Surrogate in-vehicle information systems and driver behaviour: Effects of visual and cognitive load in simulated rural driving

The underlying aim of HASTE, an EU FP5 project, is the development of a valid, cost-effective and reliable assessment protocol to evaluate the potential distraction of an in-vehicle information system on driving performance. As part of this development, the current study was performed to examine the systematic relationship between primary and secondary task complexity for a specific task modality in a particular driving environment. Two fundamentally distinct secondary tasks (or surrogate in-vehicle information systems, sIVIS) were developed: a visual search task, designed such that it only required visual processing/demand and an auditory continuous memory task, intended to cognitively load drivers without any visual stimulus. A high fidelity, fixed-base driving simulator was used to test 48 participants on a car following task. Virtual traffic scenarios varied in driving demand. Drivers compensated for both types of sIVIS by reducing their speed (this result was more prominent during interaction with the visual task). However, they seemed incapable of fully prioritising the primary driving task over either the visual or cognitive secondary tasks as an increase in sIVIS demand was associated with a reduction in driving performance: drivers showed reduced anticipation of braking requirements and shorter time-to-collision. These results are of potential interest to designers of in-vehicle systems

Is Drivers' Situation Awareness Influenced by a Fully Automated Driving Scenario?

This paper presents results from a study conducted for the European FP6 project CityMobil. The experiment described here is part of four cross-site experiments designed to study the human factors issues associated with various degrees of automated driving. Thirty-nine drivers were asked to drive a simulated route with two zones in a within-subjects design, with a main factor of automation. Driver behaviour in “manual” driving, where all driving manoeuvres and decisions were made by the drivers, was compared to “highly automated” driving, where lateral and longitudinal control of the driving task was dictated by the “automated system”. In this condition, drivers were asked to take their foot off the pedals and their hands off the steering wheel and allow the vehicle to be driven for them. Situation awareness in both driving environments was measured by computing drivers‟ response time to a series of unexpected/critical traffic events. Results showed that drivers‟ response to these events was significantly later in the highly automated condition, implying both reduced situation awareness and perhaps an excessive trust in the automated system

Working memory and auditory localization: demand for central resources impairs performance

Four experiments explored possible roles for working memory in sound localization. In each experiment, the angular error of localization was assessed when performed alone, or concurrently with a working-memory task. The role of the phonological slave systems in auditory localization was ruled out by Experiments 1 and 2, while an engagement of central resources was suggested by the results of Experiment 3. Experiment 4 examined the involvement of visuo-spatial systems in auditory localization and revealed impairment of localization by the concurrent spatial workingmemory task. A comparison of dual-task decrement across all four studies suggests that localization places greater demand on central than on spatial resources

The effect of varying levels of vehicle automation on drivers’ lane changing behaviour

Much of the Human Factors research into vehicle automation has focused on driver responses to critical scenarios where a crash might occur. However, there is less knowledge about the effects of vehicle automation on drivers’ behaviour during non-critical take-over situations, such as driver-initiated lane-changing or overtaking. The current driving simulator study, conducted as part of the EC-funded AdaptIVe project, addresses this issue. It uses a within-subjects design to compare drivers’ lane-changing behaviour in conventional manual driving, partially automated driving (PAD) and conditionally automated driving (CAD). In PAD, drivers were required to re-take control from an automated driving system in order to overtake a slow moving vehicle, while in CAD, the driver used the indicator lever to initiate a system-performed overtaking manoeuvre. Results showed that while drivers’ acceptance of both the PAD and CAD systems was high, they generally preferred CAD. A comparison of overtaking positions showed that drivers initiated overtaking manoeuvres slightly later in PAD than in manual driving or CAD. In addition, when compared to conventional driving, drivers had higher deviations in lane positioning and speed, along with higher lateral accelerations during lane changes following PAD. These results indicate that even in situations which are not time-critical, drivers’ vehicle control after automation is degraded compared to conventional driving

The design of haptic gas pedal feedback to support eco-driving

Previous literature suggests that haptic gas pedals can assist the driver in search of maximum fuel economy. This study investigated three haptic pedal designs, each with high and low intensities of feedback, in a rapid prototyping, paired comparison design. Twenty drivers took part, experiencing the systems in a high-fidelity driving simulator. Results suggested that drivers were best guided towards an “idealized” (most fuel efficient) gas pedal position by force feedback (where a driver feels a step change in gas pedal force) as opposed to stiffness feedback (where a driver feels a changing gas pedal firmness). In either case, high levels of force/stiffness feedback were preferred. Objective performance measures mirrored the subjective results. Whilst the short-term nature (brief system exposure) of this study led to difficulties in drawing longer-term conclusions, it would appear that force feedback haptics are better suited than stiffness feedback to augment an effective driver interface supporting “green” driving

