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• With a survey, we investigated factors that predict trust in ADAS for current users. • Subjective knowledge, but not objective knowledge of limitations, predicted trust. • Objective and subjective knowledge were not correlated. • Propensity to trust technology in general also predicted trust in ADAS. Trust has been shown to influence whether drivers use advanced driver assistance systems (ADAS) appropriately, and thus understanding the factors influencing trust in ADAS may help inform interventions to support appropriate use. We surveyed 369 drivers to investigate the factors that predict trust in ADAS for current users. Participants were required to have experience using ADAS, specifically systems that simultaneously control longitudinal and lateral movement of the vehicle (participants reported using adaptive cruise control and lane keeping assist systems at the same time in their vehicle at least 1–4 times per month). In addition to assessing trust, the survey included questions to assess objective knowledge about ADAS limitations, self-reported understanding of ADAS (i.e., how correct and complete drivers thought their understanding of ADAS was), number of methods they had previously used to learn about ADAS, frequency of ADAS use, familiarity with technology, propensity to trust technology, and demographics. Regression results showed that self-reported understanding, but not objective knowledge, predicted trust in ADAS, with higher self-reported understanding being associated with higher trust. Self-reported understanding was not correlated with objective knowledge; participants rated their self-reported understanding highly, but only identified an average of 42% of the system limitations included in the survey. Propensity to trust technology was also a significant predictor of trust in ADAS, with higher propensity to trust technology in general associated with higher trust in ADAS. These findings suggest that interventions aimed at supporting appropriate trust in ADAS could be designed to increase drivers' awareness of potential gaps in their understanding and adjust expectations of ADAS for those with a high propensity to trust technology. [ABSTRACT FROM AUTHOR]

Recent research has extensively examined in-vehicle display designs for supporting the operation of driving automation. As automation relieves drivers from various driving tasks including vehicle control (e.g. steering, accelerating, and braking), driving performance measures (e.g. speed, lane deviations) may not be informative indicators for evaluating the effectiveness of in-vehicle displays. Gaze-based measures are a better alternative given their link to driver visual attention, an indication of driver engagement. A scoping review was conducted to review the literature on the design of displays for supporting the operation of driving automation and the evaluation of these displays using gaze-based measures. Forty-three articles were included in the review. Most of the studies investigated visual (and mixed visual-auditory) displays that provide alerts to the driver for when to intervene automation classified as Level 3. The adopted gaze measures mostly relied on static areas of interest (AOIs), with fewer studies looking at more fine-grained, context dependent AOIs. The paper summarises the findings of the review, including research trends and gaps, as well as recommendations for future research. [ABSTRACT FROM AUTHOR]

Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames. Secondly, the Speed Limit Classification (SLC) module, featuring machine learning classifiers alongside a newly developed model called DeepSL, harnesses the power of a CNN architecture to extract intricate features from speed limit sign images, ensuring efficient and precise recognition. In addition, a new Speed Limit Classifiers Fusion (SLCF) module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning's voting technique. Through rigorous software and hardware validation processes, the proposed methodology has achieved highly significant F1 scores of 99.98% and 99.96% for DS theory and the voting method, respectively. Furthermore, a prototype encompassing all components demonstrates outstanding reliability and efficacy, with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board, marking a significant advancement in TSRS technology. [ABSTRACT FROM AUTHOR]

This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the “virtual track” theory and creates a model for virtual track autonomous vehicle management and control based on the ideas and methods of railway train operation. The developed model and adopted algorithm allow for large-scale autonomous driving vehicle control on the highway while considering the temporal-spatial distribution of vehicles, temporal-spatial trajectory diagram optimization, and the management and control model and algorithm for autonomous vehicles, as design goals. The ultimate objective is to increase the safety of the road traffic environment when autonomous vehicles are operating in it together with human-driven vehicles and achieve more integrated and precise organization and scheduling of these vehicles in such mixed traffic conditions. The developed model adopted a “particle swarm” optimization algorithm that is tested in a hypothetical network pending a full-scale test on a real highway. The paper concludes that the proposed management and control model and algorithm based on the “virtual track” theory is promising and demonstrates feasibility and effectiveness for further development and future application.

