Over the last decade it has become increasingly popular to ride a motorcycle in Sweden. A mandatory risk education for competence A and A1 was introduced from the 1st of November 2009. Since the education is new, few evaluations have been conducted so far.

This study evaluates the risk education for motorcyclists based on the perspective of driving instructors. The goal was to compile the instructors' comments and experiences with respect to the new risk training. An additional objective was to examine the driving instructors' perceived effects of the education on their students' driving behavior. Six semi-structured interviews with driving instructors and an observational study of the education at various driving schools were conducted. In addition, a participant observation study was performed during a further education of 15 driving instructors. The results show that the instructors experience a great need for the new risk training and that the implementation of the training itself has been good. Students have been referring to the risk training, which according to the instructors indicate that the students have embraced the essence of the training. As a result, the students drive more carefully now and think twice in certain situations according to the instructors. This was considered a desirable result.

The purpose of this study was to evaluate the VTI train simulator with regard to simulator-based training and education in the ERTMS (European Rail Traffic Management System) system for train drivers, compared to the ERSA simulator. The purpose was also to study two different ways of combining theoretical education with practical training in a simulator. In the future, the major railway routes in Sweden will be equipped with ERTMS. This will require considerable training for Sweden’s approximately 3,500 train drivers in future need of ERTMS education. It is unrealistic to think that these should be trained on real tracks. Therefore, there is a need for a more realistic ERTMS simulator compared to the European Rail Software Applications (ERSA) simulator that Trafikverket provides. The study was conducted as a between group design with two measurements. Three different groups were compared under different conditions. Theoretical education in ERTMS combined with training in the ERSA simulator (group 0), theoretical education in ERTMS ending with training in the VTI-simulator (group 1) and theoretical education in ERTMS partly alternated with training in the VTI-simulator (group 2).

This report, which provides an overview of the compulsory basic training for train drivers in Sweden, highlights the occurrence of simulator-based training in education, along with the training efforts being made with regards to the future introduction of ERTMS/ETCS. The report also shows the possibilities and limitations of increased use of simulators in driver training and describes the most important governing documents for train drivers and train driver training. Furthermore, the Swedish Transport Agency curriculum for train driver licenses is presented along with the institutions engaged in basic education, training and examination of train driver’s. Also, the Swedish Transport Administration’s E-learning tool for ERTMS, the ERSA-simulator and company specific ERTMS education at SJ and Green Cargo are described. Moreover, Swedish train companies’ and educators’ current use and future needs of simulators for train driver training were examined. Examples from other domains where simulators are used in a training context are also presented.

Vehicle crashes lead to huge economic and social consequences, and one non-negligible cause of accident is driver sleepiness. Driver sleepiness analysis based on the monitoring of vehicle acceleration, steering and deviation from the road or physiological and behavioral monitoring of the driver, e.g., monitoring of yawning, head pose, eye blinks and eye closures, electroencephalogram, electrooculogram, electromyogram and electrocardiogram (ECG), have been used as a part of sleepiness alert systems.

Heart rate variability (HRV) is a potential method for monitoring of driver sleepiness. Despite previous positive reports from the use of HRV for sleepiness detection, results are often inconsistent between studies. In this work, we have re-evaluated the feasibility of using HRV for detecting drivers’ sleepiness during real road driving. A database consists of ECG measurements from 10 drivers, driving during morning, afternoon and night sessions on real road were used. Drivers have reported their average sleepiness level by using the Karolinska sleepiness scale once every five minutes. Statistical analysis was performed to evaluate the potential of HRV indexes to distinguish between alert, first signs of sleepiness and severe sleepiness states. The results suggest that individual subjects show different reactions to sleepiness, which produces an individual change in HRV indicators. The results motivate future work for more personalized approaches in sleepiness detection.

The increasing use of mobile phones while cycling has raised safety concerns. In this paper two studies of mobile phone use by cyclists are presented.

The first study was designed to characterize mobile phone use by cyclists in Sweden, while the second studied how mobile phone use affected cyclist behaviour and compensation strategies. Mobile phone use was observed in about 20 percent of all urban bicycle trips. The usage varied with cyclist age with the highest usage among young cyclists. Of those using phones, 90% of the cyclists observed used head-phones.

In parallel, standardized, interviews 15% of cyclists under 15 years old stated that they always used mobile phones while cycling. Listening to music in headphones was the most fre-quent self-reported activity. To converse using hand-held phones was also rather common, and was the only mobile phone usage reported by women above 50 years old.

In the second study twenty-two young cyclists (age 16-25 years) completed a route in real traffic five times while listening to music, receiving and making calls, receiving and sending text messages, searching for information on the internet and while cycling normally without using the phone. The route and the types of tasks were controlled, but the cyclists could choose rather freely when, where and how to carry out the tasks. When the cyclist returned to the starting point, a short interview was conducted. During the interviews cyclists reported their experiences and the compensation strategies they used while cycling.

This chapter describes how acceptance has been measured and identifies various measurement categories. The relationship between these measurement methods and the different definitions of acceptance appearing in the literature is described and the lack of correspondence between definition and measurement is highlighted. The chapter illustrates the different outcomes of acceptance measurements depending on choice of assessment method and gives some guidance that could be used depending on the purpose of the assessment.

This chapter provides a brief overview of acceptance models used within the area of information technology.  One particular model, the Unified Theory of Acceptance and Use of Technology (UTAUT), is then discussed, and a study is reported in which the model was used to assess driver acceptance of a particular driver assistance system. The key findings of that study are reported, and suggestions are made for refining UTAUT to make it more suitable for assessing acceptance of driver assistance systems.

Despite the recognised importance of the concept of acceptance, how and why new technologies are actually accepted by drivers is not well understood. While many studies claim to have measured acceptance, few have explicitly defined what it is. This chapter points out the importance of defining acceptance and categorises definitions that have been used according to their “essence”. Distinctions between different types of acceptance as well as between acceptance and acceptability are also described. A proposal for a common definition of acceptance is then presented and discussed.

