The Human Factors of Transitions in Highly Automated Driving

The aim of this research was to investigate the nature of the out-of-the-loop (OoTL) phenomenon in highly automated driving (HAD), and its effect on driver behaviour before, during, and after the transition from automated to manual control. The work addressed questions relating to how automation affects drivers' (i) performance in transition situations requiring control- and tactical-level responses, (ii) their behaviour in automation compared to in manual driving, (iii-iv) their visual attention distribution before and during the transition, as well as (v) their perceptual-motor performance after resuming control. A series of experiments were developed to take drivers progressively further OoTL for short periods during HAD, by varying drivers' secondary task engagement and the amount of visual information from the system and environment available to them. Once the manipulations ended, drivers were invited to determine a need to resume control in critical and non-critical vehicle following situations. Results showed that, overall, drivers looked around more during HAD, compared to manual driving, and had poorer vehicle control in critical transition situations. Generally, the further OoTL drivers were during HAD, the more dispersed their visual attention. However, within three seconds of the manipulations ending, the differences between the conditions resolved, and in many cases, this was before drivers resumed control. Differences between the OoTL manipulations emerged once again in terms of the timing of drivers' initial response (take-over time) in critical events, where the further OoTL drivers were the longer it took them to resume control, but there was no difference in the quality of the subsequent vehicle control. Results suggest that any information presented to drivers during automation should be placed near the centre of the road and that kinematically early avoidance response may be more important for safety than short take-over times. This thesis concludes with a general conceptualisation of the relationship between a number of driver and vehicle/environment factors that influence driver performance in the transition

Why would people want to travel more with automated cars?

The use of automated vehicles (AVs) may enable drivers to focus on non-driving related activities while travelling and reduce the unwanted efforts of the driving task. This is expected to make using a car more attractive, or at least less unpleasant compared to manually driven vehicles. Consequently, the number and length of car trips may increase. The aim of this study was to identify the main contributors to travelling more by AV. We analysed the L3Pilot project’s pilot site questionnaire data from 359 respondents who had ridden in a conditionally automated car (SAE level 3) either as a driver or as a passenger. The questionnaire queried the respondents’ user experience with the automated driving function, current barriers of travelling by car, previous experience with advanced driving assistance systems, and general priorities in travelling. The answers to these questions were used to predict willingness to travel more or longer trips by AV, and to use AVs on currently undertaken trips. The most predictive subset of variables was identified using Bayesian cumulative ordinal regression with a shrinkage prior (regularised horseshoe). The current study found that conditionally automated cars have a substantial potential to increase travelling by car once they become available. Willingness to perform leisure activities during automated driving, experienced usefulness of the system, and unmet travel needs, which AVs could address by making travelling easier, were the main contributors to expecting to travel more by AV. For using AVs on current trips, leisure activities, trust in AVs, satisfaction with the system, and traffic jams as barriers to current car use were important contributors. In other words, perceived usefulness motivated travelling more by AV and using AVs on current trips, but also other factors were important for using them on current trips. This suggests that one way to limit the growth of traffic with private AVs could be to address currently unmet travel needs with alternative, more sustainable travel modes

Are multimodal travellers going to abandon sustainable travel for L3 automated vehicles?

Reducing car dependency supports the creation of a more sustainable transport system. However, automated vehicles (AVs) are predicted to increase the attractiveness of car travel and decrease the use of public transport and active travel. This current study explored how travellers’ intention to use AVs and their current travel behaviour influence their expectations of how they will use public transport and active travel, once conditionally automated (SAE L3) vehicles (L3 AVs) are available.Survey data (collected during the EU H2020 L3Pilot project) from among current car users from eight European countries (n = 9118) was used. Respondents were asked about their current travel mode usage, intention to use L3 AVs, and expected changes in the use of public transport and active travel once L3 AVs are available. The respondents were divided into nine user segments based on their level of intention to use L3 AVs and multimodality.Most respondents did not foresee changes in their use of public transport (62%) or active travel (67%). A higher intention to use L3 AVs increased the probability of a traveller expecting to decrease their use of public transport and, to a lesser extent, active travel. Multimodal travellers used public transport and active travel regularly and were also more likely to see a change, either up or down, in their use of public transport and active travel. The results suggest that L3 AVs may pose a challenge to the sustainability by encouraging current users of public transport and active travel to switch to personal AVs