This paper offers a novel view for managing and controlling the movement of driverless, i.e., autonomous, vehicles by converting this movement to a simulated train movement moving on a rail track. It expands on the "virtual track" theory and creates a model for virtual track autonomous vehicle management and control based on the ideas and methods of railway train operation. The developed model and adopted algorithm allow for large-scale autonomous driving vehicle control on the highway while considering the temporal-spatial distribution of vehicles, temporal-spatial trajectory diagram optimization, and the management and control model and algorithm for autonomous vehicles, as design goals. The ultimate objective is to increase the safety of the road traffic environment when autonomous vehicles are operating in it together with human-driven vehicles and achieve more integrated and precise organization and scheduling of these vehicles in such mixed traffic conditions. The developed model adopted a "particle swarm" optimization algorithm that is tested in a hypothetical network pending a full-scale test on a real highway. The paper concludes that the proposed management and control model and algorithm based on the "virtual track" theory is promising and demonstrates feasibility and effectiveness for further development and future application. [ABSTRACT FROM AUTHOR]

Abstract The purpose of the study was to investigate how drivers use assisted and automated driving systems (DAS), more specifically their usage of SAE Level 1 and Level 2 systems, in different situations. An online survey was distributed to 2500 respondents in China, Germany, Spain, and the USA. The final dataset consisted of 549 respondents, all non‐professional drivers, with access to a minimum of a Level 1 system. A subset, 159 in total, had access also to a Level 2 DAS. The survey included questions on the attitude towards, access to, and usage of Level 1 and Level 2 systems in nine different situations respectively. The data was analysed on an individual and a national level. A cluster analysis showed two main groups: frequent and non‐frequent users. On an individual level, the reported usage of Level 1 and Level 2 DAS respectively differed depending on traffic situation, weather and daylight conditions and driver state. Reports by respondents with access to both Level 1 and Level 2 systems did not reveal any statistically significant differences in usage between situations. The Spanish sample was the only one that showed a consistently different usage pattern compared to samples from China, Germany, and the USA.

• Level of agentivity, mediated by motor engagement required by automated driving assistance remains to be thoroughly studied. • A simulator experiment, manipulated the level of agentivity to study its impact on gaze during vehicle control take-over. • Level of agentivity impacted duration and frequency of gaze visits in areas crucial for car control (near road ahead). • The level of agentivity possibly delayed gaze parameters in these visual areas even shortly after manual control resumption. In the context of progressive automation of the driving activity, an alternation of automated driving phases and manual driving phases is becoming a reality. The problem of regaining manual control of the vehicle after a period of automated control (i.e., take-over) is critical, particularly concerning visual exploration during the transition phase. A driving simulator experiment was designed to investigate the impact of the level of agentivity manipulated by different levels of motor engagement on gaze parameters during the different temporal stages of a non-critical take-over situation (i.e., overtaking collision zone). The level of motor engagement decreased according to the increase in the level of automation; eye tracking data were collected, and gaze distribution over functional areas of interest was analysed across several periods of interest. Results revealed an influence of the degree of motor engagement on the gaze parameters linked to the integration and processing of visual information for a nominal driving period (i.e., automation activated) as well as during the take-over preparation period. During the period of effective resumption of manual control, most of the ocular parameters went back to their initial values, except for the higher motor disengagement modes (i.e., lowest levels of agentivity). These automation levels seem to show a residual influence of take-overs on manual driving, particularly in the ocular exploration of areas carrying the information useful for the fine regulation of the vehicle trajectory. [ABSTRACT FROM AUTHOR]

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments. [ABSTRACT FROM AUTHOR]

Driving an automated vehicle requires a clear understanding of its automation capabilities and resulting duties on the driver's side. This is true across all levels of automation but especially so on SAE levels 3 and below, where the driver has an active driving task performance and/or monitoring role. If the automation capabilities and a driver's understanding of them do not match, misuse can occur, resulting in decreased safety. In this paper, we present the results from a simulator study that investigated driving mode awareness support via ambient lights across automation levels 0, 2, and 3. We found lights in the steering wheel to be useful for momentary and lights below the windshield for permanent indication of automation-relevant information, whereas lights in the footwell showed to have little to no positive effects on driving mode awareness. [ABSTRACT FROM AUTHOR]

