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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]

Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving.

“Human error” is often implicated as a causal factor in accident investigation yet very little is done to understand ‘why’ such errors occur in the first place. This paper uses the principles of Schema Theory and the Perceptual Cycle Model (PCM) to further explore the circumstances surrounding the fatal Tesla crash in May 2016 in which the driver was fatally injured using team-PCM representations. The preliminary National Highway Traffic Safety Administration accident investigation concluded that the driver of the Tesla Model S was at fault. However, the analysis presented in this paper argues that rather than “driver error”, the underlying cause of this tragic incident could be in fact more akin to a “designer error” implicating the design of the Autopilot feature itself. This is in line with the National Transportation Safety Boards more recent announcement that suggests systems design may have contributed to the crash. It would therefore appear that the drivers expectation of system functionality may not have matched the real life capabilities of the system. This is likely to be a product of inappropriate mental models relating to system function. [ABSTRACT FROM AUTHOR]

Introduction Automated driving represents both challenges and opportunities in highway safety. Google has been developing self-driving cars and testing them under employee supervision on public roads since 2009. These vehicles have been involved in several crashes, and it is of interest how this testing program compares to human drivers in terms of safety. Methods Google car crashes were coded by type and severity based on narratives released by Google. Crash rates per million vehicle miles traveled (VMT) were computed for crashes deemed severe enough to be reportable to police. These were compared with police-reported crash rates for human drivers. Crash types also were compared. Results Google cars had a much lower rate of police-reportable crashes per million VMT than human drivers in Mountain View, Calif., during 2009–2015 (2.19 vs 6.06), but the difference was not statistically significant. The most common type of collision involving Google cars was when they got rear-ended by another (human-driven) vehicle. Google cars shared responsibility for only one crash. Conclusions These results suggest Google self-driving cars, while a test program, are safer than conventional human-driven passenger vehicles; however, currently there is insufficient information to fully examine the extent to which disengagements affected these results. Practical application Results suggest that highly-automated vehicles can perform more safely than human drivers in certain conditions, but will continue to be involved in crashes with conventionally-driven vehicles. [ABSTRACT FROM AUTHOR]

The introduction of partially automated driving systems changes the driving task into supervising the automation with an occasional need to intervene. To develop interface solutions that adequately support drivers in this new role, this study proposes and evaluates an assessment framework that allows designers to evaluate driver-support within relevant real-world scenarios. Aspects identified as requiring assessment in terms of driver-support within the proposed framework are Accident Avoidance, gained Situation Awareness (SA) and Concept Acceptance. Measurement techniques selected to operationalise these aspects and the associated framework are pilot-tested with twenty-four participants in a driving simulator experiment. The objective of the test is to determine the reliability of the applied measurements for the assessment of the framework and whether the proposed framework is effective in predicting the level of support offered by the concepts. Based on the congruency between measurement scores produced in the test and scores with predefined differences in concept-support, this study demonstrates the framework's reliability. A remaining concern is the framework's weak sensitivity to small differences in offered support. The article concludes that applying the framework is especially advantageous for evaluating early design phases and can successfully contribute to the efficient development of driver's in-control and safe means of operating partially automated vehicles. [ABSTRACT FROM AUTHOR]

Objective: To understand the influence of driving experience and distraction on drivers' anticipation of upcoming traffic events in automated vehicles. Background: In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods: A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results: Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion: In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application: Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted. [ABSTRACT FROM AUTHOR]

Understanding the factors influencing trust in advanced driver assistance systems (ADAS) may help inform training and education to support appropriate use. We surveyed 369 drivers with experience using both adaptive cruise control (ACC) and lane keeping assist (LKA). The survey included questions to assess trust in ADAS, along with objective knowledge about ADAS limitations, self-reported understanding of ADAS, familiarity with technology, propensity to trust technology, and demographics. Regression results showed that self-reported understanding, but not objective knowledge, predicted trust in ADAS. Self-reported understanding was not correlated with objective knowledge; overall, participants were not aware of many of the system limitations included in the survey. Propensity to trust technology was also a significant predictor of trust. Training/educational materials could be designed to inform drivers of potential gaps in their understanding and adjust expectations of ADAS to support appropriate trust for those with a high propensity to trust technology., (Copyright © 2023 Human Factors and Ergonomics Society.)

