Gaze entropy metrics for mental workload estimation are heterogenous during hands-off level 2 automation.

As the level of vehicle automation increases, drivers are more likely to engage in non-driving related tasks which take their hands, eyes, and/or mind away from the driving task. Consequently, there has been increased interest in creating Driver Monitoring Systems (DMS) that are valid and reliable for detecting elements of driver state. Workload is one element of driver state that has remained elusive within the literature. Whilst there has been promising work in estimating mental workload using gaze-based metrics, the literature has placed too much emphasis on point estimate differences. Whilst these are useful for establishing whether effects exist, they ignore the inherent variability within individuals and between different drivers. The current work builds on this by using a Bayesian distributional modelling approach to quantify the within and between participants variability in Information Theoretical gaze metrics. Drivers (N = 38) undertook two experimental drives in hands-off Level 2 automation with their hands and feet away from operational controls. During both drives, their priority was to monitor the road before a critical takeover. During one drive participants had to complete a secondary cognitive task (2-back) during the hands-off Level 2 automation. Changes in Stationary Gaze Entropy and Gaze Transition Entropy were assessed for conditions with and without the 2-back to investigate whether consistent differences between workload conditions could be found across the sample. Stationary Gaze Entropy proved a reliable indicator of mental workload; 92 % of the population were predicted to show a decrease when completing 2-back during hands-off Level 2 automated driving. Conversely, Gaze Transition Entropy showed substantial heterogeneity; only 66 % of the population were predicted to have similar decreases. Furthermore, age was a strong predictor of the heterogeneity of the average causal effect that high mental workload had on eye movements. These results indicate that, whilst certain elements of Information Theoretic metrics can be used to estimate mental workload by DMS, future research needs to focus on the heterogeneity of these processes. Understanding this heterogeneity has important implications toward the design of future DMS and thus the safety of drivers using automated vehicle functions. It must be ensured that metrics used to detect mental workload are valid (accurately detecting a particular driver state) as well as reliable (consistently detecting this driver state across a population).

Deceleration parameters as implicit communication signals for pedestrians' crossing decisions and estimations of automated vehicle behaviour.

Society greatly expects the widespread deployment of automated vehicles (AVs). However, the absence of a driver role results in unresolved communication issues between pedestrians and AVs. Research has shown the crucial role of implicit communication signals in this context. Nonetheless, it remains unclear how pedestrians subjectively estimate vehicle behaviour and whether they incorporate these estimations as part of their crossing decisions. For the first time, this study explores the impact of implicit communication signals on pedestrians' subjective estimations of approaching vehicle behaviour across a wide range of experimental traffic scenarios and on their crossing decisions in the same scenarios through a comprehensive analysis. Two simulator tasks, namely a natural road crossing task and a vehicle behaviour estimation task, were designed with controlled time to collision, vehicle speed, and deceleration behaviour. A novel finding is that the correlation between crossing decisions and vehicle behaviour estimations depends on the traffic scenario. Pedestrians' recognition of different deceleration behaviour aligned with their crossing decisions, supporting the notion that they actively estimate vehicle behaviour as part of their decision-making process. However, if the traffic gap was long enough, the effects of vehicle speed were the opposite between crossing decisions and estimations, suggesting that vehicle behaviour estimation may not directly impact crossing decisions when the time gap to the vehicle is large. We also found that pedestrians crossed the street earlier and estimated yielding behaviour more accurately in early-onset braking scenarios than in late-onset braking scenarios. Interestingly, vehicle speed significantly affected pedestrians' estimations, with pedestrians tending to perceive low vehicle speed as yielding behaviour regardless of whether the vehicle yielded. Finally, we demonstrated that visual cue τ̇ is a practical indicator for controlling the vehicle deceleration evidence in the experiment. In conclusion, these findings reveal in detail the role of deceleration parameters as implicit communication signals between pedestrians and AVs, with implications for road crossing safety and the development of AVs.

