How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models.

OBJECTIVE: This paper aims to describe and test novel computational driver models, predicting drivers' brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). BACKGROUND: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. METHOD: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers' arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. RESULTS: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. CONCLUSION: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. APPLICATION: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures.

OBJECTIVE: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. BACKGROUND: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. METHOD: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. RESULTS: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. CONCLUSION: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. APPLICATION: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

Effects of Cognitive Load on Driving Performance: The Cognitive Control Hypothesis.

OBJECTIVE: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. BACKGROUND: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. METHOD: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. RESULTS: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. CONCLUSION: Effects of cognitive load on driving are strongly selective and task dependent. APPLICATION: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.

A Review of Research on Driving Styles and Road Safety.

OBJECTIVE: The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety. BACKGROUND: Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking. METHOD: A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety. RESULTS: A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and sociocultural factors influencing driving style were reviewed. CONCLUSION: Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations. There is a considerable potential for increasing road safety by means of behavior modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles. APPLICATION: Knowledge about driving styles can be applied in programs for modifying driver behavior and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems.

Modeling road user response timing in naturalistic traffic conflicts: A surprise-based framework.

There is currently no established method for evaluating human response timing across a range of naturalistic traffic conflict types. Traditional notions derived from controlled experiments, such as perception-response time, fail to account for the situation-dependency of human responses and offer no clear way to define the stimulus in many common traffic conflict scenarios. As a result, they are not well suited for application in naturalistic settings. We present a novel framework for measuring and modeling response times in naturalistic traffic conflicts applicable to automated driving systems as well as other traffic safety domains. The framework suggests that response timing must be understood relative to the subject's current (prior) belief and is always embedded in, and dependent on, the dynamically evolving situation. The response process is modeled as a belief update process driven by perceived violations to this prior belief, that is, by surprising stimuli. The framework resolves two key limitations with traditional notions of response time when applied in naturalistic scenarios: (1) The strong situation dependence of response timing and (2) how to unambiguously define the stimulus. Resolving these issues is a challenge that must be addressed by any response timing model intended to be applied in naturalistic traffic conflicts. We show how the framework can be implemented by means of a relatively simple heuristic model fit to naturalistic human response data from real crashes and near crashes from the SHRP2 dataset and discuss how it is, in principle, generalizable to any traffic conflict scenario. We also discuss how the response timing framework can be implemented computationally based on evidence accumulation enhanced by machine learning-based generative models and the information-theoretic concept of surprise.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Gyrolab Immunoassays: Miniaturization, Automation, and Integration into a Rapid Workflow.

Gyrolab® is an open immunoassay platform that automates the complete immunoassay protocol in a microfluidic disc. The column profiles generated with Gyrolab immunoassays are used to gain more information about biomolecular interactions that can be useful in assay development or quantify analytes in samples. Gyrolab immunoassays can be used to cover a broad concentration range and diversity of matrices in applications ranging from biomarker monitoring, pharmacodynamics and pharmacokinetics studies, to bioprocess development in many areas, including therapeutic antibodies, vaccines, and cell and gene therapy.This chapter is an overview of Gyrolab technology, including system components and the assay development workflow, including the process of selecting affinity reagents, Gyrolab Bioaffy CDs, and assay conditions to optimize immunoassays. Two case studies are included. The first involves an assay for the humanized antibody pembrolizumab used in cancer immunotherapy that can generate data for pharmacokinetics studies. The second case study involves quantification of the biomarker and biotherapeutic interleukin-2 (IL-2) in human serum and buffer. IL-2 has been implicated in the cytokine storm associated with COVID-19, and cytokine release syndrome (CRS), which can occur during chimeric antigen receptor T cell (CART) therapy used in treating cancer. These molecules also have therapeutic relevance in combination.

Factors contributing to commercial vehicle rear-end conflicts in China: A study using on-board event data recorders.

INTRODUCTION: In the last 30years, China has undergone a dramatic increase in vehicle ownership and a resulting escalation in the number of road crashes. Although crash figures are decreasing today, they remain high; it is therefore important to investigate crash causation mechanisms to further improve road safety in China. METHOD: To shed more light on the topic, naturalistic driving data was collected in Shanghai as part of the evaluation of a behavior-based safety service. The data collection included instrumenting 47 vehicles belonging to a commercial fleet with data acquisition systems. From the overall sample, 91 rear-end crash or near-crash (CNC) events, triggered by 24 drivers, were used in the analysis. The CNC were annotated by three researchers, through an expert assessment methodology based on videos and kinematic variables. RESULTS: The results show that the main factor behind the rear-end CNC was the adoption of very small safety margins. In contrast to results from previous studies in the US, the following vehicles' drivers typically had their eyes on the road and reacted quickly in response to the evolving conflict in most events. When delayed reactions occurred, they were mainly due to driving-related visual scanning mismatches (e.g., mirror checks) rather than visual distraction. Finally, the study identified four main conflict scenarios that represent the typical development of rear-end conflicts in this data. CONCLUSIONS: The findings of this study have several practical applications, such as informing the specifications of in-vehicle safety measures and automated driving and providing input into the design of coaching/training procedures to improve the driving habits of drivers.

