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The rapid increase in the sophistication of vehicle automation demands development of evaluation protocols tuned to understanding driver-automation interaction. Driving simulators provide a safe and cost-efficient tool for studying driver-automation interaction, and this paper outlines general considerations for simulator-based evaluation protocols. Several challenges confront automation evaluation, including the limited utility of standard measures of driver performance (e.g., standard deviation of lane position), and the need to quantify underlying mental processes associated with situation awareness and trust. Implicitly or explicitly vehicle automation encourages drivers to disengage from driving and engage in other activities. Thus secondary tasks play an important role in both creating representative situations for automation use and misuse, as well as providing embedded measures of driver engagement.

The Deep Orange framework is an integral part of the graduate automotive engineering education at Clemson University International Center for Automotive Research (CU-ICAR). The initiative was developed to immerse students into the world of an OEM. For the 6th generation of Deep Orange, the goal was to develop an urban utility/activity vehicle for the year 2020. The objective of this paper is to describe the development of a multimaterial lightweight Body-in-White (BiW) structure to support an all-electric powertrain combined with an interior package that maximizes volume to enable a variety of interior configurations and activities for Generation Z users. AutoPacific data were first examined to define personas on the basis of their demographics and psychographics.

An analysis of the first 35 back-over crashes reported by NHTSA's Special Crash Investigations unit was undertaken with two objectives: (1) to test a hypothesized classification of backing crashes into types, and (2) to characterize scenario-specific conditions that may drive countermeasure development requirements and/or objective test development requirements. Backing crash cases were sorted by type, and then analyzed in terms of key features. Subsequent modeling of these SCI cases was done using an adaptation of the Driving Reliability and Error Analysis Methodology (DREAM) and Cognitive Reliability and Error Analysis Methodology (CREAM) (similar to previous applications, for instance, by Ljung and Sandin to lane departure crashes [10]), which is felt to provide a useful tool for crash avoidance technology development.

Vehicle-to-vehicle (V2V) communication systems can enable a number of wireless-based vehicle features that can improve traffic safety, driver convenience, and roadway efficiency and facilitate many types of in-vehicle services. These systems have an extended communication range that can provide drivers with information about the position and movements of nearby V2Vequipped vehicles. Using this technology, these vehicles are able to communicate roadway events that are beyond the driver's view and provide advisory information that will aid drivers in avoiding collisions or congestion ahead. Given a typical communication range of 300 meters, drivers can potentially receive information well in advance of their arrival to a particular location. The timing and nature of presenting V2V information to the driver will vary depending on the nature and criticality of the scenario.

2011 - 56th L. Ray Buckendale Lecture Driver distraction has become an important topic in society and the research community. A telltale sign of how driver distraction has impacted society is evidenced by the designation of the term "distracted driving" as Webster's New World® College Dictionary 2009 Word of the Year. Since the release of a key study directed at commercial vehicle drivers, there have been two U.S. Department of Transportation summits to address the topic, in addition to legislation banning texting-while-driving in commercial motor vehicles. Given that "driver distraction" is a construct without a consensus definition, many studies on driver distraction have focused on its fundamental and theoretical underpinning, which is a critical first step in understanding the phenomenon.

To help drivers monitor the road and to reduce blind spots, Camera/Video Imaging Systems (C/VISs) display live video from cameras mounted on the truck's exterior to drivers using displays inside the truck cabin. This study investigated drivers' performance with C/VISs in a real-world trucking operation. Twelve commercial drivers' performance with and without a C/VIS was continuously recorded while they each drove for four months. Half of the drivers used a commercially available C/VIS that had a side-view camera on each fender. The other drivers used an advanced C/VIS (A-C/VIS) that had side-view cameras, a rear-view camera, and night-vision capabilities. This paper presents the study's final results and expands on the preliminary results that were previously reported. Detailed analyses of drivers' involvement in Safety-Critical Events (SCEs), their lane change performance, and their opinions of the C/VISs are presented.

Approximately 28,000 crashes involving combination unit trucks occur each year when they are making lane changes, merges, or turns. One contributing factor in these crashes is inadequate visibility for truck drivers. Recent advances in video technology have heightened the prospect of improving commercial vehicle safety by improving drivers' vision around the truck. For such video systems to be implemented on heavy trucks, the systems/driver interface should be demonstrated as viable through research. This paper presents the Camera/Video Imaging Systems (C/VISs) developed at Virginia Tech Transportation Institute (VTTI), the methodology used to test them, and some results obtained.