Given that automation complacency, a hitherto controversial concept, is already used to blame and punish human drivers in current accident investigations and courts, it is essential to map complacency research in driving automation and determine whether current research can support its legitimate usage in these practical fields. Here, we reviewed its status quo in the domain and conducted a thematic analysis. We then discussed five fundamental challenges that might undermine its scientific legitimation: conceptual confusion exists in whether it is an individual versus systems problem; uncertainties exist in current evidence of complacency; valid measures specific to complacency are lacking; short-term laboratory experiments cannot address the long-term nature of complacency and thus their findings may lack external validity; and no effective interventions directly target complacency prevention. The Human Factors/Ergonomics community has a responsibility to minimise its usage and defend human drivers who rely on automation that is far from perfect. Practitioner summary: Human drivers are accused of complacency and overreliance on driving automation in accident investigations and courts. Our review work shows that current academic research in the driving automation domain cannot support its legitimate usage in these practical fields. Its misuse will create a new form of consumer harms. [ABSTRACT FROM AUTHOR]

Driver assistance systems are designed to increase comfort and safety by automating parts of the driving task. At the same time, modern in-vehicle information systems with large touchscreens provide the driver with numerous options for entertainment, information, or communication, and are a potential source of distraction. However, little is known about how driving automation affects how drivers interact with the center stack touchscreen, i.e., how drivers self-regulate their behavior in response to different levels of driving automation. To investigate this, we apply multilevel models to a real-world driving dataset consisting of 31,378 sequences. Our results show significant differences in drivers' interaction and glance behavior in response to different levels of driving automation, vehicle speed, and road curvature. During automated driving, drivers perform more interactions per touchscreen sequence and increase the time spent looking at the center stack touchscreen. Specifically, at higher levels of driving automation (level 2), the mean glance duration toward the center stack touchscreen increases by 36% and the mean number of interactions per sequence increases by 17% compared to manual driving. Furthermore, partially automated driving has a strong impact on the use of more complex UI elements (e.g., maps) and touch gestures (e.g., multitouch). We also show that the effect of driving automation on drivers' self-regulation is greater than that of vehicle speed and road curvature. The derived knowledge can inform the design and evaluation of touch-based infotainment systems and the development of context-aware driver monitoring systems. [ABSTRACT FROM AUTHOR]

The purpose of the study was to investigate how drivers use assisted and automated driving systems (DAS), more specifically their usage of SAE Level 1 and Level 2 systems, in different situations. An online survey was distributed to 2500 respondents in China, Germany, Spain, and the USA. The final dataset consisted of 549 respondents, all non‐professional drivers, with access to a minimum of a Level 1 system. A subset, 159 in total, had access also to a Level 2 DAS. The survey included questions on the attitude towards, access to, and usage of Level 1 and Level 2 systems in nine different situations respectively. The data was analysed on an individual and a national level. A cluster analysis showed two main groups: frequent and non‐frequent users. On an individual level, the reported usage of Level 1 and Level 2 DAS respectively differed depending on traffic situation, weather and daylight conditions and driver state. Reports by respondents with access to both Level 1 and Level 2 systems did not reveal any statistically significant differences in usage between situations. The Spanish sample was the only one that showed a consistently different usage pattern compared to samples from China, Germany, and the USA. [ABSTRACT FROM AUTHOR]

Driver fatigue reduces the safety of traditional driving and limits the widespread adoption of self-driving cars; hence, the monitoring and early detection of drivers' drowsiness plays a key role in driving automation. When representing the drowsiness indicators as large feature vectors, fitting a machine learning model to the problem becomes challenging, and the problem's perspicuity decreases, making dimensionality reduction crucial in practice. For this reason, we propose an embedded feature selection algorithm that can be later utilized as a building block in the system development of a neural network-based drowsiness detector. We have adopted a technique: a so-called Feature Prune Layer is placed in front of the first layer in the architecture; as a result, its weights change regarding the importance of the corresponding input features and are deleted iteratively until the desired number is reached. We test the algorithm on EEG data, as it is one of the best indicators of drowsiness based on the literature. The proposed FS algorithm is able to reduce the original feature set by 95% with only 1% degradation in precision, while the precision increases by 1.5% and 2.7% respectively when selecting the top 10% and top 20% of the initial features. Moreover, the proposed method outperforms the widely popular Principal Component Analysis and the Chi-squared test when reducing the original feature set by 95%: it achieves 24.3% and 3.2% higher precision respectively. [ABSTRACT FROM AUTHOR]

