Francesco Biondi

Assessing cognitive distraction in the automobile

Objective: The objective was to establish a systematic framework for measuring and understanding cognitive distraction in the automobile. Background: Driver distraction from secondary in-vehicle activities is increasingly recognized as a significant source of injuries and fatalities on the roadway. Method: Across three studies, participants completed eight in-vehicle tasks commonly performed by the driver of an automobile. Primary, secondary, subjective, and physiological measures were collected and integrated into a cognitive distraction scale. Results: In-vehicle activities, such as listening to the radio or an audio book, were associated with a low level of cognitive workload; the conversation activities of talking to a passenger in the vehicle or conversing with a friend on a handheld or hands-free cell phone were associated with a moderate level of cognitive workload; and using a speech-to-text interfaced e-mail system involved a high level of cognitive workload. Conclusion: The research established that there are significant impairments to driving that stem from the diversion of attention from the task of operating a motor vehicle and that the impairments to driving are directly related to the cognitive workload of these in-vehicle activities. Moreover, the adoption of voice-based systems in the vehicle may have unintended consequences that adversely affect traffic safety. Application: These findings can be used to help inform scientifically based policies on driver distraction, particularly as they relate to cognitive distraction stemming from the diversion of attention to other concurrent activities in the vehicle.

Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety.

This study investigated whether multimodal redundant warnings presented by advanced assistance systems reduce brake response times. Warnings presented by assistance systems are designed to assist drivers by informing them that evasive driving maneuvers are needed in order to avoid a potential accident. If these warnings are poorly designed, they may distract drivers, slow their responses, and reduce road safety. In two experiments, participants drove a simulated vehicle equipped with a forward collision avoidance system. Auditory, vibrotactile, and multimodal warnings were presented when the time to collision was shorter than five seconds. The effects of these warnings were investigated with participants performing a concurrent cell phone conversation (Exp. 1) or driving in high-density traffic (Exp. 2). Braking times and subjective workload were measured. Multimodal redundant warnings elicited faster braking reaction times. These warnings were found to be effective even when talking on a cell phone (Exp. 1) or driving in dense traffic (Exp. 2). Multimodal warnings produced higher ratings of urgency, but ratings of frustration did not increase compared to other warnings. Findings obtained in these two experiments are important given that faster braking responses may reduce the potential for a collision.

Cell-phone use diminishes self-awareness of impaired driving

Multitasking diminishes the self-awareness of performance that is often essential for self-regulation and self-knowledge. Participants drove in a simulator while either talking or not talking on a hands-free cell phone. Following previous research, participants who talked on a cell phone made more serious driving errors than control participants who did not use a phone while driving. Control participants’ assessments of the safeness of their driving and general ability to drive safely while distracted were negatively correlated with the actual number of errors made when they were driving. By contrast, cell-phone participants’ assessments of the safeness of their driving and confidence in their driving abilities were uncorrelated with their actual errors. Thus, talking on a cell phone not only diminished the safeness of participants’ driving, it diminished their awareness of the safeness of their driving.

Evaluating the impact of processing spoken words on driving

The potential impact on driving of the processing of a single, auditorily presented word is analyzed in this work. Because driving is a complex cognitive activity that involves the integration and coordination of multiple subprocesses, the authors narrowed the scope of the research to concentrate on one critical task involved in driving: driver braking response. If two tasks have to be performed concurrently and both of them require access to a capacity-limited system, then performance in one or both of the tasks will dramatically worsen because the two processes will compete for access to cognitive resources. It has been shown that both word recognition and driving require central resources; therefore, these tasks are likely to interfere with each other. In the experiments, participants were required to perform two tasks during simulated driving. In the word recognition task, participants had to categorize auditorily presented words. In the braking task, participants depressed a brake pedal in response to the lead cars brake lights. The interval of time between the onset of the tasks’ stimuli was varied. Braking responses were substantially slower as the overlap between tasks increased. This finding demonstrates that the processing of a single word hinders driving performance. The experiments carried out have significant implications in the field of road safety. Many situations, such as cell phone ringing, cell phone conversations, auditory tips from navigation systems, and auditory alerts from driver warning systems, are similar to those studied. The experiments suggest that all these situations can negatively affect a drivers response time, increasing the likelihood of near misses and accidents.

Partial-autonomous Frenzy: Driving a Level-2 Vehicle on the Open Road

Partial-autonomous vehicles are among us and represent a prominent testing ground for assessing the human interaction with autonomous vehicles. One main limitation of the studies investigating would-be users’ attitude toward partial to full autonomous driving stems from their indirect experience with such technology. In this study, participants drove a partial-autonomous vehicle on the open road and interacted with both Adaptive Cruise Control (ACC) and Lane Keeping Assist (LKAS) systems. Preliminary results show participants rating level-2 autonomous features as possible sources of stress. Participants had issues engaging these systems with denser traffic and thought these systems to be more beneficial in traffic-free driving. Compared to ACC, engaging LKAS and monitoring its functioning represented a more challenging task and participants’ ratings of stress toward this system increased over time. Findings obtained in this study are of importance for exploring user interaction with future highly-autonomous vehicles and designing effective countermeasures to make the human-machine interface of these systems more informative and easier to use.