Explaining human interactions on the road by large-scale integration of computational psychological theory.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

Who goes first? A distributed simulator study of vehicle-pedestrian interaction.

One of the current challenges of automation is to have highly automated vehicles (HAVs) that communicate effectively with pedestrians and react to changes in pedestrian behaviour, to promote more trustable HAVs. However, the details of how human drivers and pedestrians interact at unsignalised crossings remain poorly understood. We addressed some aspects of this challenge by replicating vehicle-pedestrian interactions in a safe and controlled virtual environment by connecting a high fidelity motion-based driving simulator to a CAVE-based pedestrian lab in which 64 participants (32 pairs of one driver and one pedestrian) interacted with each other under different scenarios. The controlled setting helped us study the causal role of kinematics and priority rules on interaction outcome and behaviour, something that is not possible in naturalistic studies. We also found that kinematic cues played a stronger role than psychological traits like sensation seeking and social value orientation in determining whether the pedestrian or driver passed first at unmarked crossings. One main contribution of this study is our experimental paradigm, which permitted repeated observation of crossing interactions by each driver-pedestrian participant pair, yielding behaviours which were qualitatively in line with observations from naturalistic studies.

Drivers' gaze patterns when resuming control with a head-up-display: Effects of automation level and time budget.

The removal of drivers' active engagement in driving tasks can lead to erratic gaze patterns in SAE Level 2 (L2) and Level 3 (L3) automation, which has been linked to their subsequential degraded take-over performance. To further address how changes in gaze patterns evolve during the take-over phase, and whether they are influenced by the take-over urgency and the location of the human-machine interface, this driving simulator study used a head-up display (HUD) to relay information about the automation status and conducted take-over driving experiments where the ego car was about to exit the highway with variations in the automation level (L2, L3) and time budget (2 s, 6 s). In L2 automation, drivers were required to monitor the environment, while in L3, they were engaged with a visual non-driving related task. Manual driving was also embodied in the experiments as the baseline. Results showed that, compared to manual driving, drivers in L2 automation focused more on the HUD and Far Road (roadway beyond 2 s time headway ahead), and less on the Near Road (roadway within 2 s time headway ahead); while in L3, drivers' attention was predominantly allocated on the non-driving related task. After receiving take-over requests (TORs), there was a gradual diversion of attention from the Far Road to the Near Road in L2 take-overs. This trend changed nearly in proportion to the time within the time budget and it exaggerated given a shorter time budget of 2 s. While in L3, drivers' gaze distribution was similar in the early stage of take-overs for both time budget conditions (2 s vs. 6 s), where they prioritized their early glances to Near Road with a gradual increase in attention towards Far Road. The HUD used in the present study showed the potential to maintain drivers' attention around the road center during automation and to encourage drivers to glance the road earlier after TORs by reducing glances to the instrument cluster, which might be of significance to take-over safety. These findings were discussed based on an extended conceptual gaze control model, which advances our understanding of gaze patterns around control transitions and their underlying gaze control causations. Implications can be contributed to the design of autonomous vehicles to facilitate the transition of control by guiding drivers' attention appropriately according to drivers' attentional state and the take-over urgency.

Drivers' Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural?

OBJECTIVE: This study investigated users' subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. BACKGROUND: Comfort and naturalness play an important role in contributing to users' acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. METHOD: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity. RESULTS: Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. CONCLUSION: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. APPLICATION: Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems.

Impacts of visual and cognitive distractions and time pressure on pedestrian crossing behaviour: A simulator study.