The main aim of the DiREC project is developing a common framework to support National Road Authorities (NRAs) to provide better engagement with Original Equipment Manufacturers (OEM) and service providers, identify clearer responsibilities and liabilities, and include tools to calculate the costs and benefits of providing different levels of support to Connected and Automated Vehicles (CAVs). Greater engagement and dialogue are key. By understanding the infrastructure and communications requirements of automated vehicles, and the challenges faced by CAVs in an operational environment, NRAs will be able to strategically plan their networks to support Connected and Automated Driving (CAD) and place themselves in a much stronger position to influence how traffic operates on the network.

A proactive approach to liaising with vehicle manufacturers and service providers will also promote NRA involvement in the services that are developing around digital mapping, localisation, navigation and traffic management. By aligning the digital strategies and plans of the NRAs with the requirements of OEMs and CAVs, and by giving direction to service providers, a common framework for CAD will help achieve major cost efficiencies and facilitate economic transformation. It will help optimise the delivery of infrastructure and communications systems on national road networks in support of CAD implementation whilst helping NRAs maintain their influence over CAD activity. In order to facilitate productive interactions with various stakeholders involved with CAVs, WP1 within the DiREC project is dedicated to stakeholder engagement activities. This will ensure that input from different stakeholder categories will be collected and utilized for CAV-ready framework development.

The DiREC CAV-Readiness Framework (CRF) is a framework that aims to help an NRA assess their capability with respect to the deployment of Connected and Automated Driving (CAD) solutions, their ability to influence the use of CAD on their network via investment in digital and physical infrastructure, and the impacts and outcomes of any investment decision.

DiREC structured the CRF around C-ITS Services and Use Cases as defined under the C-ROADS project. C-ROADS is a joint initiative of European Member States and road operators for testing and implementing C-ITS services, with a desire for cross-border harmonisation and interoperability.The CRF is thus a framework which can be used by NRAs to help assess their aspirations and readiness to support CAD, and to implement individual C-ITS services and use cases. It does this by:

• Defining the C-ITS services to be provided
• Breaking those services down into use cases and enablers
• Scoring the NRAs readiness, aspirations and high-level assessment of costs and impacts of each enabler to help plan and prioritise the NRA support for CAD.

The 2020 CEDR Research Call on the Impact of CAD on Safe Smart Roads had as its aim to “prepare the national road authorities on future challenges of connectivity, digitalization and automation to get to an autonomously well-managed traffic flow.” CEDR cautioned that “ If NRAs do not act proactively, the vehicle manufacturers will determine the automation of traffic flow alone, the NRAs will fall behind and huge investment will be needed to safeguard NRAs’ objectives. NRAs’ goals and roles in the Cooperative, Connected and Automated Mobility of the future must be clear…NRAs need to determine and act before other parties decide in our place where we need to invest.”.

The Digital Road for Evolving Connected and Automated Driving (DiREC) project set out to develop a toolkit for NRAs to use in their assessment and development of their capabilities as digital road operators. This toolkit, referred to as the CAV Readiness Framework (CRF), focuses on five key areasof provision that are central to the development of connected and automated driving capabilities. Those areas being:

• Physical infrastructure
• Digital infrastructure
• Communications infrastructure
• Standards and regulation
• Operational support

DiREC focused on technologies, services and regulatory infrastructure for which NRAs might either have direct responsibility or at least significant influence. Development led by vehicle manufacturers or the regulations governing them were considered to be outside the scope of influence of the NRAs, for the moment at least. Instead, attention was given to those technologies and services that are likely to be generically useful to most or all future CAD solutions. We identified that there is still a considerable diversity of approaches amongst the developers of automated vehicles, with no single technical strategy yet close to being dominant.

The Level of Service (LoS) is a widely employed metric that quantifies the performance and quality of a provided service, utilizing a predetermined scale. In the transportation domain, road capacity (i.e., maximum throughput in a given road section) is the most widely used performance indicator, where the LoS is applied. In road capacity studies, the LoS definition is dependent upon the specific context and facility under examination, such as urban areas or motorways. In urban settings, the criteria typically employed for determining LoS include average travel speed, average travel time, frequency of stops, and delays. Conversely, on motorways, LoS is determined by factors such as vehicle density, traffic speed, and frequency of lane changes (HCM, 2016). Upon specifying the context, the chosen criteria are applied, and threshold requirements are established to categorize the performance and quality under the appropriate LoS. The LoS scale can range from binary levels (e.g., acceptable or unacceptable) to more nuanced scales. For instance, the HCM (2016) employs a six-point scale (A = very good; B = good; C = acceptable; D = bad; E = very bad; F = system breakdown).

For C-ITS services (i.e., information provision), however, the aim is to provide information that are, among other things, accurate and timely to the road users, connected and autonomous vehicles, so they can react accordingly to events on the road network. The CAV-ready framework (CRF) developed in WP3 aims to illustrate the progress of National Road Authorities (NRAs) towards becoming a digital authority, meaning that the NRAs should provide traffic related information (data provision) to its users (connected and autonomous vehicles) that are precise, accurate, and timely. As such, we have defined three distinct LoS categories:

1. Basic: Minimum acceptable performance/quality
2. Enhanced: Not optimal but sufficient performance/quality
3. Advanced: Ideal or best performance/quality

Connected and Automated Driving (CAD) is an important area of digital technology that will bring disruption to individuals, economies, and societies. Most forms of CAD require some level of infrastructure support for their safe operation. Additional infrastructure and services to support CAD have the potential to improve safety even further, and to bring other benefits such as increased efficiency or reduced congestion. However, the infrastructure requirements from Original Equipment Manufacturer (OEMs) are not always clear, and it is difficult for National Road Authorities (NRAs) to predict and plan for the future levels of support needed for CAD given rapidly evolving technology and uncertain projections of future CAD demand. In addition, there is also a need for better dialogue among NRAs, OEMs and service providers to articulate those requirements and to define a roadmap and responsibilities for achieving safe and smart roads through CAD.