Using pupillometry and gaze-based metrics for understanding drivers’ mental workload during automated driving

This Horizon2020-funded driving simulator-based study on automated driving investigated the effect of different car-following scenarios, and takeover situations, on drivers’ mental workload, as measured by eye tracking-based metrics of pupil diameter and self-reported workload ratings. This study incorporated a mixed design format, with 16 drivers recruited for the SAE Level 2 (L2; SAE International, 2021) automation group, who were asked to monitor the driving and road environment during automation, and 16 drivers in the Level 3 (L3) automation group, who engaged in a non-driving related task (NDRT; Arrows task) during automation. Drivers in each group undertook two experimental drives, lasting about 18 min each. To manipulate perceived workload, difficulty of the driving task was controlled by incorporating a lead vehicle which maintained either a Short (0.5 s) or Long (1.5 s) Time Headway (THW) condition during automated car-following (ACF). Each ACF session was followed by a subsequent request to takeover, which happened either in the presence or absence of a lead vehicle. Results from standard deviation of pupil diameter values indicated that drivers’ mental workload levels fluctuated significantly more when monitoring the drive during L2 ACF, compared to manual car-following (MCF). Additionally, we found that drivers’ mental workload, as indicated by their mean pupil diameter, increased steeply around takeovers, and was further exacerbated by the presence of a lead vehicle during the takeovers, especially in the Short THW condition, for both groups. Pupil diameter was found to be sensitive to subtle variations in mental workload, and closely resembled the trend seen in self-reported workload ratings. Further research is warranted to assess the feasibility of using eye-tracking-based metrics along with other physiological sensors, especially in real-world settings, to understand whether they can be used as real-time indicators of drivers’ mental workload, in future driver state monitoring systems

Engaging in NDRTs affects drivers’ responses and glance patterns after silent automation failures

The aim of this study was to understand driver responses to “silent” failures in automated driving, where automation failed during a simulator drive, without a take-over warning. The effect of a visual non-driving related task (NDRT) and a road-based vigilance task presented drivers’ take-over response and visual attention was also investigated. Currently, automated driving systems face a number of limitations that require control to be handed back to the driver. Much of the research to date has focused on explicit take-over requests (ToRs) and shows that drivers struggle to resume control safely, exacerbated by disengagement from the driving task, for instance, due to the presence of NDRTs. However, little is known about whether, and how, drivers will respond to more subtle automation failures that come without a warning, and how this is affected by NDRT engagement. Thirty participants drove a simulated automated drive in two conditions, which had 6 silent automation failures each (3 on a Curve, 3 in a Straight), with no ToRs. In one condition, drivers were required to constantly monitor the road, which was enforced by a road-based vigilance task (VMS Only). In the other, drivers performed an additional visual NDRT, requiring them to divide their attention (VMS + Arrows). Results showed that, in both conditions, all drivers eventually detected and responded to all silent automation failures. However, engaging in an additional NDRT during automation resulted in significantly more lane excursions and longer take-over times. Adding a visual NDRT not only changed the distribution of drivers’ visual attention before and after the failure but also how they divided their attention between information on the road environment and the human–machine interface, which provided information on automation status. These results provide support for how driver monitoring systems may be used to detect drivers’ visual attention to the driving task and surroundings, and used as a tool for encouraging driver intervention, when required

What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of Automated Road Transport Systems

The main aim of this study was to use an adapted version of the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate the factors that influence users’ acceptance of automated road transport systems (ARTS). A questionnaire survey was administered to 315 users of a CityMobil2 ARTS demonstration in the city of Trikala, Greece. Results provide evidence of the usefulness of the UTAUT framework for increasing our understanding of how public acceptance of these automated vehicles might be maximised. Hedonic Motivation, or users’ enjoyment of the system, had a strong impact on Behavioural Intentions to use ARTS in the future, with Performance Expectancy, Social Influence and Facilitating Conditions also having significant effects. The anticipated effect of Effort Expectancy did not emerge from this study, suggesting that the level of effort required is unlikely to be a critical factor in consumers’ decisions about using ARTS. Based on these results, a number of modifications to UTAUT are suggested for future applications in the context of automated transport. It is recommended that designers and developers should consider the above issues when implementing more permanent versions of automated public transport

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

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

Drivers’ acceptance ratings for PAD and CAD.

<p>Error bars refer to SEM.</p