In this paper a car-following model is formulated as a time-continuous dynamic process, depending on two parameters and two inputs. One of these inputs is the follower's desired equilibrium spacing, assumed to exist and to be known. Another input is the speed of the lead vehicle. Given the formulation of the model, the contribution of these two inputs is separable from an analytical point of view. The proposed model is simple enough (whereas not being simplistic) to support real-time applications in the field of advanced driving assistance systems. Starting from the equilibrium spacing, it is possible to estimate the parameters of the model, allowing for a full identification procedure. The modeling framework was prevalidated against observed data from two different data sets, collected by means of two instrumented vehicles in independent experiments, carried out in Italy and the United Kingdom. The validation proved that the proposed car-following model gives good results not only around the desired equilibrium spacing but also in general car-following conditions. The experimental data sets are discussed in terms of parameter values as well as performance of the dynamic process against observed data. [ABSTRACT FROM AUTHOR]

As automation in vehicles becomes more prevalent, the call for understanding the behaviour of the driver while driving an automated vehicle becomes more salient. Although a variety of driver behaviour models exist, and various psychological constructs have been said to be influenced by automation, an empirically testable psychological model of automated driving has yet to be developed. Building upon Stanton and Young's model of driving automation, this article presents an updated model of interrelated psychological constructs. The proposed model was created based upon a systematic literature search of driving automation papers and a subsequent quantification of the number of reported links between a selected set of psychological constructs. A secondary aim of this article is to reach consensus in the use of psychological constructs regarding driving automation. Henceforth special attention is paid to resolving the issue of construct proliferation. [ABSTRACT FROM PUBLISHER]

This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers’ behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance because of the huge number of rear-end crashes. The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. Three different car-following paradigms are also applied to each of the experiments, and modelling parameters are calibrated and compared to obtain indirect confirmation about the observed similarities and differences in driving behaviour. [ABSTRACT FROM AUTHOR]

Many application fields in transportation engineering can benefit from an accurate modelling of car-following behavior. In particular, in recent years, an increased importance is assigned to embed behavioral abilities in ADAS (Advanced Driving Assistance Systems) and in driving automation solutions. However, accurate development of car-following models needs for accounting of the drivers' heterogeneity, which can be easily observed in car-following data. This paper contributes to analyze different sources of heterogeneity with particular focus on three factors: the dispersion over-time of the behavior of a single driver; the heterogeneous behaviors of different drivers; and the possible bias introduced by some oversimplification of the modelling framework, with particular reference to the type of leading vehicle. Our analyses are based on the observation of car-following trajectories collected in a large experiment involving one hundred drivers. Observed behaviors have been interpreted by means of several car-following models proposed in past. The comparison of the values of the parameters identified for the models (versus observed data) is adopted for the analyses. Moreover, directly observed variables (car-following speed and spacing) are adopted to complement and confirm the analyses. Results show that the greater among the sources of dispersion is the across-driver heterogeneity and that by taking into account such an inherent drivers' dispersion of car-following behaviors it is possible to better identify also the effect of the modelling oversimplifications induced by not considering the type of leading vehicle. [ABSTRACT FROM AUTHOR]

Although the number of automated vehicles is expected to grow there is limited understanding on how drivers will cope with manual driving when automation fails. The study's goals were: (1) develop an experimental test-bed for evaluation of levels of vehicle automation, in-vehicle secondary tasks, and hazardous scenarios; and (2) conduct empirical evaluation to examine how well drivers mitigate road hazards when automation fails unexpectedly, looking at situations where drivers were either engaged with secondary tasks or not prior to the automation failure and/or the hazardous event. The STISIM fixed base simulator, embedded into a car was utilized. Driving scenes were projected on a 7m diameter round screen. An in-house LabVIEW-based program was used to control the simulator and displays; enabling control of four modes of vehicle automation: Manual-no automation (M), Adaptive Cruise Control (ACC), Automatic Steering (AS), and Automated Driving (AD). Two types of secondary tasks were included: (1) Driving related. This task required on road glances; (2) Driving unrelated. This task, presented on an in-vehicle touchscreen, required in-vehicle glances. In a mixed within-between experimental design, eighteen participants were asked to drive through various drives. Each drive included 4 driving sections in the following order: (1) automated, (2) manual, (3) automated with secondary tasks, and (4) manual with secondary tasks, in one of the levels of automation (ACC, AS or AD). In each section, typical hazardous events appeared. Automation failure (i.e., the need to assume manual control) was alerted by sound and visually on the touchscreen. The results showed that while engagement with a non-driving related secondary task lead to more crashes, automation failure did not, especially when drivers were monitoring the road. In addition, drivers' performance on the secondary task revealed differential effects of automation mode with respect to the road conditions. Implications of this study are discussed. [ABSTRACT FROM AUTHOR]

