This paper presents initial work on a context-dependent driver distraction detection algorithm called AttenD2.0, which extends the original AttenD algorithm with elements from the Minimum Required Attention (MiRA) theory. Central to the original AttenD algorithm is a time buffer which keeps track of how often and for how long the driver looks away from the forward roadway. When the driver looks away the buffer is depleted and when looking back the buffer fills up. If the buffer runs empty the driver is classified as distracted. AttenD2.0 extends this concept by adding multiple buffers, thus integrating situation dependence and visual time-sharing behaviour in a transparent manner. Also, the increment and decrement of the buffers are now controlled by both static requirements (e.g. the presence of an on-ramp increases the need to monitor the sides and the mirrors) as well as dynamic requirements (e.g., reduced speed lowers the need to monitor the speedometer). The algorithm description is generic, but a real-time implementation with concrete values for different parameters is showcased in a driving simulator experiment with 16 bus drivers, where AttenD2.0 was used to ensure that drivers are attentive before taking back control after an automated bus stop docking and depot procedure. The scalability of AttenD2.0 relative to available data sources and the level of vehicle automation is demonstrated. Future work includes expanding the concept to real-world environments by automatically integrating situational information from the vehicles environmental sensing and from digital maps.

The European Rail Traffic Management System (ERTMS) will replace national standards with the aim to promote cross-border traffic and enhance efficiency. The transition involves a shift from lineside signalling to mostly in- cabin information via a Driver Machine Interface (DMI). Previous research indicates that this may lead to a decrease in driver attention to the outside world and to a decrease in workload, leading to boredom. Using a train simulator, 41 participants drove the same track with the ERTMS system and the Swedish national standard (ATC) while wearing eye- tracking equipment. Subjective workload and boredom assessments were made after each drive. An analysis of the first set of reduced data (15 participants) showed that the formal attentional requirements like the monitoring of speed changes and signals were fulfilled in almost all cases, regardless of system. Overall, however, the data indicate that in line with previous research the drivers focus their attention more to the inside of the train when using the ERTMS system. This is corroborated by the finding that horn blowing is slightly delayed with the ERTMS system. Perceived workload was generally low, with the ERTMS system experienced to be more boring. We draw the preliminary conclusion that while formal attentional requirements are fulfilled for both systems, the ERTMS system likely has a tendency to pull the drivers’ overall attention inwards. Given that for the ERTMS system most relevant information is presented inside of the train on the DMI, this is not surprising, but needs to be addressed by the authorities.

Objective:

To assess the attentional demand of different contextual factors in driving.

Background:

The attentional demand on the driver varies with the situation. One approach for estimating the attentional demand, via spare capacity, is to use visual occlusion.

Method:

Using a 3 × 5 within-subjects design, 33 participants drove in a fixed-base simulator in three scenarios (i.e., urban, rural, and motorway), combined with five fixed occlusion durations (1.0, 1.4, 1.8, 2.2, and 2.6 s). By pressing a microswitch on a finger, the driver initiated each occlusion, which lasted for the same predetermined duration within each trial. Drivers were instructed to occlude their vision as often as possible while still driving safely.

Results:

Stepwise logistic regression per scenario indicated that the occlusion predictors varied with scenario. In the urban environment, infrastructure-related variables had the biggest influence, whereas the distance to oncoming traffic played a major role on the rural road. On the motorway, occlusion duration and time since the last occlusion were the main determinants.

Conclusion:

Spare capacity is dependent on the scenario, selected speed, and individual factors. This is important for developing workload managers, infrastructural design, and aspects related to transfer of control in automated driving.

Application:

Better knowledge of the determinants of spare capacity in the road environment can help improve workload managers, thereby contributing to more efficient and safer interaction with additional tasks.

New in-vehicle information systems are now being commercialized. Despite the expected benefits, some concerns exist that they may overload drivers’ capacity and decrease performance. According to the multiple resource theory (Wickens, Hum Factors 50:449–455, https://doi.org/10.1518/001872008X288394 , 2008), overload may occur at different stages of processing, that is, perceptual–central and/or response-related stages. Therefore, different measures may be needed to detect such specific demands. We explored the sensitivity of different mental workload measurements during the performance of an auditory task alone (single task) and in combination with a tracking task that was presented without (dual task) or, with a visual display (triple task). The demands associated with the number of concurrent tasks (single, dual and triple tasks), tracking speed (low, high, adjustable) and their interaction were analyzed. To account for different processing requirements, mental workload was assessed using subjective, behavioral (performance on the auditory task) and psychophysiological measurements (event-related potentials). 17 young adults participated in the study. The results showed that most measurements discriminated between the performances of one or more tasks, as well as between low and high speeds. However, only the subjective ratings and tracking task performance further discriminated between the dual- and triple-task conditions. Finally, ERPs (N1 and P3) were the only measure detecting increases in cognitive demands associated with higher requirements on processing speed combined with the addition of the display. Our results suggest that ERPs may provide complementary information to other traditional mental workload measures. Its applications in the evaluation and design of future systems should be investigated.