This paper investigates two fundamental questions related to safety and insurance in the age of automation. First, we touch upon the question of safety and liability under infrastructure-assisted automated driving. In such an environment, automakers provide vehicle automation technology while infrastructure support service providers (ISSPs) provide smart infrastructure services. Additionally, customers can receive coverage for accidents from either of these actors but also from legacy auto insurers. We investigate the effect of market structure on safety and accident coverage and show that an integrated monopoly provides full coverage and fully accounts for accident costs when choosing safety levels. However, in the Nash setting, even though full coverage obtains, lack of coordination leads to partial internalization of accident costs by the automaker. Moreover, multiple equilibria might exist, some of them undesirable. We show that, both in the presence and absence of legacy insurance, an appropriate liability rule can induce optimal safety levels under the Nash setting. Our second question concerns itself with the role and welfare effects of the availability of legacy auto insurance in the age of infrastructure-assisted automated driving. Our analysis shows that the industry is not necessary for optimal coverage when the cost of accidents is known in advance and all possible accident scenarios are contractible. In fact, their presence can even harm safety, even though it ensures full coverage for accidents. When only insurance contracts with capped liability for automakers and ISSPs are available and in a monopolistic environment, legacy insurance potentially harms welfare. This highlights the important role of market structure in assessing the future of insurance in the age of automated driving and lays the groundwork for future investigations in this direction. • Accidents will not disappear in an automated driving market. • Multiple actors in this market: automakers, smart infrastructure operators, and legacy insurers. • How should liability and insurance be apportioned in this context? Is there still a role to play for legacy insurers? • We design and analyze a welfare-maximizing rule for liability sharing. • We show the importance of market structure in determining whether and how legacy insurance will affect welfare in the future. [ABSTRACT FROM AUTHOR]

Impacts of driving automation on traffic flow and emissions are usually studied with traffic simulations using only few speed limits and traffic volumes. Without considering the real-world prevalence of simulated scenarios, it is unknown how the results translate to real-world conditions, such as a regional motorway network. The present study assessed the potential impacts of conditionally automated driving, described by stable vehicle motion control and longer time gaps, on the European motorway network assuming no changes in other influential factors, such as travel demand or vehicle fleet. Traffic simulations provided estimates of the effect magnitude per vehicle kilometre travelled (VKT) in representative conditions, and results were scaled up using map-, traffic- and weather-related data, accounting for the VKT per condition. Overall, the impacts of automated vehicles (AVs) on the European motorway network are likely small. Travel times and delay are estimated to increase by 0.8% and 1.3% respectively at a 100% AV penetration rate among passenger cars, and CO2 emissions to drop by 0.5%. While large reductions of average travel time (up to 8.0-10.4%), delay (up to 17.5-34.8%) and emissions (up to 13.5-15.0%) were found at high traffic volumes, most (86%) of the VKT accumulate at low traffic volumes, with small estimated effects. Thus, although beneficial in some conditions, the AVs considered in this study are not likely to support Europe's sustainability goals. Findings advocate a comprehensive approach: Whereas impacts are likely greatest in heavy traffic, the prevalence of conditions must be considered in network level assessment. [ABSTRACT FROM AUTHOR]

Recently, the first successful deployment of a fully automated commercial freight train operation was announced. This is the world’s first automated heavy-duty and long-haul rail network. It’s an impressive achievement, but why has it taken so long to achieve this when driverless urban metros have been in operation for more than 50 years? Although urban metros and freight trains are vehicles moved on rails, their operation and environment differ significantly. Metros operate in closed rail networks, while freight trains operate in open rail networks. However, the same taxonomy is often used to classify automation interchangeably in both environments. This paper provides context and an overview of driving automation in freight rail and reviews the existing taxonomies. This paper starts by providing context with an overview of the general process of driving a vehicle by delimiting its different stages. Next, we describe the overall process of driving a freight train to show the distinctive features of its setup and operation. In this analysis, we will point out the essential differences between open and closed rail networks, and the tasks that can potentially be automated. Additionally, we examine the evolution of level-based automation taxonomies and review those that have been proposed exclusively for driving automation in open and closed railway networks. Our objective is to provide a thorough summarization of the most relevant taxonomies to advance the definition of a suitable taxonomy and framework to classify automation capabilities in rail freight transport and identify some complex challenges ahead.