Highly Automated Driving, Secondary Task Performance, and Driver State

Objective: a driving simulator study compared the effect of changes in workload on performance in manual and highly automated driving. Changes in driver state were also observed by examining variations in blink patterns. Background: With the addition of a greater number of advanced driver assistance systems in vehicles, the driver’s role is likely to alter in the future from an operator in manual driving to a supervisor of highly automated cars. Understanding the implications of such advancements on drivers and road safety is important. Method: a total of 50 participants were recruited for this study and drove the simulator in both manual and highly automated mode. As well as comparing the effect of adjustments in driving-related workload on performance, the effect of a secondary Twenty Questions Task was also investigated. Results: in the absence of the secondary task, drivers’ response to critical incidents was similar in manual and highly automated driving conditions. The worst performance was observed when drivers were required to regain control of driving in the automated mode while distracted by the secondary task. Blink frequency patterns were more consistent for manual than automated driving but were generally suppressed during conditions of high workload. Conclusion: highly automated driving did not have a deleterious effect on driver performance, when attention was not diverted to the distracting secondary task. Application: as the number of systems implemented in cars increases, an understanding of the implications of such automation on drivers’ situation awareness, workload, and ability to remain engaged with the driving task is important

Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering

A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework’s main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control

Were they in the loop during automated driving? Links between visual attention and crash potential

Background: A proposed advantage of vehicle automation is that it relieves drivers from the moment-to-moment demands of driving, to engage in other, non-driving related, tasks. However, it is important to gain an understanding of drivers’ capacity to resume manual control, should such a need arise. As automation removes vehicle control-based measures as a performance indicator, other metrics must be explored. Methods: This driving simulator study, conducted under the European Commission (EC) funded AdaptIVe project, assessed drivers’ gaze fixations during partially-automated (SAE Level 2) driving, on approach to critical and non-critical events. Using a between-participant design, 75 drivers experienced automation with one of five out-of-the-loop (OOTL) manipulations, which used different levels of screen visibility and secondary tasks to induce varying levels of engagement with the driving task: 1) no manipulation, 2) manipulation by light fog, 3) manipulation by heavy fog, 4) manipulation by heavy fog plus a visual task, 5) no manipulation plus an n-back task. Results: The OOTL manipulations influenced drivers’ first point of gaze fixation after they were asked to attend to an evolving event. Differences resolved within one second and visual attention allocation adapted with repeated events, yet crash outcome was not different between OOTL manipulation groups. Drivers who crashed in the first critical event showed an erratic pattern of eye fixations towards the road centre on approach to the event, while those who did not demonstrated a more stable pattern. Conclusions: Automated driving systems should be able to direct drivers’ attention to hazards no less than 6 seconds in advance of an adverse outcome

Driver Inattention During Vehicle Automation: How Does Driver Engagement Affect Resumption Of Control?

This driving simulator study, conducted as part of the EC-funded AdaptIVe project, investigated the effect of level of distraction during automation (Level 2 SAE) on drivers’ ability to assess automation uncertainty and react to a potential collision scenario. Drivers’ attention to the road was varied during automation in one of two driving screen manipulation conditions: occlusion by light fog and occlusion by heavy fog. Vehicle-based measures, drivers’ eye movements and response profiles to events after an automation uncertainty period were measured during a highly automated drive containing one of these manipulations, and compared to manual driving. In two of seven uncertainty events, a lead vehicle braked, causing a critical situation. Drivers' reactions to these critical events were compared in a between-subjects design, where the driving scene was manipulated for 1.5 minutes. Results showed that, during automation, drivers’ response profile to a potential collision scenario was less controlled and more aggressive immediately after the transition, compared to when they were in manual control. With respect to screen manipulation in particular, drivers in the heavy fog condition collided with the lead vehicle more often and also had a lower minimum headway compared to those in the light fog condition

Applicability of risky decision-making theory to understand drivers' behaviour during transitions of control in vehicle automation

This work presents a consideration of the applicability of risky decision-making theory models as a tool to understand drivers’ take-over behaviour from vehicle automation, while also incorporating the “Out of the Loop” concept and the process of Situation Awareness Recovery. A methodological discussion is provided, and implications for the processes involved in system design developments are presented. Finally, the paper concludes that the process of evidence accumulation in risky decision-making theory models has strong parallels with the process of Situation Awareness recovery. We argue that evidence accumulation models can be used as a tool to understand what information is used by drivers for achieving safe transitions of control from automation so that this knowledge can be used for a better, and more human-centred design of future in-vehicle interfaces