automobile manufacturers, suppliers, technology companies, and public agencies.

Shane McLaughlin, Center Director

Shane McLaughlin
Center Director

Featured Projects

Head-Up Displays and Distraction Potential

Automotive Head-Up Displays (HUDs) 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, HUDs may also create new challenges related to driver distraction. For example, while HUDs can minimize the amount of time required to view a display compared to a traditional Head-Down Display (HDD), viewing HUDs while driving may prevent drivers from perceiving events in the environment. In addition, if drivers perceive HUDs to be safer than HDDs, they may not regulate the length of time they spend looking at HUDs. The HUD may therefore negatively alter the visual scanning behavior of drivers. Read More

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 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. VTTI is collaborating with General Motors, Google, Southwest Research Institute, Battelle Memorial Institute, and Bishop Consulting to ensure that the issues addressed by this project are relevant to real-world applications and system concepts. This effort will 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 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 for both heavy trucks and motorcoaches are also emerging in the marketplace. CWSs use a variety of sensor technologies (e.g., radar, 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 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

Field Study of Heavy-Vehicle Collision Avoidance Systems

Substantial advancements have been made in Collision Avoidance System (CAS) technology, including advanced radar sensors, enhanced 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 systems, which generate audible and visual alerts when a rear-end conflict emerges; 2) collision mitigation braking systems, which automatically decelerate the vehicle when a driver fails to respond to a rear-end conflict; and 3) lane departure warning 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 has contracted VTTI to perform a field study of heavy-vehicle CASs. This field study will evaluate CAS performance on 150 trucks supplied by Bendix and Meritor WABCO and driven for up to 12 months each. The participating fleets’ satisfaction and acceptance levels with the CASs will be assessed at the end of the test period. This study will generate an unprecedented amount of insight into CAS reliability, driver response to CASs, and fleet acceptance of the technology.

Center Faculty

Gibran Ali

Research Associate

Jon Atwood

Research Associate

Matthew Casadonte

Project Associate

Tom Champagne

Project Assistant

Robert McCall

Research Associate

Shane McLaughlin

Center Director

Joshua Radlbeck

Senior Research Specialist

Cameron Rainey

Senior Research Associate

Sheldon Russell

Senior Research Associate

Andy Schaudt

Program Director

Vicki Williams

Human Factors Engineer

Contact

  • 540-231-1500
  • 540-231-1555

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