Distractions have been recognised as one important factor associated with pedestrian injuries, as the increasing use of cell phones and personal devices. However, the situation is less clear regarding the differences in the effects of visual-manual and auditory-cognitive distractions. Here, we investigated distracted pedestrians in a one-lane road with continuous traffic using an immersive CAVE-based simulator. Sixty participants were recruited to complete a crossing task and perform one of two distractions, a visual-manual task and an auditory-cognitive task. Moreover, normal and time pressure crossing conditions were included as a baseline and comparison. For the first time, this study directly compared the impacts of visual-manual, auditory-cognitive distractions, and time pressure on pedestrian crossing behaviour and safety in a controlled environment. The results indicated that although pedestrian safety was compromised under both types of distraction, the effects of the applied distractions were different. When engaged in the visual-manual distraction, participants crossed the road slowly, but there was no significant difference in gap acceptance or initiation time compared to baseline. In contrast, participants walked slowly, crossed earlier, and accepted smaller gaps when performing the auditory-cognitive distraction. This has interesting parallels to existing findings on how these two types of distractions affect driver performance. Moreover, the effects of the visual-manual distraction were found to be dynamic, as these effects were affected by the gap size. Finally, compared to baseline, time pressure resulted in participants accepting smaller time gaps with shorter initiation times and crossing durations, leading to an increase in unsafe decisions and a decrease in near-collisions. These results provide new evidence that two types of distraction and time pressure impair pedestrian safety, but in different ways. Our findings may provide insights for further studies involving pedestrians with different distraction components.

The effect of information from dash-based human-machine interfaces on drivers' gaze patterns and lane-change manoeuvres after conditionally automated driving.

The goal of this paper was to measure the effect of Human-Machine Interface (HMI) information and guidance on drivers' gaze and takeover behaviour during transitions of control from automation. The motivation for this study came from a gap in the literature, where previous research reports improved performance of drivers' takeover based on HMI information, without considering its effect on drivers' visual attention distribution, and how drivers also use the information available in the environment to guide their response. This driving simulator study investigated drivers' lane-changing behaviour after resumption of control from automation. Different levels of information were provided on a dash-based HMI, prior to each lane change, to investigate how drivers distribute their attention between the surrounding environment and the HMI. The difficulty of the lane change was also manipulated by controlling the position of approaching vehicles in drivers' offside lane. Results indicated that drivers' decision-making time was sensitive to the presence of nearby vehicles in the offside lane, but not directly influenced by the information on the HMI. In terms of gaze behaviour, the closer the position of vehicles in the offside lane, the longer drivers looked in that direction. Drivers looked more at the HMI, and less towards the road centre, when the HMI presented information about automation status, and included an advisory message indicating it was safe to change lane. Machine learning techniques showed a strong relationship between drivers' gaze to the information presented on the HMI, and decision-making time (DMT). These results contribute to our understanding of HMI design for automated vehicles, by demonstrating the attentional costs of an overly-informative HMI, and that drivers still rely on environmental information to perform a lane-change, even when the same information can be acquired by the HMI of the vehicle.

Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrian' crossing behavior.

INTRODUCTION: In current urban traffic, pedestrians attempting to cross the road at un-signalized locations are thought to mostly use implicit communication, such as deceleration cues, to interpret a vehicle's intention to yield. There is less reliance on explicit driver- or vehicle-based messages, such as hand/head movements, or flashing lights/beeping horns. With the impending deployment of Automated Vehicles (AV), especially those at SAE Level 4 and 5, where the driver is no longer in control of the vehicle, there has been a surge in interest in the value of new forms of communication for AVs, for example, via different types of external Human Machine Interfaces (eHMIs). However, there is still much to be understood about how quickly a novel eHMI affects pedestrian crossing decisions, and whether it provides any additional aid, above and beyond implicit/kinematic information from the vehicle. The aim of this between-participant study, funded by the H2020 interACT project, was to investigate how the combination of kinematic information from a vehicle (e.g., Speed and Deceleration), and eHMI designs, play a role in assisting the crossing decision of pedestrians in a cave-based pedestrian simulator. METHOD: Using an existing, well-recognized, message for yielding (Flashing Headlights - FH) as a benchmark, this study also investigated how quickly a novel eHMI (Slow Pulsing Light Band - SPLB) was learned. To investigate the effect of eHMI visibility on crossing decisions, the distance at which each eHMI was perceivable was also measured. RESULTS: Results showed that, compared to SPLB, the FH led to earlier crossings during vehicle deceleration, especially at lower approaching speeds, and smaller time gaps. However, although FH was visible earlier than SPLB, this visibility does not appear to be the only reason for earlier crossings, with message familiarity thought to play a role. Participants were found to learn the meaning conveyed by FH relatively quickly, crossing around 1 second earlier in its presence (compared to the no eHMI condition), across the three blocks of trials. On the other hand, it took participants at least one block of 12 trials for the new SPLB signal to affect crossing, which only accelerated crossing initiations by around 200 ms, compared to the no eHMI condition. The role of comprehension, long-term exposure, and familiarity of novel messages in this context is therefore important, if AVs are to provide safe, trustworthy communication messages, which will enhance traffic flow and efficiency.