The aim of DiREC is to establish a CAV Readiness Framework and a set of toolkits dedicated to CAVs (Connected and Autonomous Vehicles) that incorporates a wide range of components that affect CAD and the ability of highway infrastructure to support it. These components include machine readability of physical infrastructure, digital services, connectivity, in addition to aspects such as governance of the infrastructure and services, and legal and regulatory requirements. Together these components influence the ability of the NRA to become a digital road operator. The DiREC project will thus provide a framework for NRAs, service providers and OEMs to support CAD. It will consolidate and combine standards, research, and recommendations from other projects and extend research into new areas such as creating a common vision for digital twins among NRAs, understanding connectivity and connectivity requirements to support digital services and analysing how these can be met, reviewing the quality management processes around digital data, and documenting existing legal and regulatory frameworks in all areas relating to CAD.

Connected and automated vehicles potentially offer solutions to some key challenges for National Road Administrations (NRAs), such as reduction of accidents, increasing network capacity etc. As a result of this potential, both industry and certain national governments are undertaking trials that are mainly focused on technological challenges such as the ability of vehicles to drive safely in “random” situations etc. Far less attention has been paid to questions around the implications for NRAs. The overall aim of the STAPLE project is to provide a comprehensive review of technological and non-technological aspects of the most relevant connected and automated driving test sites in order to understand the impact of these sites on the NRAs’ core business and functions.

The goal of the project FIMCAR (Frontal Impact and Compatibility Assessment Research) was to define an integrated set of test procedures and associated metrics to assess a vehicle's frontal impact protection, which includes self-And partner-protection. For the development of the set, two different full-width tests (full-width deformable barrier [FWDB] test, full-width rigid barrier test) and three different offset tests (offset deformable barrier [ODB] test, progressive deformable barrier [PDB] test, moveable deformable barrier with the PDB barrier face [MPDB] test) have been investigated. Different compatibility assessment procedures were analysed and metrics for assessing structural interaction (structural alignment, vertical and horizontal load spreading) as well as several promising metrics for the PDB/MPDB barrier were developed.The final assessment approach consists of a combination of the most suitable full-width and offset tests. For the full-width test (FWDB), a metric was developed to address structural alignment based on load cell wall information in the first 40 ms of the test. For the offset test (ODB), the existing ECE R94 was chosen. Within the paper, an overview of the final assessment approach for the frontal impact test procedures and their development is given.

In the European Project FIMCAR, a proposal for a frontal impact test configuration was developed which included an additional full width deformable barrier (FWDB) test. Motivation for the deformable element was partly to measure structural forces as well as to produce a severe crash pulse different from that in the offset test. The objective of this study was to analyse the safety performance of vehicles:

• in the full width rigid barrier test (FWRB) and
• in the full width deformable barrier test (FWDB)

In total, 12 vehicles were crashed in both configurations. Comparison of these tests to real world accident data was used to identify the crash barrier most representative of real world crashes. For all vehicles, the airbag visible times were later in the FWDB configuration. This was attributed to the attenuation of the initial acceleration peak, observed in FWRB tests, by the addition of the deformable element. These findings were in alignment with airbag triggering times seen in real world crash data. Also, the dummy loadings were slightly worse in FWDB compared to FWRB tests, which is possibly linked to the airbag firing and a more realistic loading of the vehicle crash structures in the FWDB configuration. Evaluations of the lower extremities have shown a general increasing of the tibia index with the crash pulse severity.

Many crashes with heavy vehicle can be attributed to driver sleepiness or driving impairment due to sleepiness, and it is important to find methods to predict those situations and counteract this problem. The Optalert fatigue management system claims to be able to detect sleepiness. The aims of this study are to (a) evaluate if Optalert can detect sleepiness equally well as other sleepiness indicators and (b) if the data patterns obtained by Optalert correlates with these other sleepiness indicators. Twelve sleep deprived truck drivers drove for about 90 minutes in an advanced moving base truck simulator. The experimental setup, including the sleep deprivation, was designed so that the drivers should become increasingly sleepier during the trial and the intention was that they should fall asleep during the experiment.

Four different indicators of sleepiness or driving impairment due to sleepiness were used to monitor the state of the drivers; the Karolinska Sleepiness Scale (KSS), the variability in lateral position (SDLP), the blink duration and the Optalert system. The results show that all four sleepiness indicators increased with time on task. An analysis of variance revealed that the changes were significant for KSS, blink duration and the Optalert system, and a correlation analysis showed that Optalert correlated significantly with blink duration and SDLP. However, even though these correlations were significant, they were all rather low with a maximum correlation coefficient of 0.24.

In conclusion, the Optalert system is promising and the sleepiness rating provided by the system works at least equally well as the other three sleepiness indicators. There are some practical limitations to the system; there is no reliable threshold which can be used to determine when a driver is getting too sleepy to drive (this is also the case for other available sleepiness indicators), the driver needs to be attached to the vehicle via the spectacle frames and a wire, and the quality of the eye movement recordings often deteriorated when the driver started driving the truck. Moreover, during the experiment the technical reliability was sometimes low.

All new vehicle types within the European Union must now be equipped with a driver drowsiness and attention warning system starting from 2022. The specific requirements for the test procedure necessary for type approval are defined in the Annex of EU Regulation C/2021/2639. The objectives of this study were to: (i) investigate how sleepiness develops in professional truck drivers under real-road driving conditions; and (ii) assess the feasibility of a test procedure for validating driver drowsiness and attention warning systems according to the EU regulation. Twenty-four professional truck drivers participated in the test. They drove for 180 km on a dual-lane motorway, first during daytime after a normal night's sleep and then at nighttime after being awake since early morning. The results showed higher sleepiness levels during nighttime driving compared with daytime, with a faster increase in sleepiness with distance driven, especially during the night. Psychomotor vigilance task results corroborated these findings. From a driver drowsiness and attention warning testing perspective, the study design with sleep-deprived drivers at night was successful in inducing the targeted sleepiness level of a Karolinska Sleepiness Scale score of ≥ 8. Many drivers who reported a Karolinska Sleepiness Scale ≥ 8 during the drives also acknowledged feeling sleepy in the post-drive questionnaire. Reaching high levels of sleepiness on real roads during daytime is more problematic, not the least from legal and ethical perspectives as higher traffic densities during the daytime lead to increased risks.  