Traffic light assistance systems (TLASs) can be infrastructure based or on-board, and in the latter case they can inform the driver about the time remaining to green, or about the recommended speed for a smooth passage at green. A speed-based and a time-based on-board system prototype was compared against each other and against a baseline without any assistance system. Using a within-subjects design, 18 participants drove in a fixed-base simulator along a suburban road with signalised intersections, where the delay to green was set to zero (allowing a passage at the current speed), "half-speed" (requiring a clear speed reduction) and "stop" (requiring a substantial speed reduction). Driving behaviour, visual attention distribution and acceptance were evaluated. Both support systems improved driving efficiency and comfort over baseline, with the time-based system achieving higher scores in general. Both systems attracted a substantial amount of visual attention in the current setting; however, single-glance durations were below 1 s, and the number of glances forward were equal in the time-based condition compared to baseline, but lower in the speed-based condition. No red or amber light violations were registered in baseline, while some occurred with any of the systems. Acceptance for both systems was high, with higher scores for the time-based prototype. Overall, an on-board TLAS with a countdown timer to green has the potential to increase efficiency and comfort without strong indications for attention disruption, but the risk for increased red/amber light violations has to be addressed. Improved system design as a way to mitigate potential issues is discussed.

Indicators based on visual time-sharing have been used to investigate drivers visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicles coordinate system, but also for environmental and traffic targets that move independently of the drivers vehicle.

While in-vehicle eco-driving support systems have the potential to reduce greenhouse gas emissions and save fuel, they may also distract drivers, especially if the system makes use of a visual interface. The objective of this study is to investigate the visual behaviour of drivers interacting with such a system, implemented on a five-inch screen mounted above the middle console. Ten drivers participated in a real world, on-road driving study where they drove a route nine times (2 pre-baseline drives, 5 treatment drives, 2 post-baseline drives). The route was 96 km long and consisted of rural roads, urban roads and a dual-lane motorway. The results show that drivers look at the system for 5-8% of the time, depending on road type, with a glance duration of about 0.6 s, and with 0.05% long glances (amp;gt;2s) per kilometre. These figures are comparable to what was found for glances to the speedometer in this study. Glance behaviour away from the windscreen is slightly increased in treatment as compared to pre- and post-baseline, mirror glances decreased in treatment and post-baseline compared to pre-baseline, and speedometer glances increased compared to pre-baseline. The eco-driving support system provided continuous information interspersed with additional advice pop-ups (announced by a beep) and feedback pop-ups (no auditory cue). About 20% of sound initiated advice pop-ups were disregarded, and the remaining cases were usually looked at within the first two seconds. About 40% of the feedback pop-ups were disregarded. The amount of glances to the system immediately before the onset of a pop-up was clearly higher for feedback than for advice. All in all, the eco-driving support system under investigation is not likely to have a strong negative impact on glance behaviour. However, there is room for improvements. We recommend that eco-driving information is integrated with the speedometer, that optional activation of sound alerts for intermittent information is made available, and that the pop-up duration should be extended to facilitate self-regulation of information intake. (C) 2016 Elsevier Ltd. All rights reserved.

Research on partially automated driving has revealed relevant problems with driving performance, particularly when drivers' intervention is required (e.g., take-over when automation fails). Mental fatigue has commonly been proposed to explain these effects after prolonged automated drives. However, performance problems have also been reported after just a few minutes of automated driving, indicating that other factors may also be involved. We hypothesize that, besides mental fatigue, an underload effect of partial automation may also affect driver attention.

In this study, such potential effect was investigated during short periods of partially automated and manual driving and at different speeds. Subjective measures of mental demand and vigilance and performance to a secondary task (an auditory oddball task) were used to assess driver attention. Additionally, modulations of some specific attention-related event-related potentials (ERPs, N1 and P3 components) were investigated. The mental fatigue effects associated with the time on task were also evaluated by using the same measurements.

Twenty participants drove in a fixed-base simulator while performing an auditory oddball task that elicited the ERPs. Six conditions were presented (5-6 min each) combining three speed levels (low, comfortable and high) and two automation levels (manual and partially automated). The results showed that, when driving partially automated, scores in subjective mental demand and P3 amplitudes were lower than in the manual conditions. Similarly, P3 amplitude and self-reported vigilance levels decreased with the time on task. Based on previous studies, these findings might reflect a reduction in drivers' attention resource allocation, presumably due to the underload effects of partial automation and to the mental fatigue associated with the time on task. Particularly, such underload effects on attention could explain the performance decrements after short periods of automated driving reported in other studies. However, further studies are needed to investigate this relationship in partial automation and in other automation levels.

The use of speech-based interaction over traditional means of interaction in secondary tasks may increase safety in demanding environments with high requirements on operator attention. Speech interfaces have suffered from issues similar to those of visual displays, as they often rely on a complex menu structure that corresponds to that of visual systems. Recent advances in speech technology allow the use of natural language, eliminating the need for menu structures and offering a tighter coupling between the intention to act and the completion of the action. Modern speech technology may not only make already existing types of interaction safer, but also opens up for new applications, which may enhance safety. One such application is a speech-based hazard reporting system. A small fixed-base simulator study showed that drivers adapt the timing of the hazard reports to the situation at hand, such that an increase in reported workload was avoided.

Driver distraction is a contributing factor to many crashes; therefore, a real-time distraction warning system should have the potential to mitigate or circumvent many of these crashes. The objective of this paper is to investigate the usefulness of a real-time distraction detection algorithm called AttenD. The evaluation is based on data from an extended field study comprising seven drivers who drove on an average of 4351 +/- 2181 km in a naturalistic setting. Visual behavior was investigated both on a global scale and on a local scale in the surroundings of each warning. An increase in the percentage of glances at the rear-view mirror and a decrease in the amount of glances at the center console were found. The results also show that visual time sharing decreased in duration from 9.94 to 9.20 s due to the warnings, that the time from fully attentive to warning decreased from 3.20 to 3.03 s, and that the time from warning to full attentiveness decreased from 6.02 to 5.46 s. The limited number of participants does not allow any generalizable conclusions, but a trend toward improved visual behavior could be observed. This is a promising start for further improvements of the algorithm and the warning strategy.