The determinants of Portuguese preference for vehicle automation: A descriptive and explanatory study

During the last century, innovation of automated vehicles (AVs) technologies are successively maturing while progressively excluding the human intervention in vehicle driving. The objective of this paper was to analyze the determinants of Portuguese drivers’ decision to adopt AVs technologies, in an under explored context, where the driver of contemporary vehicles does all or part of the dynamic driving task (DDT) in comparison to vehicles equipped with Automated Driving systems (ADS) where the driver can become a passenger temporarily or permanently. In addition, willingness-to-pay for ADS estimates were also investigated. This study data was collected through a survey designed and deployed in Portugal. A mixed logit model was estimated, and the results obtained are in line with the literature of AVs in a number of determinants, but also highlights differences that can be explained by the Portuguese cultural, social and economic context. Overall, 83.7% of the Portuguese drivers favor contemporary vehicles, today, and among those who prefer vehicles with ADS, highly educated drivers’ are willing to pay, on average, 65,671 € for Conditional AVs, 31,185 € for Highly AVs, and about 28,622 € for Full AVs.     

Challenges for automated vehicle driver training: A thematic analysis from manual and automated driving

Considerable research and resources are going into the development and testing of Automated Vehicles. They are expected to bring society a huge number of benefits (such as: improved safety, increased capacity, reduced fuel use and emissions). Notwithstanding these potential benefits, there have also been a number of high-profile collisions involving Automated Vehicles on the road. In the majority of these cases, the driver’s inattention to the vehicle and road environment was blamed as a significant causal factor. This suggests that solutions need to be developed in order to enhance the benefits and address the challenges associated with Automated Vehicles. One such solution is driver training. As drivers still require manual driving skills when operating Automated Vehicles on the road, this paper applied the grounded theory approach to identify eight “key” themes and interconnections that exist in current manual vehicle driver training. These themes were then applied to the limited literature available on Automated Vehicle driver training, and a ninth theme of trust emerged. This helped to identify a set of training requirements for drivers of Automated Vehicles, which suggests that a multifaceted approach (covering all nine themes and manual and Automated Vehicle driving skills) to driver training is required. This framework can be used to develop and test a training programme for drivers of Automated Vehicles.     

Do drivers accept cooperative behavior of their automated vehicle on highways?

Automated cooperatively interacting vehicles will change the future of traffic completely. Such vehicles will be capable of planning and carrying out maneuvers based on vehicle-to-vehicle and vehicle-to-infrastructure communication, enabling a safer driving experience. However, this gain of safety will only be effective if drivers use and accept the decisions made by advanced automated technology. Especially when drivers are cognitively distracted, new strategies might be necessary, e.g., by further explaining the reason for a cooperative decision.In a driving simulator study, we investigated the acceptance of lane change maneuvers in cooperative situations carried out by an automated vehicle on a two-lane German highway. When the automated system detected a potential lane change ahead, it carried out one of three possible maneuvers: accelerate, decelerate, or maintain speed. Participants (N = 20) were asked whether they accepted the system’s behavior either while being cognitively distracted or in an attentive state. Thus, we investigated the influence of a cognitively demanding secondary task and, in addition, further situational characteristics (Scope of action, Criticality for the lane-changing vehicle, Display of intention) on the acceptance towards the system’s behavior. Moreover, participants had to rate the perceived situation’s criticality.Results underlined the importance of explicit indication of the intention to change lanes. Furthermore, increased cognitive load led to a higher acceptance of the defensive system behavior. This study contributes to the development of a user-centered interface and interaction strategy for cooperative interacting vehicles.     

What and how to tell beforehand: The effect of user education on understanding,interaction and satisfaction with driving automation

The success of introducing automated driving systems to consumers will depend on an appropriate understanding and human-automation interaction with this technology. Educating users on driving automation technology bears the potential to attain these two requirements. In a driving simulator study, we investigated the effects of user education on mental models, human-automation interaction performance (i.e., time on task, error rate, experimenter rating) and satisfaction with a Human-Machine Interface (HMI) for automated driving. N = 80 participants were randomly assigned to one of three different user education conditions or to a baseline. Subsequently, they completed several driver-initiated control transitions between manual, Level 2 (L2), and Level 3 (L3) automated driving. The results revealed that user education promoted an accurate evolution of mental models for driving automation. These, in turn, facilitated interaction performance in transitions from manual to both L2 and L3 automated driving. There was no comparable influence of prior education on performance in transitions between the automation levels. Due to the performance enhancing effects of user education, no further improvements of interaction performance were observed for educated users in comparison to uneducated users. There was no effect of user education on satisfaction. The current findings emphasize the necessity to provide information about automated vehicle HMIs to first-time users to support accurate understanding and behavior. Based on the current findings, we propose conceptual approaches to teach users and derive implications for user studies on automated vehicle HMIs.     

