Evaluate driver response to active warning system in level-2 automated vehicles.

As vehicles with automated functions become more prevalent on U.S. roadways, maintaining driver attention while the vehicle is engaged in automation will be an important consideration for safe operation of these vehicles. The objective of this paper is to evaluate how drivers respond and adapt to active safety warning signals in a Level 2 automatic vehicle. Specifically, statistical analysis was conducted to evaluate whether the amount of inattention prompts that drivers received changed over time, possibly indicating a change in the amount of inattention that drivers exhibited. The driving performance data was collected from sixteen participants who drove a Level 2 vehicle in an experimental setting, as part of the study Human Factors Evaluation of Level 2 and Level 3 Driving Concepts. A proprietary driver inattention warning system was installed on the experiment vehicles. The system would send a warning signal if the driver's attention was not on the primary driving task for a pre-specified duration. This study focuses on driver's response when experiencing prompts after two seconds of inattention while operating a Level 2 vehicle in automated mode. The results show that on average, the frequency of prompts the participants received decreased over the course of the experiment from 29.9 in the first ten minutes to 18.1/10 min after 110 min. The decrease levelled off after about two hours. The fact that participants received fewer prompts over time suggests that they had fewer instances of inattention lasting at least two seconds as the experiment progressed. This suggests that drivers would adapt to the alert and adjust their behavior to avoid triggering the inattention alert. The results of this study provide evidence for a potential benefit of incorporating a prompting system in vehicles with automated functions.

An analysis of driving and working hour on commercial motor vehicle driver safety using naturalistic data collection.

Current hours-of-service (HOS) regulations prescribe limits to commercial motor vehicle (CMV) drivers' operating hours. By using naturalistic-data-collection, researchers were able to assess activities performed in the 14-h workday and the relationship between safety-critical events (SCEs) and driving hours, work hours, and breaks. The data used in the analyses were collected in the Naturalistic Truck Driving Study and included 97 drivers and about 735,000 miles of continuous driving data. An assessment of the drivers' workday determined that, on average, drivers spent 66% of their shift driving, 23% in non-driving work, and 11% resting. Analyses evaluating the relationship between driving hours (i.e., driving only) and SCE risk found a time-on-task effect across hours, with no significant difference in safety outcomes between 11th driving hour and driving hours 8, 9 or 10. Analyses on work hours (i.e., driving in addition to non-driving work) found that risk of being involved in an SCE generally increased as work hours increased. This suggests that time-on-task effects may not be related to driving hours alone, but implies an interaction between driving hours and work hours: if a driver begins the day with several hours of non-driving work, followed by driving that goes deep into the 14-h workday, SCE risk was found to increase. Breaks from driving were found to be beneficial in reducing SCEs (during 1-h window after a break) and were effective in counteracting the negative effects of time-on-task.

On the training and testing of entry-level commercial motor vehicle drivers.

This study examined the effectiveness of 3 different training types on commercial motor vehicle (CMV) drivers' skill levels. The training types included a conventional 8-week certified course, a conventional 8-week certified course with approximately 60% of driving time spent in a CMV driving simulator, and a Commercial Driver's License (CDL) test focused short course. Participants' scores on the Division of Motor Vehicles (DMV) road and range tests were assessed. In addition to their DMV scores, participants replicated DMV road and range driving tests in an instrumented vehicle and the CMV driving simulator. Results indicated no training group differences in DMV road tests. There were differences between training groups on DMV range tests and real truck and simulator versions of the DMV road and range tests; on these tests conventional- and simulator-trained participants generally scored higher than CDL-focused participants. However, all groups performed higher in the real truck than in the simulator for both road and range tests. These findings indicate the need for a minimum standard of entry-level CMV driver training as well as support of the use of a driving simulator for training entry-level drivers; however, testing using a simulator does not appear to be feasible with current technology.

The impact of secondary task cognitive processing demand on driving performance.

Crash causation research has identified inattention as a major source of driver error leading to crashes. The series of experiments presented herein investigate the characteristics of an in-vehicle information system (IVIS) task that could hinder driving performance due to uncertainty buildup and cognitive capture. Three on-road studies were performed that used instrumented passenger and tractor-trailer vehicles to obtain real-world driving performance data. Participants included young, middle-aged, and older passenger vehicle drivers and middle-aged and older commercial vehicle operators. While driving, they were presented with IVIS tasks with various information densities, decision-making elements, presentation formats, and presentation modalities (visual or auditory). The experiments showed that, for both presentation modalities, the presence of multiple decision-making elements in a task had a substantial negative impact on driving performance of both automobile drivers and truck drivers when compared to conventional tasks or tasks with only one decision-making element. The results from these experiments can be used to improve IVIS designs, allowing for potential IVIS task phenomena such as uncertainty buildup and cognitive capture to be avoided.