• We compared two ADAS training approaches in a remote face-to-face study. • We found no difference in ADAS knowledge or reliance intention between approaches. • Both training approaches lowered trust for scenarios where ADAS may not work. • But training focused on limitations was associated with negative bias towards ADAS. Expecting drivers to learn and remember numerous limitations may not be a practical approach to training for advanced driver assistance systems (ADAS), particularly for self-initiated training in the absence of formal training requirements. One alternative is focusing on the importance of the driver remaining engaged in the driving task (responsibility-focused approach). We investigated the effects of two training videos (responsibility-focused and limitation-focused) on reliance intention, trust, and ADAS knowledge. In a remote study, participants (N = 61) watched dashcam clips (8 that require takeover, 8 no takeover) and for each clip, they reported whether they would manually intervene and their trust in ADAS (assessing situational reliance intention and trust, respectively). Participants also completed a questionnaire that included items measuring ADAS knowledge. Responses were collected at three stages: pre-training, post-training, and a follow-up session (minimum four weeks later). There were no significant differences between approaches in terms of knowledge of situations in which ADAS would not work, appropriate situational reliance intention, or trust in takeover scenarios. Compared to the responsibility-focused video, the limitation-focused video was associated with lower trust in no takeover scenarios and negative bias at post-training (i.e., bias towards reporting that ADAS would not work for the knowledge questionnaire and bias towards taking manual control/not using ADAS for the dashcam clips). Given the limited differences between training approaches and potential drawbacks of the limitation-focused approach, our results suggest that the responsibility-focused training approach is worth exploring further. [ABSTRACT FROM AUTHOR]

In socio-technical systems such as modern planes or cars, assistance systems are used to increase performance and to maintain safety. This raises the questions, how they cooperate with human drivers and whether human operators face a loss of control. The article examines the loss-of-control argument empirically by means of a survey of a sample of car drivers with a number of driver assistance systems. It takes personal experiences with these systems into account, as reported by interviewees, and also figures out main factors that influence the drivers’ perceptions. We want to assess if the cooperation of driver assistance systems in modern cars raises the complexity and non-controllability of the whole system to a degree that is evaluated negatively by respondents in terms of loss-of-control. Additionally, our study asks how the interviewees perceive the current role distribution in modern cars and which future role distribution between humans and autonomous technology they expect. Our analysis will show that our respondents mostly feel comfortable with driver assistance systems, and satisfaction with automated driving does not decrease, but rather increase if more driver assistance systems of the maneuver type are implemented. At the same time, the number of automation malfunctions, reported by our interviewees, proved to be much smaller than we expected. In contrast to the assertion of a loss-of-control in highly automated systems, our data will show, that this hypothesis cannot be confirmed, at least not at the level of self-reported personal experiences and subjective perceptions of non-professional users such as car drivers. [ABSTRACT FROM AUTHOR]

The current approach to driving automation has been primarily vehicle-centric. However, a vehicle-infrastructure cooperative approach, in which infrastructure and vehicles cooperate to perform the different driving tasks, may prevail in enabling automated driving. This paper conducts an economic analysis of vehicle infrastructure cooperation for automated driving. In doing so, we present a model that captures investment decisions in vehicle automation and infrastructure digitalization and their effect on travelers' purchase and travel decisions. Our analysis shows that, under certain conditions, equipping both infrastructure and vehicles is socially optimal. However, by analyzing strategic interactions between infrastructure support service providers and automakers, we show that lack of coordination between these two actors results in suboptimal investment in vehicle automation and infrastructure digitalization. Especially, when these two technologies are complementary, service providers are reluctant to invest in digital infrastructure and vehicle manufacturers tend to over equip their vehicles so as to avoid relying on infrastructure technology. Thus, we conclude by showing that better coordination between automakers and service providers – under the form of profit sharing – is welfare-improving and could potentially yield the socially optimal levels of automation and digitalization. • Investment decision on vehicle infrastructure cooperation for driving automation. • Equipping both infrastructure and vehicles is socially optimal. • Lack of coordination results in suboptimal investment. • Profit sharing is welfare-improving. [ABSTRACT FROM AUTHOR]

In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system. [ABSTRACT FROM AUTHOR]

This paper discusses driving system design based on traffic rules. This allows fully automated driving in an environment with human drivers, without necessarily changing equipment on other vehicles or infrastructure. It also facilitates cooperation between the driving system and the host driver during highly automated driving. The concept, referred to as legal safety, is illustrated for highly automated driving on highways with distance keeping, intelligent speed adaptation, and lane-changing functionalities. Requirements by legal safety on perception and control components are discussed. This paper presents the actual design of a legal safety decision component, which predicts object trajectories and calculates optimal subject trajectories. System implementation on automotive electronic control units and results on vehicle and simulator are discussed. [ABSTRACT FROM PUBLISHER]

