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For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model’s parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator.

There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic. These models focus their learning on low-dimensional error metrics, for example average distance between model-generated and observed trajectories. Such metrics permit relative comparison of models, but do not provide clearly interpretable information on how close to human behavior the models actually come, for example in terms of higher-level behavior phenomena that are known to be present in human driving. We study highway driving as an example scenario, and introduce metrics to quantitatively demonstrate the presence, in a naturalistic dataset, of two familiar behavioral phenomena: (1) The kinematics-dependent contest, between on-highway and on-ramp vehicles, of who passes the merging point first. (2) Courtesy lane changes away from the outermost lane, to leave space for a merging vehicle. Applying the exact same metrics to the output of a state-of-the-art machine-learned model, we show that the model is capable of reproducing the former phenomenon, but not the latter. We argue that this type of behavioral analysis provides information that is not available from conventional model-fitting metrics, and that it may be useful to analyze (and possibly fit) models also based on these types of behavioral criteria., This paper has been submitted to 24th IEEE International Conference on Intelligent Transportation - ITSC2021, September 19-22, 2021 Indianapolis, IN, United States

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.

Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate–the visual looming–of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms., Author summary Evidence accumulation models of decision-making propose that humans accumulate noisy sensory evidence over time up to a decision threshold. We demonstrate that this type of model can describe human behavior well not only in abstract, semi-static laboratory tasks, but also in a task that is relevant to human movement in the real world. Specifically, we show that a model directly accumulating the continuously time-varying visual looming (optical expansion) of an approaching obstacle explains full probability distributions of when humans can detect this collision threat. Using electroencephalography, we find indications that this type of evidence is accumulated differently in the brain compared to evidence accumulation in previously studied, more abstract tasks. Our experimental paradigm, model, and findings open for wider application of this type of decision-making model to laboratory and real-world tasks with ecologically relevant, time-varying sensory evidence, and further studies into how such decisions are implemented neurally. There are also societal implications: In applied safety research and traffic accident litigation it is conventionally assumed that human collision detection is limited by a fixed perceptual threshold, an assumption that our results show to be highly inaccurate.

School engagement has been shown to protect students from dropping out of education, depression and school burnout. The aim of this Finnish study was to explore the association between child-parent relationships and how much 99,686 children aged 9–11 years liked school. The data were based on the 2019 School Health Promotion Study, conducted by the Finnish Institute for Health and Welfare. This asked children whether they liked school or not and about their child-parent relationships. Univariate and multivariate analyses were used to examine the data separately for boys and girls and the results are presented as odds ratios (OR) and 95% confidence intervals (CI). According to the results, girls showed more school engagement than boys (81.9% versus 74.0%), and it was more common in children who felt that their parents communicated with them in a supportive way. This association was slightly stronger for girls than boys (OR 2.46 95% CI 2.33–2.59 versus OR 2.10 95% CI 2.02–2.20). It is important that child-parent relationships and communication are considered during school health examinations, so that children who have lower support at home can be identified. publishedVersion

Este trabalho apresenta uma apreciação teórica da aplicabilidade de modelos de tomada de decisão de risco como uma ferramenta para entender o comportamento de motoristas durante retomadas de controle de um veículo automatizado. O artigo se foca na relação entre o conceito de “Out of the Loop” e consciência da situação. Uma discussão metodológica é feita, e suas implicaçóes para o design de produtos é apresentada. Ao fim da discussão, este artigo conclui que o processo de acumulação de evidência em modelos de tomada de decisão possui paralelos fortes com o conceito de retomada de consciência da situação. Dito isto, modelos de acumulação de evidência podem ser tuilizados como ferramentas para entender como motoristas usam a informação para tomar decisões seguras, e esta informação pode ser reforçada no design de interfaces embarcadas. Ao fim do artigo, um modelo conceitual é apresentado como sugestão para aplicação prática da teoria proposta em dados experimentais.

The surface activity of hot-dip galvanized steel was evaluated after alkaline cleaning by exposing the cleaned specimens in humid supercritical carbon dioxide, followed by extraction and quantification of the formed corrosion products. Different free alkalinities of the cleaning bath were studied to obtain information on the evolution of zinc surface activity at different levels of surface etching. Surface reactivity of uncleaned galvanized steel is restricted and local, with major contributions from grain boundaries and intermetallic particles. Formation of zinc corrosion products took place in uncleaned samples exclusively on and around these sites. Al2O3 removal by alkaline cleaning gradually increased the surface activity. The major increase in surface activity was achieved when increasing the free alkalinity from 0.5 to 4.0 mEq/L, which was shown by formation of zinc corrosion products within zinc grains. The surface activity was confirmed by applying a titanium hexafluoride pretreatment on the cleaned panels and measuring the lateral microscale uniformity of the formed layer. The uniformity increased when larger areas of galvanized steel became activated by alkaline cleaning. Reactivity of zinc is rather difficult to quantify when setting up an industrial cleaning sequence for galvanized steel. Controlled exposure of cleaned samples in humid supercritical carbon dioxide, followed by quantification of oxidized zinc, provides a straightforward method to evaluate zinc reactivity.