Cars are increasingly being automated, with the ultimate goal of creating a completely self-driving car. The interaction between the human driver or user of an automated vehicle and the driving automation will radically change from the interaction drivers now have with their vehicles. This article classifies the various new interactions that will occur as automation progresses through the six levels of automation discerned by the Society of Automotive Engineers. Smooth interaction between man and machine is critical for safety. A breakdown in interaction can easily give rise to accidents. The ensuing liability questions are difficult to solve: who is responsible for a breakdown in interaction? This article looks through the lens of EU product liability law as harmonized in directive 85/374/EC and asks whether failed man–machine interactions may be qualified as defects. [ABSTRACT FROM AUTHOR]

Design of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future research. [ABSTRACT FROM AUTHOR]

• The effects of various factors on comfort in partially automated cars were examined. • Type of road, weather conditions and traffic congestion all influenced comfort. • Adapting the speed of the vehicle helped to reduce discomfort in some conditions. • Data clustering revealed four different behavioral profiles among participants. • Anderson's scenario-based method can be used to study comfort in automated cars. Although it is key to improving acceptability, there is sparse scientific literature on the experience of humans as passengers in partially automated cars. The present study therefore investigated the influence of road type, weather conditions, traffic congestion level, vehicle speed, and human factors (e.g., trust in automated cars) on passenger comfort in an automated car classified as Level 3 according to the Society of Automotive Engineers (SAE). Participants were exposed to scenarios in which a character is driven by an SAE Level 3 automated car in different combinations of conditions (e.g., highway × heavy rain × very congested traffic × vehicle following prescribed speed). They were asked to rate their perceived comfort as if they were the protagonist. Results showed that comfort was negatively affected by driving in downtown (vs. highway), heavy rain, and congested traffic. Interaction analyses showed that reducing the speed of the vehicle improved comfort in these two last conditions, considered either individually or in combination. Cluster analysis revealed four profiles: trusting in automation , averse to speed reduction , risk averse , and mistrusting automation. These profiles were all influenced differently by the driving conditions, and corresponded to varying levels of trust in automated cars. This study suggests that optimizing comfort in automated cars should take account of both driving conditions and human profiles. [ABSTRACT FROM AUTHOR]

• Video data from a 4-week field test allowed analysis of disengagement from driving. • Disengagement included interacting with electronics or driving with hands-off-wheel. • Analysis compared disengagement when using automation relative to manual driving. • Use of partial automation was associated with increased odds of disengagement. • Increased disengagement was more apparent the last two weeks of the 4-week trial. A small body of research on the real-world use of commercially available partial driving automation suggests that drivers may struggle with or otherwise lapse in adequately monitoring the system and highway environment, and little is known about key issues such as how behavior associated with system use changes over time. The current study assessed how driver disengagement, defined as visual-manual interaction with electronics or removal of hands from the wheel, differed as drivers became more accustomed to partial automation over a 4-week trial. Ten volunteers drove a Volvo S90 with adaptive cruise control (ACC), which automates speed and headway, and Pilot Assist, which combines ACC and continuous lane centering. Instrumentation captured automation use, secondary task activity, hands-on-wheel status, vehicle speed, and GPS location during all trips. The longer drivers used the Pilot Assist partial automation system, the more likely they were to become disengaged, with a significant increase in the odds of observing participants with both hands off the steering wheel or manipulating a cell phone relative to manual control. Results associated with use of ACC found comparable or lower levels of disengagement compared to manual driving as the study progressed. This study highlights concerns about vehicle control and the degree to which drivers remain actively in the loop when using automation. Calls for implementing more robust driver monitoring with partial automation appear warranted—particularly those that track head or eye position. [ABSTRACT FROM AUTHOR]