• We surveyed owners and non-owners (with no firsthand experience) of ACC and LKA. • Drivers who owned ACC and/or LKA were not more aware of system limitations. • Owners had a stronger response bias in favour of system capabilities. • Better knowledge was associated with lower trust among non-owners, but not owners. • Higher trust was associated with higher reliance intention in owners and non-owners. Understanding what drivers know about state-of-the-art advanced driver assistance systems (ADAS), like adaptive cruise control (ACC) and lane keeping assistance (LKA) is important because such knowledge can influence trust in and reliance on the automation. We surveyed ADAS owners (N = 102) and non-owners (N = 262), with the primary objective of assessing knowledge and trust of ACC and LKA, and investigating the relationship between knowledge and trust among drivers who have not received special training. The survey contained demographic questions, ACC and LKA knowledge questionnaires (assessing knowledge of capabilities and limitations commonly found in owner's manuals), and ACC and LKA trust ratings. From the knowledge questionnaires, sensitivity (i.e., knowledge of the true capabilities of ACC and LKA) and response bias were assessed and used to predict trust. Results showed that owners did not have better knowledge of system capabilities/limitations than non-owners, in fact, owners had a stronger bias in favour of system capabilities. For non-owners, better knowledge of system capabilities was associated with lower trust, and those who were more biased towards endorsing system capabilities had higher trust. Neither knowledge nor response bias was associated with trust among owners. Further research is needed to confirm our results with a larger sample of owners, but given that it is also impractical to expect drivers to learn and remember all possible ADAS limitations, it may be beneficial to focus training efforts on improving drivers' overall understanding of the fallibility of ADAS and reinforcing their role when using ADAS to support appropriate trust and reliance. [ABSTRACT FROM AUTHOR]

Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants' experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers' take-over performance, but also enhance user experience during take-over requests from automated to manual driving. [ABSTRACT FROM AUTHOR]

• We tested in-vehicle displays to support driver anticipation in automated vehicles. • TORAC displayed takeover request (TOR) + automation capability (AC) information. • STTORAC displayed surrounding traffic (ST) information in addition to TOR and AC. • STTORAC facilitated, while TORAC impeded anticipation. • TORAC increased automation reliance; STTORAC supported appropriate reliance. This paper investigates the effectiveness of in-vehicle displays in supporting drivers' anticipation of traffic conflicts in automated vehicles (AVs). Providing takeover requests (TORs) along with information on automation capability (AC) has been found effective in supporting AV drivers' reactions to traffic conflicts. However, it is unclear what type of information can support drivers in anticipating traffic conflicts, so they can intervene (pre-event action) or prepare to intervene (pre-event preparation) proactively to avert them. In a driving simulator study with 24 experienced and 24 novice drivers, we evaluated the effectiveness of two in-vehicle displays in supporting anticipatory driving in AVs with adaptive cruise control and lane keeping assistance: TORAC (TOR + AC information) and STTORAC displays (surrounding traffic (ST) information + TOR + AC information). Both displays were evaluated against a baseline display that only showed whether the automation was engaged. Compared to the baseline display, STTORAC led to more anticipatory driving behaviors (pre-event action or pre-event preparation) while TORAC led to less, along with decreased attention to environmental cues that indicated an upcoming event. STTORAC led to the highest level of driving safety, as indicated by minimum gap time for scenarios that required driver intervention, followed by TORAC, and then the baseline display. Providing surrounding traffic information to drivers of AVs, in addition to TORs and automation capability information, can support their anticipation of potential traffic conflicts. Without the surrounding traffic information, drivers can over-rely on displays that provide TORs and automation capability information. [ABSTRACT FROM AUTHOR]

To date, vehicle manufacturers have largely been left to their own initiatives when it comes to the design, development and implementation of automated driving features. Whilst this has enabled developments within the field to accelerate at a rapid pace, we are also now beginning to see the negative aspects of automated design (e.g., driver complacency, automation misuse and ethical dilemmas). It is therefore becoming increasingly important to identify systemic aspects that can address some of these Human Factors challenges. This paper applies the principles of the Risk Management Framework to explore the wider systemic issues associated with automated driving in the United Kingdom through the novel application of network metrics. The authors propose a number of recommendations targeted at each level of the Risk Management Framework that seek to shift the power of influence away from vehicle manufacturers and back into the hands of governing bodies. [ABSTRACT FROM AUTHOR]