Latent driver sleepiness may in some cases be masked by for example social interaction, stress and physical activity. This short-term modulation of sleepiness may also result from environmental factors, such as when driving in stimulating environments. The aim of this study is to compare two road environments and investigate how they affect driver sleepiness. Thirty young male drivers participated in a driving simulator experiment where they drove two scenarios: a rural environment with winding roads and low traffic density, and a suburban road with higher traffic density and a more built-up roadside environment. The driving task was essentially the same in both scenarios, i.e. to stay on the road, without much interaction with other road users. A 2 x 2 design, with the conditions rural versus suburban, and daytime (full sleep) versus night-time (sleep deprived), was used. The results show that there were only minor effects of the road environment on subjective and physiological indicators of sleepiness. In contrast, there was an increase in subjective sleepiness, longer blink durations and increased EEG alpha content, both due to time on task and to night-time driving. The two road environments differed both in terms of the demand on driver action and of visual load, and the results indicate that action demand is the more important of the two factors. The notion that driver fatigue should be countered in a more stimulating visual environment such as in the city is thus more likely due to increased task demand rather than to a richer visual scenery. This should be investigated in further studies.

Driver sleepiness studies are often carried out with alert drivers during daytime and sleep-deprived drivers during night-time. This design results in a mixture of different factors (e.g. circadian effects, homeostatic effects, light conditions) that may confound the results. The aim of this study was to investigate the effect of light conditions on driver sleepiness. Thirty young male drivers (23.6 ± 1.7 years old) participated in a driving simulator experiment where they drove on a rural road. A 2 × 2 design was used with the conditions daylight versus darkness, and daytime (full sleep) versus night-time (sleep deprived). The results show that light condition had an independent effect on the sleepiness variables. The subjective sleepiness measured by Karolinska Sleepiness Scale was higher, lateral position more left-oriented, speed lower, electroencephalogram alpha and theta higher, and blink durations were longer during darkness. The number of line crossings did not change significantly with light condition. The day/night condition had profound effects on most sleepiness indicators while controlling for light condition. The number of line crossings was higher during night driving, Karolinska Sleepiness Scale was higher, blink durations were longer and speed was lower. There were no significant interactions, indicating that light conditions have an additive effect on sleepiness. In conclusion, Karolinska Sleepiness Scale and blink durations increase primarily with sleep deprivation, but also as an effect of darkness. Line crossings are mainly driven by the need for sleep and the reduced alertness at the circadian nadir. Lane position is, however, more determined by light conditions than by sleepiness.

New simulator models concerning vibration, noise and graphics have been designed and implemented in the VTI Simulator III. The objective of this study is to validate this simulator in terms of road surface realism. Twenty-four drivers participated in the study and drove the same route both in the simulator and on real roads. Three road sections ranging from very smooth to rather uneven were incorporated in the design. The comparison included the objective driving parameter speed as well as subjective parameters from questionnaires and rating scales (evenness, quietness and comfort level). A road section with five speed limit changes was of particular interest in the analyses. No statistically significant difference could be found between the simulator and the car, neither in the parameter speed (in sections with no speed limit changes) nor in the ratings evenness and quietness. Despite similar speed profiles surrounding the speed limit signs, there was a statistically significant difference between the speed in the car and in the simulator, with more rapid accelerations and decelerations in the simulator. The comfort rating was shown to be higher in the car compared to the simulator, but in both cases the general trend showed higher comfort on smoother roads. These results indicate absolute validity for the ratings evenness and quietness, and for the measure speed, and relative validity for comfort and speed surrounding speed limit signs.

This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.

When using eye movements to determine the state of a car driver it is important that the eye tracker is robust, unobtrusive, inexpensive and fully automatic. The objectives of this study are to compare the performance of a one-camera system with a three-camera system and to investigate if the accuracy and availability of the one-camera system is sufficient to monitor driver state. Data from 53 subjects were evaluated and the results indicate that there is not much difference between a single-camera system and a multi-camera system as long as the driver is looking straight ahead. However, with more peripheral gaze directions, the larger coverage that is provided by the additional cameras works in favour of the multi-camera system. © ACM 2010.

Driving and operating a vehicle is to a great extent a visual task. In driver behaviour studies it is therefore important to be able to measure where the driver is looking. Today this can be done unobtrusively and remotely in real-time with camera based eye tracking. The most common remote eye tracking systems use multiple cameras in order to give satisfactory results. However, promising results using only one camera has recently emerged on the market. The main objective of this study is to compare eye tracking systems with one and three cameras, respectively, during various measurement conditions.

A total of 53 participants were enrolled in the study. Data from the two eye trackers were acquired and analysed in terms of availability, accuracy and precision. The results indicate that both availability and accuracy are affected by many different factors. The most important factors are the number of cameras that is used and the angular distance from straight ahead. In the central region (straight ahead) both one-camera and three-camera systems have a high degree of accuracy and availability, but with increasing distance from the central region, the results deteriorate. This effect falls harder upon the one-camera system. Interestingly, there were no significant effects when wearing glasses in either availability or accuracy. There was however an interaction effect between distance and glasses.

Advantages with a one-camera system are that it is cheaper, easier to operate and easier to install in a vehicle. A multi-camera system will, on the other hand, provide higher availability and accuracy for areas that are far from the road centre. A one-camera system is thus mostly suitable for in-vehicle applications such as systems that warn drivers for sleepiness or distraction while multi-camera solutions are preferable for research purposes.

Observer-rated sleepiness (ORS) based on video recordings of the driver’s face is often used when analysing naturalistic driving data. The aim of this study is to investigate if ORS ratings agree with subjective self-reported sleepiness (SRS).