• Dependability initiated and most governed driver trust in partial driving automation. • Continuous exposure to driving automation leads increases in the level of driver trust. • Driver trust can be guided by different dimensions of trust according to the levels of knowledge about driving automation and the type of automation failure. • Results have implications for developing driver training methods. To prompt the use of driving automation in an appropriate and safe manner, system designers require knowledge about the dynamics of driver trust. To enhance this knowledge, this study manipulated prior information of a partial driving automation into two types (detailed and less) and investigated the effects of the information on the development of trust with respect to three trust attributions proposed by Muir (1994): predictability, dependability, and faith. Furthermore, a driving simulator generated two types of automation failures (limitation and malfunction), and at six instances during the study, 56 drivers completed questionnaires about their levels of trust in the automation. Statistical analysis found that trust ratings of automation steadily increased with the experience of simulation regardless of the drivers' levels of knowledge. Automation failure led to a temporary decrease in trust ratings; however, the trust was rebuilt by a subsequent experience of flawless automation. Results showed that dependability was the most dominant belief of drivers' trust throughout the whole experiment, regardless of their knowledge level. Interestingly, detailed analysis indicated that trust can be accounted by different attributions depending on the drivers' circumstances: the subsequent experience of error-free automation after the exposure to automation failure led predictability to be a secondary predictive attribution of drivers' trust in the detailed group whilst faith was consistently the secondary contributor to shaping trust in the less group throughout the experiment. These findings have implications for system design regarding transparency and for training methods and instruction aimed at improving driving safety in traffic environments with automated vehicles. [ABSTRACT FROM AUTHOR]

In the case of damage caused by a conventionally driven vehicle, it is usually possible in EU Member States to subject the possessor/controller of the vehicle to heightened tortious no-fault liability, i.e. strict liability. The development and possible introduction of self-driving vehicles pose a challenge also for tort law, because it is unlikely that self-driving vehicles will not cause any damage to third parties. With the application of strict liability in mind, this article attempts to identify possible differences between damage caused by a conventional vehicle as opposed to that caused by a self-driving vehicle. In light of this developing technology the key legislative question to be answered is whether the introduction of self-driving vehicles calls for, among other things, the revision of strict liability rules. Answers to these questions are sought mainly based on Estonian tort law.

Automated Driver Assistance Systems (ADAS), which aim to enhance safety and comfort while driving, are becoming increasingly popular in vehicles today. However, ADAS are not yet operative in every situation due to technical limitations, and therefore do not cover all driving situations, traffic, weather and/or road conditions. In order for drivers to use these systems in a safe manner, they need to understand the different modes of operation, as well as the limitations of the systems, or they will not be able to build appropriate trust and adequate usage strategies.Therefore, the purpose of this study was to investigate the factors influencing user understanding of ADAS by implementing an Explanatory Sequential Mixed Methods design. This was done by triangulating data from a Naturalistic Driving (ND) study (132 vehicles) with explanations and reflections from in-depth interviews of purposefully selected participants (12 drivers from the vehicle pool) who were showing different usage patterns.The results show that users’ understanding is influenced by preconceptions about the system, as well as the perceived system performance and usefulness, leading to different levels of trust that affect the users’ engagement with the ADAS. It was found that the driver’s perception of a system does not just change over time, but changes through different situations presented, challenging the expected events and the users’ mental model of the interaction with the system. Therefore, to gain trust and appropriate usage strategies for the ADAS the user needs to overcome potentially negative experiences and challenge the current understanding of the ADAS, by stepping over the threshold.

With the successful introduction of advanced driver assistance systems, vehicles with driving automation technologies have begun to be released onto the market. Because the role of human drivers during automated driving may be different from the role of drivers with assistance systems, it is important to determine how general users consider such new technologies. The current study has attempted to consider driver trust, which plays a critical role in forming users' technology acceptance. In a driving simulator experiment, the demographic information of 56 drivers (50% female, 64% student, and 53% daily driver) was analyzed with respect to Lee and Moray's three dimensions of trust: purpose, process, and performance. The statistical results revealed that female drivers were more likely to rate higher levels of trust than males, and non-student drivers exhibited higher levels of trust than student drivers. However, no driving frequency-related difference was observed. The driver ratings of each trust dimension were neutral to moderate, but purpose-related trust was lower than process- and performance-related trust. Additionally, student drivers exhibited a tendency to distrust automation compared to non-student drivers. The findings present a potential perspective of driver acceptability of current automated vehicles. [ABSTRACT FROM AUTHOR]

Driving automation has been developing rapidly during the latest decade. However, all current technologies of driving automation still require human drivers’ monitoring and intervention. This means that during driving automation, the control by human driver and by the driving automation system are blended. In this case, if the human driver loses the sense of agency over the vehicle, he/she may not be able to actively engage in driving, and may excessively rely on the driving automation system. This review focuses on the subjective feeling of agency of the human driver over the vehicle in such situations. We address the possible measures of agency in driving automation, and discuss the insights from literatures on the sense of agency in joint control, robotics, automation, and driving assistance. We suggest that maintaining the sense of agency for human driver is important for ethical and safety reasons. We further propose a number of avenues for further research, which may help to better design an optimized driving automation considering human sense of agency.