External Human-Machine Interfaces Can Be Misleading: An Examination of Trust Development and Misuse in a CAVE-Based Pedestrian Simulation Environment.

OBJECTIVE: To investigate pedestrians' misuse of an automated vehicle (AV) equipped with an external human-machine interface (eHMI). Misuse occurs when a pedestrian enters the road because of uncritically following the eHMI's message. BACKGROUND: Human factors research indicates that automation misuse is a concern. However, there is no consensus regarding misuse of eHMIs. METHODS: Sixty participants each experienced 50 crossing trials in a Cave Automatic Virtual Environment (CAVE) simulator. The three independent variables were as follows: (1) behavior of the approaching AV (within-subject: yielding at 33 or 43 m distance, no yielding), (2) eHMI presence (within-subject: eHMI on upon yielding, off), and (3) eHMI onset timing (between-subjects: eHMI turned on 1 s before or 1 s after the vehicle started to decelerate). Two failure trials were included where the eHMI turned on, yet the AV did not yield. Dependent measures were the moment of entering the road and perceived risk, comprehension, and trust. RESULTS: Trust was higher with eHMI than without, and the -1 Group crossed earlier than the +1 Group. In the failure trials, perceived risk increased to high levels, whereas trust and comprehension decreased. Thirty-five percent of the participants in the -1 and +1 Groups walked onto the road when the eHMI failed for the first time, but there were no significant differences between the two groups. CONCLUSION: eHMIs that provide anticipatory information stimulate early crossing. eHMIs may cause people to over-rely on the eHMI and under-rely on the vehicle-intrinsic cues. APPLICATION: eHMI have adverse consequences, and education of eHMI capability is required.

Drivers' Intentions to Use Different Functionalities of Conditionally Automated Cars: A Survey Study of 18,631 Drivers from 17 Countries.

A number of studies have investigated the acceptance of conditionally automated cars (CACs). However, in the future, CACs will comprise of several separate Automated Driving Functions (ADFs), which will allow the vehicle to operate in different Operational Design Domains (ODDs). Driving in different environments places differing demands on drivers. Yet, little research has focused on drivers' intention to use different functions, and how this may vary by their age, gender, country of residence, and previous experience with Advanced Driving Assistance Systems (ADAS). Data from an online survey of 18,631 car drivers from 17 countries (8 European) was used in this study to investigate intention to use an ADF in one of four different ODDs: Motorways, Traffic Jams, Urban Roads, and Parking. Intention to use was high across all ADFs, but significantly higher for Parking than all others. Overall, intention to use was highest amongst respondents who were younger (<39), male, and had previous experience with ADAS. However, these trends varied widely across countries, and for the different ADFs. Respondents from countries with the lowest Gross Domestic Product (GDP) and highest road death rates had the highest intention to use all ADFs, while the opposite was found for countries with high GDP and low road death rates. These results suggest that development and deployment strategies for CACs may need to be tailored to different markets, to ensure uptake and safe use.

Predicting takeover response to silent automated vehicle failures.