Context Software startups develop innovative products through which they scale their business rapidly, and thus, provide value to the economy, including job generation. However, most startups fail within two years of their launch because of a poor problem-solution fit and negligence of the learning process during minimum viable product (MVP) development. An ideal startup ecosystem can assist in MVP development by providing the necessary entrepreneurial education and technical skills to founding team members for identifying problem-solution fit for their product idea, allowing them to find the right product-market fit. However, existing knowledge on the effect of the startup ecosystem elements on the MVP development is limited. Objective The empirical study presented in this article aims to identify the effect of the six ecosystem elements (entrepreneurs, technology, market, support factors, finance, and human capital) on MVP development. Method We conducted a study with 13 software startups and five supporting organizations (accelerators, incubator, co-working space, and investment firm) in the startup ecosystem of the city of Oulu in Finland. Data were collected through semi-structured interviews, observation, and materials. Results The study results showed that internal sources are most common for identifying requirements for the product idea for MVP development. The findings indicate that supporting factors, such as incubators and accelerators, can influence MVP development by providing young founders with the necessary entrepreneurship skills and education needed to create the right product-market fit. Conclusions We conclude from this study of a regional startup ecosystem that the MVP development process is most affected by founding team members’ experiences and skill sets and by advanced technologies. Furthermore, a constructive startup ecosystem around software startups can boost up the creation of an effective MVP to test product ideas and find a product-market fit.

Listeria monocytogenes causes the foodborne illness listeriosis, which exhibits high fatality among people in risk groups. The incidence of listeriosis has increased in Europe, which raises concerns about L. monocytogenes occurrence in foodstuffs. Ready-to-eat seafood products are considered particularly risky vehicles. Poor hygiene at processing facilities predisposes them to L. monocytogenes contamination, which can be controlled by stringent self-checking system measures. We examined the association of fish-processing plant operational and hygiene practices with the occurrence of L. monocytogenes in vacuum-packaged gravad (cold-salted) and cold-smoked salmon and rainbow trout products. Product sampling of 21 fish-processing plants was carried out, and operational procedures relating to L. monocytogenes control were surveyed using an in-depth risk assessment questionnaire. L. monocytogenes occurred only in sliced and mainly in gravad products of seven fish-processing plants. Shortages in preventive measures were discovered predominantly among the L. monocytogenes positive fish-processing plants. Using generalized linear modeling, we identified the following features associated with L. monocytogenes product contamination: the number of processing machines, deficiencies in the processing environment and machinery sanitation, and staff movement from areas of low toward high hygiene. Furthermore, performing frequent periodic thorough sanitation alongside everyday sanitation practices associated with a decreased risk of product contamination.

Driving simulators can be used to test vehicle designs earlier, prior to building physical prototypes. One area of particular interest is winter testing since testing is limited to specific times of year and specific regions in the world. To ensure that the simulator is fit for purpose, an objective assessment is required. In this study a simulator and real world comparison was performed with three simulator configurations (standard, no steering torque, no motion) to assess the ability of a utility triplet of analyses to be able to quantify the differences between the real world and the different simulator configurations. The results suggest that the utility triplet is effective in measuring the differences in simulator configurations and that the developed “Virtual Sweden” environment achieved rather good behavioural fidelity in the sense of preserving absolute levels of many measures of behaviour. The main limitation in the simulated environment seemed to be the poor match of the dynamic lateral friction limit on snow and ice when compared to the real world.

The leading cause of death of adolescents in developed countries is injury. Alcohol is a major contributor to adolescent injury. Most of the injury deaths in youth are caused by traffic crashes. Dr...

Objective\ud \ud The paper presents a systematic analysis of drivers’ crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context.\ud \ud \ud \ud Methods\ud \ud We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers’ gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers’ pre-incident behavior (secondary behavior, gaze behavior), and drivers’ perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver.\ud \ud \ud \ud Results\ud \ud The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers’ last glance duration before the event, last glance eccentricity, duration of ‘eyes on road’ immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers’ maneuver intention.\ud \ud \ud \ud Conclusions\ud \ud In this paper we analyzed driving context, drivers’ behavior, event criticality, and drivers’ response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers’ maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.

We studied ice-nucleating abilities of particulate emissions from a modern heavy-duty diesel engine using three different types of fuel. The polydisperse particle emissions were sampled during engine operation and introduced to a continuous-flow diffusion chamber (CFDC) instrument at a constant relative humidity RHwater = 110 %, and temperature was ramped between −43 °C and −32 °C (T-scan). The tested fuels were EN 590 compliant low-sulfur fossil diesel, hydrotreated vegetable oil (HVO) and rapeseed methyl ester (RME), and all were investigated without blending. Sampling was carried out at different stages in the engine exhaust after-treatment system, with and without simulated atmospheric processing using an oxidation flow reactor. In addition to ice-nucleation experiments, we used supportive instrumentation to characterize the emission particles and present six different physical and chemical properties of them. We found that the studied emissions were poor ice-nucleators and substitution of fossil diesel with renewable fuels, using different emission after-treatment systems and photochemical aging of total exhaust had only little effect on their ice-nucleating abilities.