A farewell to brake reaction times? Kinematics-dependent brake response in naturalistic rear-end emergencies.

Driver braking behavior was analyzed using time-series recordings from naturalistic rear-end conflicts (116 crashes and 241 near-crashes), including events with and without visual distraction among drivers of cars, heavy trucks, and buses. A simple piecewise linear model could be successfully fitted, per event, to the observed driver decelerations, allowing a detailed elucidation of when drivers initiated braking and how they controlled it. Most notably, it was found that, across vehicle types, driver braking behavior was strongly dependent on the urgency of the given rear-end scenario's kinematics, quantified in terms of visual looming of the lead vehicle on the driver's retina. In contrast with previous suggestions of brake reaction times (BRTs) of 1.5s or more after onset of an unexpected hazard (e.g., brake light onset), it was found here that braking could be described as typically starting less than a second after the kinematic urgency reached certain threshold levels, with even faster reactions at higher urgencies. The rate at which drivers then increased their deceleration (towards a maximum) was also highly dependent on urgency. Probability distributions are provided that quantitatively capture these various patterns of kinematics-dependent behavioral response. Possible underlying mechanisms are suggested, including looming response thresholds and neural evidence accumulation. These accounts argue that a naturalistic braking response should not be thought of as a slow reaction to some single, researcher-defined "hazard onset", but instead as a relatively fast response to the visual looming cues that build up later on in the evolving traffic scenario.

Effects of working memory load and repeated scenario exposure on emergency braking performance.

OBJECTIVE: The objective of the present study was to examine the effect of working memory load on drivers' responses to a suddenly braking lead vehicle and whether this effect (if any) is moderated by repeated scenario exposure. BACKGROUND: Several experimental studies have found delayed braking responses to lead vehicle braking events during concurrent performance of nonvisual, working memory-loading tasks, such as hands-free phone conversation. However, the common use of repeated, and hence somewhat expected, braking events may undermine the generalizability of these results to naturalistic, unexpected, emergency braking scenarios. METHOD: A critical lead vehicle braking scenario was implemented in a fixed-based simulator.The effects of working memory load and repeated scenario exposure on braking performance were examined. RESULTS: Brake response time was decomposed into accelerator pedal release time and accelerator-to-brake pedal movement time. Accelerator pedal release times were strongly reduced with repeated scenario exposure and were delayed by working memory load with a small but significant amount (178 ms).The two factors did not interact. There were no effects on accelerator-to-brake pedal movement time. CONCLUSION: The results suggest that effects of working memory load on response performance obtained from repeated critical lead vehicle braking scenarios may be validly generalized to real world unexpected events. APPLICATION: The results have important implications for the interpretation of braking performance in experimental settings, in particular in the context of safety-related evaluation of in-vehicle information and communication technologies.

An anthroposophic lifestyle and intestinal microflora in infancy.

The intestinal flora is considered to have an impact on the development of the immune system. In the anthroposophic lifestyle, a diet comprising vegetables spontaneously fermented by lactobacilli, and a restrictive use of antibiotics, anti-pyretics and vaccinations, is typical. The aim of this study was to assess the gut flora in infants in relation to certain lifestyle characteristics associated with anthroposophy. Sixty-nine children < 2 years of age with an anthroposophic lifestyle, and 59 infants of a similar age with a traditional lifestyle, were clinically examined and questionnaire replies assessed. Fecal samples were analyzed by bacterial enumeration, bacterial typing through biochemical fingerprinting and by measuring microflora-associated characteristics (MACs). The numbers of colony-forming units (CFU)/g of feces were significantly higher for enterococci and lactic acid bacteria in children who had never been exposed to antibiotics (5.5 x 107 vs. 2.1 x 107; p < 0.001 and 10 x 107 vs. 4.1 x 107; p < 0.01, respectively). Furthermore, the number of enterococci was significantly higher in breastfed and vegetarian infants (p < 0.01). The diversity (Simpson's diversity index) of lactobacilli, as determined by biochemical fingerprinting, was higher in infants born at home than in those born in hospital (p < 0.01). Several MACs were related to specific lifestyle features, and infants with an anthroposophic lifestyle had a higher proportion of acetic acid and a lower proportion of propionic acid in their stool as compared to the control children. In conclusion, lifestyle factors related to the anthroposophic way of life influenced the composition of the gut flora in the infants. These differences may contribute to the lower prevalence of atopic disease previously observed in children in anthroposophic families.