Forty raters assessed 54 video-clips showing drivers with varying levels of sleepiness. The video-clips were recorded during a field experiment focusing on driver sleepiness using the same cameras that are typically used in large-scale field studies. The weak results prompted a second test. Ten human factors researchers made pairwise comparisons of videos showing the same four participants in an alert versus a very sleepy condition. The task was simply to select the video-clip where the driver was sleepy.

The overall average percentage of video segments where ORS and SRS matched was 41 % in Test 1. ORS 0 (alert) and ORS 2 (very sleepy) were easier to score than ORS 1 and it was slightly harder to rate night-time drives. Inter-rater agreement was low, with average Pearson’s r correlations of 0.19 and Krippendorff’s alpha of 0.15. In Test 2, the average Pearson’s r correlations was 0.35 and Krippendorff’s alpha was 0.62. The correspondence between ORS and SRS showed an agreement of 35 %.

The results indicate that ORS ratings based on real road video recordings correspond poorly with SRS and have low inter-rater agreement. Further research is necessary in order to further evaluate the usefulness of ORS as a measure of sleepiness.

The use of mobile phone and similar devices while driving has been a topic of discussion and research for several years. It is now an established fact that driving performance is deteriorated due to distraction but no clear conclusions can yet be drawn concerning influence on crash rates. Better studies on this relationship is needed. Most countries in Europe and many countries elsewhere have introduced different types of bans for handheld devices. Sweden has, however, no such bans. VTI was commissioned by the Swedish Government to outline possible means to reduce the dangerous usage of mobile phones and other communication devices while driving as alternatives to banning. This task was a result of a previous VTI-state-of-the-art review of research on mobile phone and other communication device usage while driving. One of the findings in the review was that bans on handheld phones did not appear to reduce the number of crashes.

Eighteen different countermeasures in three main areas were suggested. (1) Technical solutions such as countermeasures directed towards the infrastructure, the vehicle and the communication device. (2) Education and information, describing different ways to increase knowledge and understanding among stakeholders and different driver categories. (3) Different possibilities for how society, industry and organisations can influence the behaviour of individuals, via policies, rules, recommendations and incentives. Our conclusion is that a combination of different countermeasures is needed – where education and information to the drivers are combined with support and incentives for a safe usage of different communication devices.

This paper presents initial work on a context-dependent driver distraction detection algorithm called AttenD2.0, which extends the original AttenD algorithm with elements from the Minimum Required Attention (MiRA) theory. Central to the original AttenD algorithm is a time buffer which keeps track of how often and for how long the driver looks away from the forward roadway. When the driver looks away the buffer is depleted and when looking back the buffer fills up. If the buffer runs empty the driver is classified as distracted. AttenD2.0 extends this concept by adding multiple buffers, thus integrating situation dependence and visual time-sharing behaviour in a transparent manner. Also, the increment and decrement of the buffers are now controlled by both static requirements (e.g. the presence of an on-ramp increases the need to monitor the sides and the mirrors) as well as dynamic requirements (e.g., reduced speed lowers the need to monitor the speedometer). The algorithm description is generic, but a real-time implementation with concrete values for different parameters is showcased in a driving simulator experiment with 16 bus drivers, where AttenD2.0 was used to ensure that drivers are attentive before taking back control after an automated bus stop docking and depot procedure. The scalability of AttenD2.0 relative to available data sources and the level of vehicle automation is demonstrated. Future work includes expanding the concept to real-world environments by automatically integrating situational information from the vehicles environmental sensing and from digital maps.

City bus drivers spend hours driving under time pressure, in congested traffic and in a monotonous sitting position. This leads to unhealthy working conditions, especially in terms of physical and psychological stress. The aim of this study is to investigate whether an active steering system can alleviate the musculoskeletal stress involved in manoeuvring a bus. Twenty bus drivers drove a city bus equipped with the Volvo dynamic steering (VDS) support system in real traffic. Steering effort was evaluated with electromyography and with a questionnaire. Compared to baseline, VDS significantly reduced the required muscle activity by on average 15–25% while turning, and up to 68% in the part of the manoeuvre requiring maximum effort. The bus drivers believed that VDS will help reduce neck and shoulder problems, and they expressed a desire to have VDS installed in their own bus.

Av resultaten från denna enkät är något av det mest tydliga att saker som funnits i en bil en längre tid, exempelvis att ställa in radio, använda navigationsutrustning och prata i telefon, görs i betydligt högre utsträckning än nyare saker som att skicka meddelanden, surfa på internet eller spela spel. Överlag tycks det vara låg acceptans för de nyare företeelserna och en stor andel av urvalet tycker att det är felaktigt eller olämpligt att hålla på med dessa aktiviteter. Att prata i telefon med handsfree tycks uppfattas av många som säkrare och mer lämpligt än att inte använda det. Detta avspeglas även i inställning till införandet av förbud, där en relativt stor andel accepterar att det ska vara lagligt att använda handsfreefunktioner men att annan användning av kommunikationsutrustning borde förbjudas.

I den andra delen genomfördes en mätning av mobilanvändning under färd genom installerande tav en mobilapp. Den genomförda undersökningen är den första av sitt slag åtminstone i Sverige, så att den, trots vissa brister och begränsningar, kan leverera information som hittills har varit okänt. I detta del diskuteras resultaten, även i förhållande till enkätsvaren, följt av en reflektion över metoden och möjliga förbättringar, som är önskvärda inför en fortsättningsstudie.

The objective of this exploratory study is to investigate if lane departures are associated with local sleep, measured via source-localized electroencephalography (EEG) theta power in the 5-9 Hz frequency range. Thirty participants drove in an advanced driving simulator, resulting in 135 lane departures at high levels of self-reported sleepiness. These lane departures were compared to matching non-departures at the same sleepiness level within the same individual. There was no correspondence between lane departures and global theta activity. However, at the local level an increased risk for lane departures was associated with increased theta content in brain regions related to motor function.

