Center for Automated Vehicle Systems
The Center for Automated Vehicle Systems uses a transdisciplinary approach to supporting 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. We perform this work for automobile manufacturers, suppliers, technology companies and public agencies. The group emphasizes a mixture of human factors methods, traditional engineering analyses and data analytics.
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
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
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.