User acceptance of automated public transport: Valence of an autonomous minibus experience

Autonomous driving is receiving increasing attention in the automotive industry as well as in public transport. However, it is still unclear whether users are willing to use automated public transportation at all. In order to answer this and other questions, the transport company of the city of Mainz, Germany, tested the autonomous minibus EMMA (Elektro-Mobilität Mainz Autonom) on a 600-meter-long test track in public space. The study presented here was conducted with the aim of exploring crucial determinants for the use of an autonomous minibus. On the basis of established acceptance models, a questionnaire was developed, which was completed in a field survey by a total of 942 participants before or after their journey with the minibus. Autonomous vehicles in public transport in general and the minibus in particular were evaluated positively by the majority of respondents. Above all, participants judged safety and environmental friendliness of the minibus as important. Participants who completed the questionnaire after their first trip with EMMA provided higher ratings of acceptance than those who had not travelled on the bus. Performance expectancy was the most important predictor for both acceptance of automated public transport in general and acceptance of the minibus EMMA. However, the experienced valence of the ride, in terms of how pleasant or unpleasant passengers experienced the first trip with the minibus, also affected acceptance of the minibus. This suggests a role of valence on intention-to-use, which has hardly been considered in previous theories and studies.     

Behavioral changes to repeated takeovers in automated driving: The drivers’ ability to transfer knowledge and the effects of takeover request process

Only a couple of studies evaluated whether drivers of automated vehicles change their takeover behavior when they experience takeover requests repeatedly. Even less evidence was accumulated regarding the question whether drivers are able to transfer learned behavior to takeover situations with varying visibility characteristics and whether drivers’ takeover behavior depends on the takeover process in these situations. This paper therefore examines three research questions. First, it assesses how drivers change their behavior with the repeated experience of a takeover situation with the same visibility (fog or no fog). Second, it tests whether drivers can transfer their learned takeover behavior from a takeover situation with high or low visibility to the same takeover situation with different visibility conditions. Third, it assesses whether drivers’ takeover behavior and their experience of the situation differ between a one-step and a two-step takeover request process. Forty participants experienced a takeover situation three times. Experimental trials varied between-subjects concerning the permanent presence or absence of fog in the adaptation condition, the change of visibility conditions from fog to no fog or vice versa in the transfer condition, and the design of the takeover process with one-step or two-steps. Dependent variables included participants’ takeover time, minimum time-to-collision (TTC_min) with the construction site, deceleration and maximum steering behavior, and their ratings of criticality of the driving situation and perceived effort. Results show that participants adapted their deceleration behavior when repeatedly experiencing a takeover situation with the same visibility characteristics (adaptation condition). Changing these characteristics (transfer condition) lead to increased minimum TTCs, criticality and perceived effort ratings. In general, participants were able to maintain their takeover behavior in takeover situations with varying visibility characteristics indicating that they can transfer their takeover behavior across situations. Finally, the two-step takeover request process was associated with longer takeover times. However, minimum TTCs were larger and maximum steering movements and criticality ratings were lower compared to the one-step process. We conclude that drivers transfer their behavior across takeover situations. However, this performance comes at higher costs in terms of perceived effort and criticality. In addition, two-step takeover request processes should be favored over one-step processes when designing takeover requests. Future studies should examine the validity of the results in various takeover situations and on-the-road studies.     

Assessing the feasibility of the theory of planned behaviour in predicting drivers’ intentions to operate conditional and full automated vehicles