• Acceptance of five Level 2 systems varied across test vehicles. • Adaptive cruise control was trusted more than lane centering. • Functional attributes predicted agreement that automation improved driving experience. • Uncomfortable experiences with automation occurred in common road scenarios. Driving automation systems are being introduced into mass-market vehicles, but little is known about whether drivers will trust driving automation systems and use the technology. In this study, volunteer drivers operated five vehicles equipped with automated longitudinal and lateral control and completed surveys about their experience. A subset of drivers also documented uncomfortable experiences as they used the automation while driving. Driver agreement that the automation improved the overall driving experience was significantly higher for Vehicle A than the systems implemented in the other four vehicles. Drivers reported significantly higher trust in adaptive cruise control than in lane centering in every vehicle but Vehicle B. Increased agreement that the automation consistently detected lane lines; detected moving vehicles ahead; and made smooth, gentle steering inputs was associated with significant increases in agreement that the automation improved the overall driving experience. Situations where drivers reported feeling uncomfortable with the automation during their drive were dominated by instances where lane centering struggled with common roadway features such as hills and intersections. [ABSTRACT FROM AUTHOR]

• Younger and older drivers consider driving automation trustworthy and acceptable. • The initial system experience significantly increases trust and acceptance. • After the initial system experience, trust and acceptance remain on a stable level. • Especially older drivers show a positive attitude towards driving highly automated. • Age-specific acceptance barriers regarding automotive technologies are identified. Highly automated driving (HAD) is expected to improve future road transport, especially for older adults, provided that it is trusted and accepted by drivers. Research on Advanced Driver Assistance Systems (ADAS) suggests that system experience can enhance drivers' trust and acceptance. To evaluate the transferability of this result to HAD, we examined the development of drivers' trust and acceptance regarding this technology at different stages of system experience in a driving simulator as well as on a test track. Age effects were additionally addressed by comparing the results of 20 younger (25–45 years) and 20 older (65–85 years) drivers in the driving simulator study. Trust and acceptance were assessed before the initial system experience as well as after the first and second automated drive. Both age groups showed slightly positive a priori trust and acceptance ratings, which significantly increased after the initial experience and remained stable afterwards. Older drivers reported a more positive attitude towards using HAD despite their lower self-assessed self-efficacy and environmental conditions facilitating HAD-usage (e.g. technical support) compared to younger drivers. In the subsequent test track study, trust and acceptance of the younger driver group were assessed before and after experiencing HAD in a test vehicle. Neither trust nor acceptance decreased despite the absence of further system experiences between both studies and the increased realism on the test track. These results underline the importance of the initial system experience for HAD-trust and –acceptance and emphasize the significance of automotive technologies for the preservation of older drivers' mobility. [ABSTRACT FROM AUTHOR]

The adoption of electric vehicles (EVs) is emerging in the car-sharing business due to the high potential of reducing operational costs as well as greenhouse gas emissions. In this early stage of the EV-sharing business, it is still unclear which battery-charging technology and insurance contract would be more competitive for EV-sharing operators in the context of future business. We have developed a stylized model with which to analyze the impact of different charging technologies, insurance contracts and other related factors on the EV-sharing operator's profit. The operator's demand is derived from customers' utility of driving under a membership scheme. Various operational costs such as electricity and parking place costs are considered. Different battery-charging technologies and insurance contracts are incorporated into the model and then compared. The results show that membership and driving time are strategic complements, and that the operator's profit is sensitive to policy interventions. Fixed-premium insurance leads to lower prices for the EV-sharing service than per-hour-premium insurance. The per-hour premium fees decrease more significantly than the fixed premium fees as autonomous vehicles reduce accident costs. The conditions for different battery-charging technologies to be price competitive are identified and meaningful policy implications are derived. [ABSTRACT FROM AUTHOR]