A model of communication-enabled traffic interactions

A major challenge for autonomous vehicles is handling interactive scenarios, such as highway merging, with human-driven vehicles. A better understanding of human interactive behaviour could help address this challenge. Such understanding could be obtained through modelling human behaviour. However, existing modelling approaches predominantly neglect communication between drivers and assume that some drivers in the interaction only respond to others, but do not actively influence them. Here we argue that addressing these two limitations is crucial for accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model the interaction in an integral way rather than modelling an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication and bounded rationality. We demonstrate the model in a simplified merging scenario, illustrating that it generates plausible interactive behaviour (e.g., aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles

Field Production and Functional Evaluation of Chloroplast-Derived Interferon-α2b

Type I interferons (IFNs) inhibit viral replication and cell growth and enhance the immune response, and therefore have many clinical applications. IFN-α2b ranks third in world market use for a biopharmaceutical, behind only insulin and erythropoietin. The average annual cost of IFN-α2b for the treatment of hepatitis C infection is $26 000, and is therefore unavailable to the majority of patients in developing countries. Therefore, we expressed IFN-α2b in tobacco chloroplasts, and transgenic lines were grown in the field after obtaining United States Department of Agriculture Animal and Plant Health Inspection Service (USDA-APHIS) approval. Stable, site-specific integration of transgenes into chloroplast genomes and homoplasmy through several generations were confirmed. IFN-α2b levels reached up to 20% of total soluble protein, or 3 mg per gram of leaf (fresh weight). Transgenic IFN-α2b had similar in vitrobiological activity to commercially produced PEG-Intron™ when tested for its ability to protect cells against cytopathic viral replication in the vesicular stomatitis virus cytopathic effect (VSV CPE) assay and to inhibit early-stage human immunodeficiency virus (HIV) infection. The antitumour and immunomodulating properties of IFN-α2b were also seen in vivo . Chloroplast-derived IFN-α2b increased the expression of major histocompatibility complex class I (MHC I) on splenocytes and the total number of natural killer (NK) cells. Finally, IFN-α2b purified from chloroplast transgenic lines (cpIFN-α2b) protected mice from a highly metastatic tumour line. This demonstration of high levels of expression of IFN-α2b, transgene containment and biological activity akin to that of commercial preparations of IFN-α2b facilitated the first field production of a plant-derived human blood protein, a critical step towards human clinical trials and commercialization

Harmonizing and improving European education in prescribing: An overview of digital educational resources used in clinical pharmacology and therapeutics

Aim: Improvement and harmonization of European clinical pharmacology and therapeutics (CPT) education is urgently required. Because digital educational resources can be easily shared, adapted to local situations and re-used widely across a variety of educational systems, they may be ideally suited for this purpose. Methods: With a cross-sectional survey among principal CPT teachers in 279 out of 304 European medical schools, an overview and classification of digital resources was compiled. Results: Teachers from 95 (34%) medical schools in 26 of 28 EU countries responded, 66 (70%) of whom used digital educational resources in their CPT curriculum. A total of 89 of such resources were described in detail, including e-learning (24%), simulators to teach pharmacokinetics and/or pharmacodynamics (10%), virtual patients (8%), and serious games (5%). Together, these resources covered 235 knowledge-based learning objectives, 88 skills, and 13 attitudes. Only one third (27) of the resources were in-part or totally free and only two were licensed open educational resources (free to use, distribute and adapt). A narrative overview of the largest, free and most novel resources is given. Conclusion: Digital educational resources, ranging from e-learning to virtual patients and games, are widely used for CPT education in EU medical schools. Learning objectives are based largely on knowledge rather than skills or attitudes. This may be improved by including more real-life clinical case scenarios. Moreover, the majority of resources are neither free nor open. Therefore, with a view to harmonizing international CPT education, more needs to be learned about why CPT teachers are not currently sharing their educational materials

TraViA: a Traffic data Visualization and Annotation tool in Python

In recent years, multiple datasets containing traffic recorded in the real world and containing human-driven trajectories have been made available to researchers. Among these datasets are the HighD, pNEUMA, and NGSIM datasets. TraViA, an open-source Traffic data Visualization and Annotation tool was created to provide a single environment for working with data from these three datasets. Combining the data in a single visualization tool enables researchers to easily study data from all sources. TraViA was designed in such a way that it can easily be extended to visualize data from other datasets and that specific needs for research projects are easily implemented.Human-Robot Interactio

Modelling communication-enabled traffic interactions

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles—for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver’s behaviours. We demonstrate our model’s potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model’s decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles

Interactive Merging Behavior in a Coupled Driving Simulator: Experimental Framework and Case Study

Human highway-merging behavior is an important aspect when developing autonomous vehicles (AVs) that can safely and successfully interact with other road users. To design safe and acceptable human-AV interactions, the underlying mechanisms in human-human interactive behavior need to be understood. Exposing and understanding these mechanisms can be done using controlled driving simulator experiments. However, until now, such human-factors merging experiments have focused on aspects of the behavior of a single driver (e.g., gap acceptance) instead of on the dynamics of the interaction. Furthermore, existing experimental scenarios and data analysis tools (i.e., concepts like time-to-collision) are insufficient to analyze human-human interactive merging behavior. To help facilitate human-factors research on merging interactions, we propose an experimental framework consisting of a general simplified merging scenario and a set of three analysis tools: (1) a visual representation that captures the combined behavior of two participants and the safety margins they maintain in a single plot; (2) a signal (over time) that describes the level of conflict; and (3) a metric that describes the amount of time that was required to solve the merging conflict, called the conflict resolution time. In a case study with 18 participants, we used the proposed framework and analysis tools in a top-down view driving simulator where two human participants can interact. The results show that the proposed scenario can expose diverse behaviors for different conditions. We demonstrate that our novel visual representation, conflict resolution time, and conflict signal are valuable tools when comparing human behavior between conditions. Therefore, with its simplified merging scenario and analysis tools, the proposed experimental framework can be a valuable asset when developing driver models that describe interactive merging behavior and when designing AVs that interact with humans.Human-Robot Interactio

A Human Factors Approach to Validating Driver Models for Interaction-aware Automated Vehicles

A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle’s actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this paper, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workflow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workflow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model’s operational behavior was inconsistent with observed human behavior. The case study illustrates that a principled evaluation workflow is useful and needed. We believe that our workflow will support the development of appropriate driver models for future automated vehicles.Human-Robot Interactio

Exploring the Distribution of AENG, the Old Frisian Counterpart of Modern English ANY, Modern Dutch ENIG.

Old Frisian regularly features a word which is etymologically related to the word any in Modern English, and to enig in Modern Dutch. The purpose of this explorative article is to make a beginning with the description of the distributional properties of this word aeng in old Frisian. We will show that this word, like its Modern English counterpart any, behaves like a negative polarity item. An attempt is made to characterise the range of syntactic / semantic contexts in which it may appear