Indicators based on visual time-sharing have been used to investigate drivers' visual behaviour during additional task execution. However, visual time-sharing analyses have been restricted to additional tasks with well-defined temporal start and end points and a dedicated visual target area. We introduce a method to automatically extract visual time-sharing sequences directly from eye tracking data. This facilitates investigations of systems, providing continuous information without well-defined start and end points. Furthermore, it becomes possible to investigate time-sharing behavior with other types of glance targets such as the mirrors. Time-sharing sequences are here extracted based on between-glance durations. If glances to a particular target are separated by less than a time-based threshold value, we assume that they belong to the same information intake event. Our results indicate that a 4-s threshold is appropriate. Examples derived from 12 drivers (about 100 hours of eye tracking data), collected in an on-road investigation of an in-vehicle information system, are provided to illustrate sequence-based analyses. This includes the possibility to investigate human-machine interface designs based on the number of glances in the extracted sequences, and to increase the legibility of transition matrices by deriving them from time-sharing sequences instead of single glances. More object-oriented glance behavior analyses, based on additional sensor and information fusion, are identified as the next future step. This would enable automated extraction of time-sharing sequences not only for targets fixed in the vehicle's coordinate system, but also for environmental and traffic targets that move independently of the driver's vehicle.

While in-vehicle eco-driving support systems have the potential to reduce greenhouse gas emissions and save fuel, they may also distract drivers, especially if the system makes use of a visual interface. The objective of this study is to investigate the visual behaviour of drivers interacting with such a system, implemented on a five-inch screen mounted above the middle console. Ten drivers participated in a real-world, on-road driving study where they drove a route nine times (2 pre-baseline drives, 5 treatment drives, 2 post-baseline drives). The route was 96 km long and consisted of rural roads, urban roads and a dual-lane motorway.

The results show that drivers look at the system for 5–8% of the time, depending on road type, with a glance duration of about 0.6 s, and with 0.05% long glances (>2s) per kilometre. These figures are comparable to what was found for glances to the speedometer in this study. Glance behaviour away from the windscreen is slightly increased in treatment as compared to pre- and post-baseline, mirror glances decreased in treatment and post-baseline compared to pre-baseline, and speedometer glances increased compared to pre-baseline. The eco-driving support system provided continuous information interspersed with additional advice pop-ups (announced by a beep) and feedback pop-ups (no auditory cue). About 20% of sound initiated advice pop-ups were disregarded, and the remaining cases were usually looked at within the first two seconds. About 40% of the feedback pop-ups were disregarded. The amount of glances to the system immediately before the onset of a pop-up was clearly higher for feedback than for advice.

Many incidents and crashes can be attributed to driver distraction, and it is essential to learn how to detectdistraction in order to develop efficient countermeasures. A number of distraction detection algorithms have been developed over the years, and the objective of this paper is to summarize available approaches and to describe these algorithms in a unified framework. The review is limited to real-time algorithms that are intended to detect visual distraction.

Snow was falling heavily when Sarah was driving on a slippery road to her cousin’s country cottage. It was dark outside, and the visibility was poor. She had planned to arrive before sunset, but the rental service had made a mistake, and it took hours before she got her rental car at the airport. It was past midnight now, and after a long day of traveling, Sarah was starting to get sleepy.

Fortunately, there were only 15 km to go, but her eyelids were starting to feel heavy. To stay awake, she put her favorite CD on, turned up the volume, and started to sing along. This seemed to help a little -good- only 10 km to go. This was when Sarah’s phone started ringing, and she awkwardly tried to find the mute button for the car stereo while answering the phone. As she looked up again, she barely caught a glimpse of the red brake lights of the car in front of her as she smashed into it.

Driver distraction is a contributing factor to many crashes; therefore, a real-time distraction warning system should have the potential to mitigate or circumvent many of these crashes. The objective of this paper is to investigate the usefulness of a real-time distraction detection algorithm called AttenD. The evaluation is based on data from an extended field study comprising seven drivers who drove on an average of 4351 ± 2181 km in a naturalistic setting.

Visual behavior was investigated both on a global scale and on a local scale in the surroundings of each warning. An increase in the percentage of glances at the rear-view mirror and a decrease in the amount of glances at the center console were found. The results also show that visual time sharing decreased in duration from 9.94 to 9.20 s due to the warnings, that the time from fully attentive to warning decreased from 3.20 to 3.03 s, and that the time from warning to full attentiveness decreased from 6.02 to 5.46 s. The limited number of participants does not allow any generalizable conclusions, but a trend toward improved visual behavior could be observed. This is a promising start for further improvements of the algorithm and the warning strategy.

Percent road center (PRC) is a performance indicator which is sensitive to driver distraction. The original definition of PRC is based on fixation data extracted from eye movement recordings, but it has also been suggested that PRC can be determined directly from the gaze data without segmenting it into saccades and fixations. The primary aim of this paper is to investigate if this is the case.

Naturalistic driving data from a small scale field operational test comprising seven vehicles was used in the evaluation. It was found that PRC time traces based on gaze data and fixation data, respectively, were highly similar (correlation coefficient=0.95, average wavelet semblance=0.84) except for an absolute amplitude difference of about 8%. This indicates that the two approaches can be used interchangeably and that the processing step of segmenting gaze data into saccades and fixations can be left out.

In addition to this finding, design issues related to the calculation of PRC are investigated. Especially, the impact of gaze cases pointing towards the intersection of the road centre area and the centre rear mirror were investigated. Results lead to conclude that gazes and fixations on the centre rear mirror should be removed from the PRC calculations, as they may negatively influence the correctness of the performance indicator.

Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their purpose. The main idea is to consider not only the intention behind each glance, but to also account for what is relevant in the surrounding scene, regardless of whether the driver has looked there or not. In essence, the old approaches, unaware as they are of the larger context or motivation behind eye movements, have taken us as far as they can. We propose this more integrative approach to gain a better understanding of the complexity of drivers' informational needs and how they satisfy them in the moment.