This paper discusses radio usage habits observed during analysis of 700 hours of video sampled from the 100-Car Naturalistic Driving Study database. Analysts used large-scale printouts of each vehicle's radio faceplate and recorded interactions based on video analysis of hand movement and location (without the assistance of audio recordings). The duration and specific manipulations or adjustments were recorded for each interaction. The results summarize the length and type of interactions, most often-used controls, and total percentage of time drivers interacted with the radio.

Assessing driver drowsiness and providing timely alerts is the basis for drowsy driver monitoring systems. Though technologies are available that claim to reliably provide this function, they tend to be single-metric systems that may not be sufficiently robust for real-world operation. To address this issue, a prototype system that integrated two drowsiness measures was developed. The prototype combined machine-vision-based drowsy driver monitoring technology and the analysis of driver/vehicle performance parameters with the goal of more reliably assessing driver drowsiness. The prototype concept, called PERCLOS+, used PERCLOS (a slow eye-closure measure) in combination with lane deviation (to assess driver performance). Based on preliminary on-road tests, the prototype was found to be more robust than a single-metric system.

Driver distraction, defined here as engaging in a secondary task or activity that is not central to the primary task of driving, has been shown to be a contributing factor for many crashes. The secondary tasks and other activities in which drivers choose to engage while driving is also known to be highly varied, including very complex activities(e.g., text messaging on a cellular device) to very simple activities (e.g., selecting a radio preset). Several important distinctions affect the relative risk of engaging in these tasks. Recent data from large-scale instrumented vehicle studies (i.e., “naturalistic” driving studies like the recently released “100 car study” (1)) have begun to provide data where the relative risk, in terms of crash and near crash involvement, can be directly assessed for differing secondary tasks. These data have provided some important insights into the features that create risk.

Camera/Video Imaging Systems (C/VISs) display video captured from cameras mounted on the truck's fenders and trailer to drivers using displays mounted inside the truck cabin. C/VISs provide a countermeasure to blind-spot related crashes by allowing drivers to see objects not ordinarily visible by a typical mirror configuration. They also support drivers in determining the clearance between the trailer and an adjacent vehicle when performing a lane change. The National Highway Traffic Safety Administration (NHTSA) and the Federal Motor Carrier Safety Association (FMCSA) have collaboratively funded research on the development of C/VISs that operate during the day, as well as enhancing C/VISs to operate at night and in inclement weather. This paper presents the work performed in developing a C/VIS capable of being used in an eight-month technology field demonstration (TFD), which will allow the measurement of driver behavior with the C/VIS in a revenue-producing environment.

On-board Camera/Video Imaging Systems (C/VISs) for heavy vehicles display live images to the driver of selected areas to the sides, and in back of the truck's exterior using displays inside the truck cabin. They provide a countermeasure to blind-spot related crashes by allowing drivers to see objects not ordinarily visible by a typical mirror configuration, and to better judge the clearance between the trailer and an adjacent vehicle when changing lanes. The Virginia Tech Transportation Institute is currently investigating commercial motor vehicle (CMV) driver performance with C/VISs through a technology field demonstration sponsored by the National Highway Traffic Safety Administration (NHTSA) and the Federal Motor Carrier Safety Administration (FMCSA). Data collection, which consists of recording twelve CMV drivers performing their daily employment duties with and without a C/VIS for four months, is currently underway.

Sixteen participants aged 55–65yrs provided deBoer scale ratings of discomfort glare for a vehicle with horizontally swiveling HID headlamps and a vehicle with the same headlamps that did not swivel in eight scenarios staged in a darkened parking lot. Participants, who were seated in the driver’s position of a stationary vehicle and instructed when to look, viewed the oncoming test vehicles in scenarios of 180m left turn, 180m right turn, 80m left turn, 80m right turn, left turn beside participant vehicle, crossing left in front of participant vehicle, right turn beside participant vehicle, and straightaway, in counterbalanced presentation orders. The swiveling headlamp vehicle provided statistically lower glare ratings in both 180m curves and the 80m right curve and statistically lower or similar in the intersection scenarios than the fixed headlamp vehicle.