Several time synchronous ad hoc networks may operate in the same geographical area. Although network time synchronization within an individual network has been widely discussed, internetwork time synchronization relying on time sources the networks carry has not been addressed. Therefore, this paper proposes an internetwork time synchronization procedure that allows to use one network as a time source and share its time, like a global navigation satellite system time, to other networks. The internetwork time synchronization procedure runs in gateway nodes that could be a member in several networks, and it collaborates with the individual network time synchronization algorithms that operate at the network level. Consequently, the paper proposes a generic, robust hybrid network time synchronization protocol and analyses its performance using a sensible metric called a mutual consensus. It is shown that the common time is achieved with a limited variance in the networks that operate in the master-slave, distributed or hybrid mode. Provided numerical examples illustrate the behavior of the algorithm and confirm the theoretical findings.

Automated vehicles (AVs) will change the role of the driver, from actively controlling the vehicle to primarily monitoring it. Removing the driver from the control loop could fundamentally change the way that drivers sample visual information from the scene, and in particular, alter the gaze patterns generated when under AV control. To better understand how automation affects gaze patterns this experiment used tightly controlled experimental conditions with a series of transitions from ‘Manual’ control to ‘Automated’ vehicle control. Automated trials were produced using either a ‘Replay’ of the driver’s own steering trajectories or standard ‘Stock’ trials that were identical for all participants. Gaze patterns produced during Manual and Automated conditions were recorded and compared. Overall the gaze patterns across conditions were very similar, but detailed analysis shows that drivers looked slightly further ahead (increased gaze time headway) during Automation with only small differences between Stock and Replay trials. A novel mixture modelling method decomposed gaze patterns into two distinct categories and revealed that the gaze time headway increased during Automation. Further analyses revealed that while there was a general shift to look further ahead (and fixate the bend entry earlier) when under automated vehicle control, similar waypoint-tracking gaze patterns were produced during Manual driving and Automation. The consistency of gaze patterns across driving modes suggests that active-gaze models (developed for manual driving) might be useful for monitoring driver engagement during Automated driving, with deviations in gaze behaviour from what would be expected during manual control potentially indicating that a driver is not closely monitoring the automated system.

Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.

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Highly automated vehicles (HAVs) will need to interact with pedestrians in a safe and efficient way. Thus, investigating and modeling vehicle-pedestrian interactions at uncontrolled locations is essential to ensure safety and acceptance of these vehicles. Controlled studies are a valuable tool for these scenarios where all the tasks are not possible to be done in the real world and where some variables should be controlled with high accuracy for the development of models of human behavior. In this paper, a game-theoretic model was tested using data from a distributed simulator study. The study was conducted by connecting a desktop driving simulator to a CAVE-based pedestrian lab, providing a safe environment for testing the model’s ability to capture the gap acceptance behavior of pedestrians when interacting with a Human-Driven (HD) or an Automated Vehicle (AV). The results showed that, overall, the model could capture pedestrian behavior well and the pedestrians had lower crossing probabilities in front of the AV. This was seemingly due to differences in vehicle kinematics. Further analysis of the pedestrians’ data revealed the importance of given instructions to the participants in these types of studies. Lessons learned through this study were also used to suggest further ideas on how to design controlled studies for game-theoretic modelling.

Declining ATM numbers pose a challenge for competition policy and financial regulatory authorities. In this report we review the Finnish experience of regulating the competition in the ATM industry. To analyze the Finnish developments we extend the model of Kopsakangas-Savolainen and Takalo (2014), and draw on the existing literature and benchmarks from the selected other countries. We document how changes in the ATM market regulation and market structure has decoupled the ATM network size from the declining cash use in Finland. The Finnish regulation has almost exclusively focused on foreign fees, while in general it would be better to regulate interchange fees. If the optimal fee regulation is not feasible, the authorities could also consider quantity regulation.

Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on the concept of sensorimotor control, we develop a modeling framework applying the principles of utility maximization, motor primitives, and intermittent action decisions to account for the details of interactive behaviors among road users. The framework connects these principles to the decision theory and is applied to determine whether such an approach can reproduce the following phenomena: When two pedestrians travel on crossing paths, (a) their interaction is sensitive to initial asymmetries, and (b) based on which, they rapidly resolve collision conflict by adapting their behaviors. When a pedestrian crosses the road while facing an approaching car, (c) either road user yields to the other to resolve their conflict, akin to the pedestrian interaction, and (d) the outcome reveals a specific situational kinematics, associated with the nature of vehicle acceleration. We show that these phenomena emerge naturally from our modeling framework when the model can evolve its parameters as a consequence of the situations. We believe that the modeling framework and phenomenon-centered analysis offer promising tools to understand road user interactions. We conclude with a discussion on how the model can be instrumental in studying the safety-critical situations when including other variables in road-user interactions., Comment: 10 pages, 7 figures

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