Traffic-Calming Measures Affecting Perceived Speed in Approaching Bends: On-Field Validated Virtual Environment

With the aim of reducing the number of road traffic deaths around the world, the United Nations General Assembly proclaimed 2011–2020 the Decade of Action for Road Safety. Excessive speed is one of the main problems to overcome. The aim of this study was to test the effectiveness of traffic-calming measures in reducing drivers’ speed along a road with a dangerous bend in an inland area near Venice, Italy. The driving simulator of the Transportation Laboratory of the University of Padua and the simulated scenario were validated by reproducing the study site environment. A driving simulator experiment was conducted to analyze changes in speed profiles associated with various countermeasures: evenly spaced guideposts, tall guideposts, narrowing guideposts, and dragons teeth markings. Tall and narrowing guideposts served to reduce drivers’ speed by up to 2.7 km/h. Unlike tall guideposts, which produced no detrimental effect on drivers’ behavior, narrowing guideposts led drivers to occupy more variable positions within the lane. In view of this at-risk behavior, the convenience of the option to produce an apparent narrowing of the lane on real roads was discussed. This study found that the use of driving simulators was a reliable research tool to reproduce drivers’ real behavior. The study also provided effective, low-cost measures to counteract excessive speed on dangerous road sections.

The Effects of Voice System Design Components on Driver Workload

In-vehicle voice control systems are standard in most new vehicles. However, despite auditory-vocal interaction allowing drivers to keep their hands on the steering wheel and eyes on the forward roadway, recent findings indicate the potential for these systems to increase levels of workload and lead to lengthy interaction times. Although many studies have examined the distraction potential of interacting with in-vehicle voice control systems, more research is needed to understand the relationship between different system design components and workload. In this study, we investigate the role of system delay, system accuracy, and menu depth in determining the overall level of demand and interaction times on eight different 2017 model-year vehicles. Voice system accuracy was measured via playback of a pre-recorded sample of voice commands through a studio monitor mounted near the headrest. Menu depth and system delay were calculated by measuring, respectively, the number of interaction steps and total system processing time required to access common infotainment functions. These measures were validated through linear and multiple regression analyses with workload and task time collected in an on-road study. We found system delay and system accuracy to be significant predictors of task time and subjective measures of workload from the NASA Task Load Index and the Driving Activity Load Index. A In addition to providing valuable information on the role of separate voice control system design components on resulting levels of workload, these results extend past research by generalizing findings to multiple current auditory-vocal systems.

The Challenge of Advanced Driver Assistance Systems Assessment: A Scale for the Assessment of the Human–Machine Interface of Advanced Driver Assistance Technology

Despite driver assistance systems being engineered to enhance safety, recent studies show the potential for some of these systems and deficient human–machine interfaces to cause unintended consequences on safety. The NHTSA, the Alliance of Automotive Manufacturers, and the European Commission have all released best practices and human factors guidelines for the development and assessment of function-aspecific interfaces. However, given their broad scope, none of these documents provides a rating and benchmarking tool for assessing design aspects pertinent to a wide spectrum of assistance systems, ranging from rearview cameras to lane keeping assist systems. In this article we detail the development of a scale for assessing the human–machine interfaces of 10 different assistance systems. The scale contains 59 items, developed through multiple iterations, in which a total of 94 distinct assistance systems available on vehicles of different makes and models underwent evaluation. For each system included, we provide a description of its characteristics, a list of items for assessment, and relevant references. Widely accepted industry (ISO, SAE) standards, design guidelines, and assessment methodologies were considered for the development of this scale. The adoption of this scale required at least two evaluators to rate each system against specific assessment items using the following 4-point scale: No Concern (3 points), Minor Concerns (2 points), Serious Concerns (1 point), Not Applicable (0 points). Final ratings resulting from this evaluation will aid evaluators in the benchmarking process, and in determining what specific design aspects of the systems assessed merit further attention.

80 MPH and out-of-the-loop: Effects of real-world semi-automated driving on driver workload and arousal

The introduction of semi-automated driving systems is expected to mitigate the safety consequences of human error. Observational findings suggest that relinquishing control of vehicle operational control to assistance systems might diminish driver engagement in the driving task, by reducing levels of arousal. In this study, drivers drove a Tesla Model S with Autopilot in both semi-automated and manual modes. Driver behavior was monitored using a combination of physiological and behavioral measures. Compared to manual driving, a reduction in driver physiological activation was observed during semi-automated driving. Also, performance to the peripheral detection task suffered in semi-automated mode, with slower response times recorded in this condition than during manual driving. Taken together, our data suggest that semi-automated driving might not ease safety consequences of human error. Instead, these findings suggest it might cause a drop in driver monitoring, possibly followed by a spike in automation-generated distraction.

Performance and Workload Trends: The Effects of Repeated Exposure to “High” Demand Tasks

The N-back and Surrogate Reference Task (SuRT) are frequently used to evaluate the workload potential of secondary driving tasks as high cognitive and visual demand benchmarks. This paper examines the effect of repeated exposures to the N-back and SuRT reference tasks, and any resulting change in task performance or workload that may negate their effectiveness as calibration tools and high workload benchmarks. One-way repeated measures ANOVA analyses demonstrate that N-back performance improves while workload decreases, suggesting limitations in methodology for measuring task workload after multiple exposures. Alternatively, SuRT performance improves while workload remains relatively stable, indicating the task elicits a constant visual demand despite performance improvements. This paper discusses the limitations of the N-back and SuRT as reference tasks in workload and driving research, and proposes future directions to further clarify their use.