Guided by the Theory of Planned Behaviour (TPB), this study examined the beliefs underpinning, and feasibility of the factors in predicting, individuals’ intentions to use a conditional (Level 3) automated vehicle (AV) and a full (Level 5) AV. Australian drivers (N = 505) aged 17–81 years (Mean age = 33.69, SD = 18.79) were recruited and completed a 20 min online survey which featured both quantitative and qualitative items. For the quantitative data, two linear regressions revealed that the TPB standard constructs of attitudes, subjective norm, and perceived behavioural control (PBC) accounted for 66% of the variance in intentions to use a conditional AV and 68% of the variance in intentions to use a full AV. Of the TPB constructs, attitudes and subjective norms were significant positive predictors of future intentions to use conditional and full AVs. For the qualitative data, some differences emerged for the underlying behavioural beliefs that underpinned intentions to use conditional and full AVs. For example, having beliefs about control over the conditional AV was identified by many participants as an advantage, while not being in full control of the full AV was identified as a disadvantage. For underlying control beliefs, participants identified similar barriers for both vehicle types, including; high costs, lack of trust, lack of control over the vehicle, lack of current legislation to support the mainstream introduction of these vehicles, and concerns of safety for self and for other road users when operating AVs. Overall, these findings provide some support for applying the TPB to understand drivers’ intended use of AVs. However, while the current study showed that the constructs of attitudes and subjective norms might reflect intended use of AVs, more research is required to further examine the role of PBC. Additionally, the findings provide initial insights into the underlying behavioural and control beliefs that may motivate drivers to use AVs and highlight the similarities and differences in drivers’ perceptions towards two levels of vehicle automation.     

Hilbert's epsilon as an operator of indefinite committed choice

Hilbert and Bernays avoided overspecification of Hilbert's ε-operator. They axiomatized only what was relevant for their proof-theoretic investigations. Semantically, this left the ε-operator underspecified. After briefly reviewing the literature on semantics of Hilbert's epsilon operator, we propose a new semantics with the following features: We avoid overspecification (such as right-uniqueness), but admit indefinite choice, committed choice, and classical logics. Moreover, our semantics for the ε simplifies proof search and is natural in the sense that it mirrors some cases of referential interpretation of indefinite articles in natural language.     

Pedestrians’ road crossing behaviour in front of automated vehicles: Results from a pedestrian simulation experiment using agent-based modelling

The objective of this research is to explore the relation between personal characteristics of pedestrians and their crossing behaviour in front of an automated vehicle (AV). For this purpose, a simulation experiment was developed using Agent-Based Modelling (ABM) techniques. Sixty participants were asked to cross the road in a virtual environment displayed on a computer screen, allowing to record their crossing behaviour when in the presence of AVs and conventional vehicles (CVs). In some experimental configurations, the AVs communicated their intention to continue or not to continue their trajectories through the use of lights. The ABM allowed controlling the behaviour of the vehicles when interacting with the simulated avatar of the respondents. The subjects of the experiment were also asked to fill in a questionnaire about usual behaviour in traffic, as well as attitudes and risk perceptions toward crossing roads. The questionnaire data were used to estimate individual specific behavioural latent variables by means of principal component analysis which resulted in three main factors named: violations, lapses, and trust in AVs. The results of generalized linear mixed models applied to the data showed that besides the distance from the approaching vehicle and existence of a zebra crossing, pedestrians’ crossing decisions are significantly affected by the participants’ age, familiarity with AVs, the communication between the AV and the pedestrian, and whether the approaching vehicle is an AV. Moreover, the introduction of the latent factors as explanatory variables into the regression models indicated that individual specific characteristics like willingness to take risks and violate traffic rules, and trust in AVs can have additional explanatory power in the crossing decisions.     

Effects of different non-driving-related-task display modes on drivers’ eye-movement patterns during take-over in an automated vehicle

Highly automated vehicles relieve drivers from driving tasks, allowing them to engage in non-driving-related-tasks (NDRTs). However, drivers are required to take over control in certain circumstances due to the limitations of highly automated vehicles. This study focused on drivers’ eye-movement patterns during take-overs when an NDRT (watching videos) was presented via a head-up-display (HUD) and a mobile device display (MDD), compared to no NDRT as the baseline. The experiment was conducted in a high-fidelity driving simulator with real-world driving videos scenarios. Forty-six participants took part in the experiment by completing three drives in three counterbalanced conditions (HUD, MDD and baseline). A take-over-request was issued towards the end of automated driving requesting drivers to stop the NDRT and take over control. Eye-movement data including pupil diameter, blinks, glance duration and number of AOI (Area of Interest) were collected and analysed. The results show that during automated driving, drivers were more engaged in the MDD NDRT with smaller pupil diameter and shorter glance duration on the front scenario compared to the HUD and baseline modes. The number of AOI was reduced during automated driving in both MDD and HUD modes. The take-over-request redirected drivers’ visual attention back to the driving task from NDRT by increasing drivers’ pupil diameter, glance duration and number of AOI. However, the effect of MDD NDRT on pupil diameter and glance duration continued even after the take-over-request when the NDRT was terminated. The study demonstrated HUD is a better display to help maintain drivers’ attention on the road.