Current and foreseeable automated vehicles are not able to respond appropriately in all circumstances and require human monitoring. An experimental examination of steering automation failure shows that response latency, variability and corrective manoeuvring systematically depend on failure severity and the cognitive load of the driver. The results are formalised into a probabilistic predictive model of response latencies that accounts for failure severity, cognitive load and variability within and between drivers. The model predicts high rates of unsafe outcomes in plausible automation failure scenarios. These findings underline that understanding variability in failure responses is crucial for understanding outcomes in automation failures.

The effect of motor control requirements on drivers' eye-gaze pattern during automated driving.

This driving simulator study compared drivers' eye movements during a series of lane-changes, which required different levels of motor control for their execution. Participants completed 12 lane-changing manoeuvres in three drives, categorised by degree of manual engagement with the driving task: Fully Manual Drive, Manual Intervention Required, Fully Automated Drive (Manual drive, Partial automation, Full automation). For Partial automation, drivers resumed control from the automated system and changed lane manually. For Full automation, the automated system managed the lane change, but participants initiated the manoeuvre by pulling the indicator lever. Results were compared to the Manual drive condition, where drivers controlled the vehicle at all times. For each driving condition, lane changing was initiated by drivers, at their discretion, in response to a slow-moving lead vehicle, which entered their lane. Failure to change lane did not result in a collision. To understand how different motor control requirements affected driver visual attention, eye movements to the road centre, and drivers' vertical and horizontal gaze dispersion were compared during different stages of the lane change manoeuvre, for the three drives. Results showed that drivers' attention to the road centre was generally lower for drives with less motor control requirements, especially when they were not engaged in the lane change process. However, as drivers moved closer to the lead vehicle, and prepared to change lane, the pattern of eye movements to the road centre converged, regardless of whether drivers were responsible for the manual control of the lane change. While there were no significant differences in horizontal gaze dispersion between the three drives, vertical dispersion for the two levels of automation was quite different, with higher dispersion during Partial automation, which was due to a higher reliance on the HMI placed in the centre console.

The Effects of Fatigue on Cognitive Performance in Police Officers and Staff During a Forward Rotating Shift Pattern.

BACKGROUND: Few studies have examined the effects of a forward rotating shift pattern on police employee performance and well-being. This study sought to compare sleep duration, cognitive performance, and vigilance at the start and end of each shift within a three-shift, forward rotating shift pattern, common in United Kingdom police forces. METHODS: Twenty-three police employee participants were recruited from North Yorkshire Police (mean age, 43 years). The participants were all working the same, 10-day, forward rotating shift pattern. No other exclusion criteria were stipulated. Sleep data were gathered using both actigraphy and self-reported methods; cognitive performance and vigilance were assessed using a customized test battery, comprising five tests: motor praxis task, visual object learning task, NBACK, digital symbol substitution task, and psychomotor vigilance test. Statistical comparisons were conducted, taking into account the shift type, shift number, and the start and end of each shift worked. RESULTS: Sleep duration was found to be significantly reduced after night shifts. Results showed a significant main effect of shift type in the visual object learning task and NBACK task and also a significant main effect of start/end in the digital symbol substitution task, along with a number of significant interactions. CONCLUSION: The results of the tests indicated that learning and practice effects may have an effect on results of some of the tests. However, it is also possible that due to the fast rotating nature of the shift pattern, participants did not adjust to any particular shift; hence, their performance in the cognitive and vigilance tests did not suffer significantly as a result of this particular shift pattern.

Steering bends and changing lanes: The impact of optic flow and road edges on two point steering control.