A driving simulator study was conducted by using the same setup in two driving simulators, one with a moving base and one with a fixed base. The aim of the study was to investigate a selection of commonly used performance indicators (PIs) for their sensitivity to secondary tasks loading on different modalities and levels of difficulty, and to evaluate their robustness across simulator platforms. The results showed that, across platforms, the longitudinal PIs behaved similarly whereas the lateral control and eye movement based performance indicators differed. For modality, there were considerable effects on lateral, longitudinal as well as eye movement PIs. However, there were only limited differences between the baseline and the cognitive and haptic tasks. For difficulty, clear effects on PIs related to lateral control and eye movements were shown. Additionally, it should be noted that there were large individual differences for several of the PIs. In conclusion, many of the most commonly used PIs are susceptible to individual differences, and, especially the PIs for lateral control, to the platform and environment where they are acquired, which is why generalizations should be made with caution.

Visual distraction among cyclists interacting with their mobile phones is a growing concern. Yet, very little research has actually investigated how cyclists apply visual strategies and adapt task execution depending on the traffic situation. The aim of this study is to investigate visual behaviour of cyclists when conducting self-paced (initiated by the cyclist) vs. system-paced (initiated by somebody else) smartphone tasks in traffic. Twenty-two cyclists completed a track in real traffic while listening to music, receiving and making calls, receiving and sending text messages, and searching for information on the internet. The route and the types of tasks were controlled, but the cyclists could choose rather freely when and where along the route to carry out the tasks, thus providing semi-naturalistic data on compensatory behaviour. The results show that the baseline and music conditions were similar in terms of visual behaviour. When interacting with the phone, it was found that glances towards the phone mostly came at the expense of glances towards traffic irrelevant gaze targets and also led to shortened glance durations to traffic relevant gaze targets, while maintaining the number of glances. This indicates that visual “spare capacity” is used for the execution of the telephone tasks. The task type influenced the overall task duration and the overall glance intensity towards the phone, but not the mean nor maximum duration of individual glances. Task pacing was the factor that influenced visual behaviour the most, with longer mean and maximum glance durations for self-paced tasks. In conclusion, the cyclists used visual strategies to integrate the handling of mobile phones into their cycling behaviour. Glances directed towards the phone did not lead to traffic relevant gaze targets being missed. In system-paced scenarios, the cyclists checked the traffic more frequently and intensively than in self-paced tasks. This leads to the assumption that cyclists prepare for self-initiated tasks by for example choosing a suitable location. Future research should investigate whether these strategies also exists amongst drivers and other road user groups.

To successfully get around in traffic it is often necessary to keep track of several relevant targets at the same time. This can be done by combining foveal and peripheral visual information sampling. Especially if no detailed input is needed, for example when confirming the absence of road users, it may be enough to use peripheral vision only. Using a driving simulator with an urban scenery, 35 participants passed three zebra crossings with a) no pedestrians nearby, b) pedestrians standing nearby and c) pedestrians nearby of whom one started walking towards the street. In the last case, all participants foveated the walking person, albeit around one third of the participants already released the throttle before the first glance at the pedestrian. The standing pedestrians were foveated in almost all instances, whereas the roadside nearby the zebra crossing without people nearby was not foveated by around a quarter of the participants. Taken together, the results indicate that peripheral vision may suffice to confirm the absence or presence of pedestrians. With people present, a glance towards them is initiated, likely to check for additional information. Throttle release before foveation is an indication that the walking pedestrian was detected as relevant with peripheral vision.

Hours of service regulations govern the working hours of commercial motor vehicle drivers, but these regulations may become more flexible as highly automated vehicles have the potential to afford periods of in-cab rest or even sleep while the vehicle is moving. A prerequisite is robust continuous monitoring of when the driver is resting (to account for reduced time on task) or sleeping (to account for the reduced physiological drive to sleep). The overall aims of this paper are to raise a discussion of whether it is possible to obtain successful rest during automated driving, and to present initial work on a hypothetical data driven algorithm aimed to estimate if it is possible to gain driving time after resting under fully automated driving. The presented algorithm consists of four central components, a heart rate-based relaxation detection algorithm, a camera-based sleep detection algorithm, a fatigue modelling component taking time awake, time of day and time on task into account, and a component that estimates gained driving time. Real-time assessment of driver fitness is complicated, especially when it comes to the recuperative value of in-cab sleep and rest, as it depends on sleep quality, time of day, homeostatic sleep pressure and on the activities that are carried out while resting. The monotony that characterizes for long-haul truck driving is clearly interrupted for a while, but the long-term consequences of extended driving times, including user acceptance of the key stakeholders, requires further research.

Driver sleepiness contributes to a considerable proportion of road accidents, and a fit-for-duty test able to measure a driver’s sleepiness level might improve traffic safety. The aim of this study was to develop a fit-for-duty test based on eye movement measurements and on the sleep/wake predictor model (SWP, which predicts the sleepiness level) and evaluate the ability to predict severe sleepiness during real road driving. Twenty-four drivers participated in an experimental study which took place partly in the laboratory, where the fit-for-duty data were acquired, and partly on the road, where the drivers sleepiness was assessed. A series of four measurements were conducted over a 24-h period during different stages of sleepiness. Two separate analyses were performed; a variance analysis and a feature selection followed by classification analysis. In the first analysis it was found that the SWP and several eye movement features involving anti-saccades, pro-saccades, smooth pursuit, pupillometry and fixation stability varied significantly with different stages of sleep deprivation. In the second analysis, a feature set was determined based on floating forward selection. The correlation coefficient between a linear combination of the acquired features and subjective sleepiness (Karolinska sleepiness scale, KSS) was found to be R=. 0.73 and the correct classification rate of drivers who reached high levels of sleepiness (KSS ≥ 8) in the subsequent driving session was 82.4% (sensitivity = 80.0%, specificity = 84.2% and AUC = 0.86). Future improvements of a fit-for-duty test should focus on how to account for individual differences and situational/contextual factors in the test, and whether it is possible to maintain high sensitive/specificity with a shorter test that can be used in a real-life environment, e.g. on professional drivers. © 2012 Elsevier Ltd.

