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This paper describes (1) the telematics distraction/overload problem, (2) what distraction and overload are and how they differ, (3) the standards and guidelines that apply to the design and evaluation of driver interfaces/human-machine interfaces (HMI) for telematics (and their strengths and weaknesses), and (4) what standards and research are needed to support the development of driver interfaces. Most of the paper is a detailed discussion of evaluation standards, in particular SAE Recommended Practices J2364 (Task Time and Occlusion Tests) and J2365 (Task Time Estimation), ISO Standards 16673 (Occlusion Test) and 26022 (Lane-Change Test), and the AAM Driver Focus Guideline.

This paper (1) summarizes previous human factors/safety research on parking (8 studies, mostly over 20 years old), (2) provides statistics for 10,400 parking-related crashes in Michigan from 2000-2002, and (3) summarizes interviews with 6 insurance agents concerning parking crashes. These sources indicate: 1 About 1/2 to 3/4 of parking crashes involve backing, often into another moving vehicle while emerging from a parking stall. 2 Eight-and-a-half foot-wide stalls had higher crash rates than wider stalls. 3 Most parallel parking crashes occur on major streets, not minor streets. 4 Lighting and driver impairment were minor factors in parking crashes.

Driving workload managers continually assess the difficulty of driving and regulate the flow of information to drivers that could interfere with driving, such as automatically diverting an incoming phone call to an answering machine when a driver is turning at an intersection. This paper summarizes the pertinent crash and human performance literature, identifies the unique nature of telematics tasks, and describes likely workload manager architectures, applicable regulations, and current industry efforts. In addition to promoting telematics system safety and enhancing warning systems, research on workload managers is likely to advance the science of driving and provide many other safety benefits.

This paper explores the potential safety performance of “Future Generation” automated speed control crash avoidance systems for Commercial Vehicles. The technologies discussed in this paper include Adaptive Cruise Control (ACC), second and third generation Forward Collision Avoidance and Mitigation Systems (F-CAM) comprised of Forward Collision Warning (FCW) with Collision Mitigation Braking (CMB) technology as applied to heavy trucks, including single unit and tractor semitrailers. The research [1[ discussed in this paper is from a study conducted by UMTRI which estimated the safety benefits of current and future F-CAM systems and the comparative efficacy of adaptive cruise control. The future generation systems which are the focus of this paper were evaluated at two separate levels of product refinement, “second generation” and “third generation” systems.

As new technology is added to vehicles and traffic congestion increases, there is a concern that drivers will be overloaded. As a result, there has been considerable interest in measuring driver workload. This can be achieved using many methods, with subjective assessments such as the NASA Task Loading Index (TLX) being most popular. Unfortunately, the TLX is unanchored, so there is no way to compare TLX values between studies, thus limiting the value of those evaluations. In response, a method was created to anchor overall workload ratings. To develop this method, 24 subjects rated the workload of clips of forward scenes collected while driving on rural, urban, and limited-access roads in relation to 2 looped anchor clips. Those clips corresponded to Level of Service (LOS) A and E (light and heavy traffic) and were assigned values of 2 and 6 respectively.

The Lane Change Task (LCT) provides a simple, scorable simulation of driving, and serves as a primary task in studies of driver distraction. It is widely accepted, but somewhat limited in functionality, a problem this project partially overcomes. In the Lane Change Task, subjects drive along a road with 3 lanes in the same direction. Periodically, signs appear, indicating in which of the 3 lanes the subject should drive, which changes from sign to sign. The software is plug-and-play for a current Windows computer with a Logitech steering/pedal assembly, even though the software was written 18 years ago. For each timestamp in a trial, the software records the steering wheel angle, speed, and x and y coordinates of the subject. A limitation of the LCT is that few characteristics of this useful software can be readily modified as only the executable code is available (on the ISO 26022 website), not the source code.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Engaging in visual-manual tasks such as selecting a radio station, adjusting the interior temperature, or setting an automation function can be distracting to drivers. Additionally, if setting the automation fails, driver takeover can be delayed. Traditionally, assessing the usability of driver interfaces and determining if they are unacceptably distracting (per the NHTSA driver distraction guidelines and SAE J2364) involves human subject testing, which is expensive and time-consuming. However, most vehicle engineering decisions are based on computational analyses, such as the task time predictions in SAE J2365. Unfortunately, J2365 was developed before touch screens were common in motor vehicles.