Twent-two participants of varying ages detected roadside targets in two consecutive dynamic evaluations of a horizontally swiveling headlamp vehicle and a vehicle with the same headlamps that did not swivel. Participants detected targets as they drove unlighted low-speed public roads. Scenarios encountered were intersection turns, and curves with approximate radii of 70-90m, 120-140m, 170-190m, and 215-220m. Results from the first study found improved detection distances from the swiveling headlamps in left curves, but unexpectedly decreased detection distances in larger radius right hand curves. The swiveling algorithm was altered for the second study, and the headlamps used did not have the same beam pattern as in the first study. Results from the second study again found improved detection distances from the swiveling headlamps while in the larger radius right hand curves fixed and swivel were not statistically different.

This paper describes an effort to gather lane-changing and passing driver behavior data using naturalistic observation methods. Participants were ordinary commuters who drove instrumented vehicles to and from work while data were automatically collected. The three types of data included driver, vehicle response, and vehicle interaction data via collected video, vehicle sensor, and radar systems. These data were combined using a data integration system to understand lane-change and passing maneuvers. Developed specifically for this project, this system allows analysts to understand and characterize lane-changing and passing maneuvers by presenting the three types of data in an intuitive, integrated manner. The integrated data will facilitate understanding of driver behavior. This understanding will assist designers in the development of future crash reduction countermeasures including Crash Avoidance Systems (CAS).

Most methods of vibration analysis focus on measuring the level of vibration. Some methods like ISO-2631 weigh vibration level based on human sensitivity of location, direction, and frequency. Sound can be similarly measured by sound pressure level in dB. It may also be weighted to human frequency sensitivity such as dBA but sound and noise analysis has progressed to measure sound quality. The characteristic and the nature of the sound is studied; for example equal or near equal sound levels can provide different experiences to the listener. Such is the question for vibration; can vibration quality be assessed just as sound quality is assessed? Early on in our studies, vibration sensory experts found a difference in 4 seats yet no objective measurement of vibration level could reliably confirm the sensory experience. Still these particular experiences correlated to certain verbal descriptors including smoothness/roughness.

This paper presents the results of a study of how people interacted with a production voice-command based interface while driving on public roadways. Tasks included phone contact calling, full address destination entry, and point-of-interest (POI) selection. Baseline driving and driving while engaging in multiple-levels of an auditory-vocal cognitive reference task and manual radio tuning were used as comparison points. Measures included self-reported workload, task performance, physiological arousal, glance behavior, and vehicle control for an analysis sample of 48 participants (gender balanced across ages 21-68). Task analysis and glance measures confirm earlier findings that voice-command interfaces do not always allow the driver to keep their hands on the wheel and eyes on the road, as some assume.

Current vehicle acoustic performance prediction methods, CAE (computer aided engineering) or physical testing, have some difficulty predicting interior sound in the mid-frequency range (300 to 1000 Hz). It is in this frequency range where the overall acoustic performance becomes sensitive to not only the contributions of structure-borne sources, which can be studied using traditional finite element analysis (FEA) methods, but also the contribution of airborne noise sources which increase proportional to frequency. It is in this higher frequency range (>1000 Hz) that physical testing and statistical CAE methods are traditionally used for performance studies. This paper will discuss a study that was undertaken to test the capability of a finite element modeling method that can accurately simulate air-borne noise phenomena in the mid-frequency range.

Some rollover testing methodologies require specification of vehicle kinematic parameters including travel speed, vertical velocity, roll rate, and pitch angle, etc. at the initiation of vehicle to ground contact, which have been referred to as touchdown conditions. The complexity of the vehicle, as well as environmental and driving input characteristics make prediction of realistic touchdown conditions for rollover crashes, and moreover, identification of parameter sensitivities of these characteristics, is difficult and expensive without simulation tools. The goal of this study was to study the sensitivity of driver input on touchdown parameters and the risk of rollover in cases of steering-induced soil-tripped rollovers, which are the most prevalent type of rollover crashes. Knowing the range and variation of touchdown parameters and their sensitivities would help in picking realistic parameters for simulating controlled rollover tests.

This study presents a long-term examination of the effects of two types of perceptual-cognitive brain training programs on senior driver behavior and on-road driving performance. Seniors (70+) engaged in either a Toyota-designed in-vehicle training program based on implicit learning principles or a commercially available computer-based training program developed by Posit Science. Another group served as a no-contact control group; total enrollment was 55 participants. Participants completed a series of four experimental sessions: (1) baseline pre-training, (2) immediate post-training, (3) 6-9 months post-training, and (4) 12-16 months post-training. Experimental metrics taken at each session included measures of vehicle control and driver glance behavior on public roads.