The effects of driver sleepiness are often quantified as deteriorated driving performance, increased blink durations and high levels of subjective sleepiness. Driver sleepiness has also been associated with increasing levels of electroencephalogram (EEG) power, especially in the alpha range. The present exploratory study investigated a new measure of driver sleepiness, the EEG fixation-related lambda response. Thirty young male drivers (23.6±1.7years old) participated in a driving simulator experiment in which they drove on rural and suburban roads in simulated daylight versus darkness during both the daytime (full sleep) and night-time (sleep deprived). The results show lower lambda responses during night driving and with longer time on task, indicating that sleep deprivation and time on task cause a general decrement in cortical responsiveness to incoming visual stimuli. Levels of subjective sleepiness and line crossings were higher under the same conditions. Furthermore, results of a linear mixed-effects model showed that low lambda responses are associated with high subjective sleepiness and more line crossings. We suggest that the fixation-related lambda response can be used to investigate driving impairment induced by sleep deprivation while driving and that, after further refinement, it may be useful as an objective measure of driver sleepiness.

Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task. © 2011 IEEE.

The prevalence of mobile phone usage in traffic has been studied by road-side counting, naturalistic driving data, surveillance cameras, smartphone logging, and subjective estimates via surveys. Here, we describe a custom-made smartphone logging application along with suggestions on how future such applications should be designed. The developed application logs’ start and end times of all phone interactions (mobile phone applications, incoming/outgoing phone calls and text messages, audio output, and screen activations). In addition, all movements are automatically classified into transport, cycling, walking, running, or stationary. The capabilities of the approach are demonstrated in a pilot study with 143 participants. Examples of results that can be gained from smartphone logging include prevalence in different transportation modes (here found to be 12% while driving, 4% while cycling, and 7% while walking), which apps are being used (here found to be 19% navigation, 12% talking, 12% social media, and 10% games) and on which road types (rural, urban, highway etc.). Smartphone logging was found to be an insightful complement to the other methods for assessing phone use in traffic, especially since it allows the analyses of which apps are used and where they are used, split into transportation mode and road type, all at a relatively low cost.

Assisted and automated driving brings new challenges and opportunities when it comes to driver fatigue. With lower levels of vehicle automation, driver monotony and boredom in combination with demanding attentive monitoring leads to higher levels of fatigue, especially during the night when the sleep pressure is high. With higher levels of vehicle automation, when the driver is not required to continuously monitor the roadway and the automation system, task-related fatigue can be counteracted by engaging in non-driving-related activities. Finally, with the highest levels of vehicle automation, it may even become possible for drivers to sleep while on the move. Aside from making it possible for private car drivers to take strategic naps during a drive, this also opens up the possibility for more flexible, risk management-based, hour of service regulations for professional drivers. This chapter summarises the current state of the art on how assisted and piloted driving affects driver fatigue and how automation may facilitate recovery and recuperation from fatigue while on the move. It also covers how automated functions will impact driver monitoring systems and how new ways of counteracting driver fatigue may arise when automation is available.

While the negative effects of alcohol on driving performance are undisputed, it is unclear how driver attention, eye movements and visual information sampling are affected by alcohol consumption. A simulator study with 35 participants was conducted to investigate whether and how a driver's level of attention is related to self-paced non-driving related task (NDRT)-engagement and tactical aspects of undesirable driver behaviour under increasing levels of breath alcohol concentration (BrAC) up to 1.0 ‰. Increasing BrAC levels lead to more frequent speeding, short time headways and weaving, and higher NDRT engagement. Instantaneous distraction events become more frequent, with more and longer glances to the NDRT, and a general decline in visual attention to the forward roadway. With alcohol, the compensatory behaviour that is typically seen when drivers engage in NDRTs did not appear. These findings support the theory that alcohol reduces the ability to shift attention between multiple tasks. To conclude, the independent reduction in safety margins in combination with impaired attention and an increased willingness to engage in NDRTs is likely the reason behind increased crash risk when driving under the influence of alcohol. © 2023

The objective of this study was to compare the development of sleepiness during manual driving versus level 2 partially automated driving, when driving on a motorway in Sweden. The hypothesis was that partially automated driving will lead to higher levels of fatigue due to underload. Eighty-nine drivers were included in the study using a 2 × 2 design with the conditions manual versus partially automated driving and daytime (full sleep) versus night-time (sleep deprived). The results showed that night-time driving led to markedly increased levels of sleepiness in terms of subjective sleepiness ratings, blink durations, PERCLOS, pupil diameter and heart rate. Partially automated driving led to slightly higher subjective sleepiness ratings, longer blink durations, decreased pupil diameter, slower heart rate, and higher EEG alpha and theta activity. However, elevated levels of sleepiness mainly arose from the night-time drives when the sleep pressure was high. During daytime, when the drivers were alert, partially automated driving had little or no detrimental effects on driver fatigue. Whether the negative effects of increased sleepiness during partially automated driving can be compensated by the positive effects of lateral and longitudinal driving support needs to be investigated in further studies. © 2021 The Author(s)

The current deliverable aims at presenting the results of the analysis of stakeholder needs, in order to have support for selecting the most relevant use cases. For the identification of user requirements of all stakeholders relevant to school transportation different methods were used:

- Focus groups with representatives of one user or stakeholder group

- Workshops with different stakeholders

- 2 questionnaire surveys (Questionaire A: Road Experts, Questionaire B: children, parents and bus drivers)

Problems and needs in the following areas were subject of discussions, interviews and questionnaires:

- Behaviour of road users counteracting with school buses

- Behaviour of pupils on the school bus and while entering and exiting the same

- Design of bus stops

- Protection of pupils on the school bus

- Condition of school buses

- Education of school bus drivers

- Education of pupils concerning school transportation

- Information flow

- Route to/from school

- Special needs of children with disabilities

Results show that the organisation of school transport varies between countries and even within a country. Stakeholders underline the importance of consistent regulations and clear responsibilities as a basis for a safe way to school by bus.