Successful driving involves steering corrections that respond to immediate positional errors while also anticipating upcoming changes to the road layout ahead. In popular steering models these tasks are often treated as separate functions using two points: the near region for correcting current errors, and the far region for anticipating future steering requirements. Whereas two-point control models can capture many aspects of driver behavior, the nature of perceptual inputs to these two "points" remains unclear. Inspired by experiments that solely focused on road-edge information (Land & Horwood, 1995), two-point models have tended to ignore the role of optic flow during steering control. There is recent evidence demonstrating that optic flow should be considered within two-point control steering models (Mole, Kountouriotis, Billington, & Wilkie, 2016). To examine the impact of optic flow and road edges on two-point steering control we used a driving simulator to selectively and systematically manipulate these components. We removed flow and/or road-edge information from near or far regions of the scene, and examined how behaviors changed when steering along roads where the utility of far-road information varied. While steering behaviors were strongly influenced by the road-edges, there were also clear contributions of optic flow to steering responses. The patterns of steering were not consistent with optic flow simply feeding into two-point control; rather, the global optic flow field appeared to support effective steering responses across the time-course of each trajectory.

Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming.

Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ-1, the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ-1 as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ-1.

What externally presented information do VRUs require when interacting with fully Automated Road Transport Systems in shared space?

As the desire for deploying automated ("driverless") vehicles increases, there is a need to understand how they might communicate with other road users in a mixed traffic, urban, setting. In the absence of an active and responsible human controller in the driving seat, who might currently communicate with other road users in uncertain/conflicting situations, in the future, understanding a driverless car's behaviour and intentions will need to be relayed via easily comprehensible, intuitive and universally intelligible means, perhaps presented externally via new vehicle interfaces. This paper reports on the results of a questionnaire-based study, delivered to 664 participants, recruited during live demonstrations of an Automated Road Transport Systems (ARTS; SAE Level 4), in three European cities. The questionnaire sought the views of pedestrians and cyclists, focussing on whether respondents felt safe interacting with ARTS in shared space, and also what externally presented travel behaviour information from the ARTS was important to them. Results showed that most pedestrians felt safer when the ARTS were travelling in designated lanes, rather than in shared space, and the majority believed they had priority over the ARTS, in the absence of such infrastructure. Regardless of lane demarcations, all respondents highlighted the importance of receiving some communication information about the behaviour of the ARTS, with acknowledgement of their detection by the vehicle being the most important message. There were no clear patterns across the respondents, regarding preference of modality for these external messages, with cultural and infrastructural differences thought to govern responses. Generally, however, conventional signals (lights and beeps) were preferred to text-based messages and spoken words. The results suggest that until these driverless vehicles are able to provide universally comprehensible externally presented information or messages during interaction with other road users, they are likely to contribute to confusing and conflicting interactions between these actors, especially in a shared space setting, which may, therefore, reduce efficient traffic flow.

Is improved lane keeping during cognitive load caused by increased physical arousal or gaze concentration toward the road center?

Driver distraction is one of the main causes of motor-vehicle accidents. However, the impact on traffic safety of tasks that impose cognitive (non-visual) distraction remains debated. One particularly intriguing finding is that cognitive load seems to improve lane keeping performance, most often quantified as reduced standard deviation of lateral position (SDLP). The main competing hypotheses, supported by current empirical evidence, suggest that cognitive load improves lane keeping via either increased physical arousal, or higher gaze concentration toward the road center, but views are mixed regarding if, and how, these possible mediators influence lane keeping performance. Hence, a simulator study was conducted, with participants driving on a straight city road section whilst completing a cognitive task at different levels of difficulty. In line with previous studies, cognitive load led to increased physical arousal, higher gaze concentration toward the road center, and higher levels of micro-steering activity, accompanied by improved lane keeping performance. More importantly, during the high cognitive task, both physical arousal and gaze concentration changed earlier in time than micro-steering activity, which in turn changed earlier than lane keeping performance. In addition, our results did not show a significant correlation between gaze concentration and physical arousal on the level of individual task recordings. Based on these findings, various multilevel models for micro-steering activity and lane keeping performance were conducted and compared, and the results suggest that all of the mechanisms proposed by existing hypotheses could be simultaneously involved. In other words, it is suggested that cognitive load leads to: (i) an increase in arousal, causing increased micro-steering activity, which in turn improves lane keeping performance, and (ii) an increase in gaze concentration, causing lane keeping improvement through both (a) further increased micro-steering activity and (b) a tendency to steer toward the gaze target.

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.