EFFECT OF SHORT-STORAGE HRGCs ON DRIVER DECISION BEHAVIOR AND SAFETY CONCERNS: REAL-WORLD ANALYSIS AND EXPERIMENTAL EVIDENCE

Vehicle-train collisions at highway-rail grade crossings (HRGCs) continue to be a safety concern, and despite improvements in warnings, many of these incidents are attributed to human error. In some cases, distractions other than railroad traffic, such as HRGCs with limited space between the railroad tracks and the highway intersection, may create additional cognitive burdens for drivers. We investigated the effect of HRGC type (short-storage vs. non-short storage) on driver attention and decision-making in two studies. In Study 1, we systematically analyzed 996 incidents from 2017-2019 from the Federal Railroad Administration’s Safety database. Driver decision making and outcomes were different depending on HRGC type, with more train strikes in short storage incidents, as opposed to vehicle strikes. Study 2 was a controlled lab experiment in which drivers identified safety concerns in driving images. Drivers reported more safety concerns, and rated them more important in images of short-storage HRGCs than non-short storage HRGCs. This pattern did not depend on their rural or urban driving experience. Eye-tracking analysis found some differences in search behavior depending on the type of HRGC. This research contributes to a new area of research in rail safety, as studies comparing the two types of HRGCs have previously not been done. Interventions for non-short-storage HRGCs may not apply to short-storage HRGCs if it is found that drivers approach them differently

EXPLICIT RULE LEARNING: A COGNITIVE TUTORIAL METHOD TO TRAIN USERS OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING SYSTEMS

Today’s intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system’s underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems’ output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used to train humans in intelligent software systems; one of them is exemplar-based training. Although there has been some success, depending on the structure of the system, there are limitations to exemplars, which oftentimes are post hoc and case-based. Explicit Rule Learning is a global and rule-based training method that incorporates exemplars, but goes beyond specific cases. It provides learners with rich, robust mental models and the ability to transfer the learned skills to novel, previously unencountered situations. Learners are given verbalizable, probabilistic if...then statements, supplemented with exemplars. This is followed up with a series of practice problems, to which learners respond and receive immediate feedback on their correctness. The expectation is that this method will result in a refined representation of the system’s underlying principles, and a richer and more robust mental model that will enable the learner to simulate future states. Preliminary research helped to evaluate and refine Explicit Rule Learning. The final study in this research applied Explicit Rule Learning to a more real-world system, autonomous driving. The mixed-method within-subject study used a more naturalistic environment. Participants were given training material using the Explicit Rule Learning method and were subsequently tested on their ability to predict the autonomous vehicle’s actions. The results indicate that the participants trained with the Explicit Rule Learning method were more proficient at predicting the autonomous vehicle’s actions. These results, together with the results of preceding studies indicate that Explicit Rule Learning is an effective method to accelerate the proficiency of learners of intelligent software systems. Explicit Rule Learning is a low-cost training intervention that can be adapted to many intelligent software systems, including the many types of AI/ML systems in today’s world

MWCNTs of different physicochemical properties cause similar inflammatory responses, but differences in transcriptional and histological markers of fibrosis in mouse lungs

Multi-walled carbon nanotubes (MWCNTs) are extensively produced and used in composite materials and electronic applications, thus increasing risk of worker and consumer exposure. MWCNTs are an inhomogeneous group of nanomaterials that come in various lengths, shapes and with different metal contaminations, which makes hazard evaluation difficult. However, several studies suggest that length plays an important role in the toxicity induced by MWCNTs. How the length influences toxicity at the molecular level is yet to be characterized. Female C57BL/6 mice were exposed by single intratracheal instillation to 18, 54 or 162 µg/mouse of a short MWCNT (NRCWE-026, 847±102 nm in length) or long MWCNT (NM-401, 4048±366 nm in length). The two MWCNTs were extensively characterized. Lung tissues were harvested 24 h, 3 d and 28 d after exposure. We employed DNA microarrays, bronchoalveolar lavage fluid analysis, comet assay and dichlorodihydrofluorescein assay in order to profile the pulmonary responses. Bioinformatics tools were then applied to compare and contrast the expression profiles and to build a length dependent property-response matrix for gene-by-gene comparison. The toxicogenomic analysis of the global mRNA changes after exposure to the short, entangled NRCWE-026 or the longer, stiffer NM-401 showed high degree of similarities. The toxicity of both MWCNTs was driven by strong inflammatory and acute phase responses, which peaked at day 3 and was observed both in bronchoalveolar lavage cell influx and in gene expression profiles. The inflammatory response was sustained at post-exposure day 28. Also, at the sub-chronic level, we identified a sub-set of 14 fibrosis related genes that were uniquely differentially regulated after exposure to NM-401. Acellular ROS production occurred almost exclusively with NRCWE-026, however the longer NM-401 induced in vivo DNA strand breaks and differential regulation of genes involved in free radical scavenging more readily than NRCWE-026. Our results indicate that the global mRNA response after exposure to MWCNTs is length independent at the acute time points, but that fibrosis may be length dependent sub-chronic end point.JRC.H.6-Digital Earth and Reference Dat

When Self-Driving Fails: Evaluating Social Media Posts Regarding Problems and Misconceptions about Tesla’s FSD Mode

With the recent deployment of the latest generation of Tesla’s Full Self-Driving (FSD) mode, consumers are using semi-autonomous vehicles in both highway and residential driving for the first time. As a result, drivers are facing complex and unanticipated situations with an unproven technology, which is a central challenge for cooperative cognition. One way to support cooperative cognition in such situations is to inform and educate the user about potential limitations. Because these limitations are not always easily discovered, users have turned to the internet and social media to document their experiences, seek answers to questions they have, provide advice on features to others, and assist other drivers with less FSD experience. In this paper, we explore a novel approach to supporting cooperative cognition: Using social media posts can help characterize the limitations of the automation in order to get information about the limitations of the system and explanations and workarounds for how to deal with these limitations. Ultimately, our goal is to determine the kinds of problems being reported via social media that might be useful in helping users anticipate and develop a better mental model of an AI system that they rely on. To do so, we examine a corpus of social media posts about FSD problems to identify (1) the typical problems reported, (2) the kinds of explanations or answers provided by users, and (3) the feasibility of using such user-generated information to provide training and assistance for new drivers. The results reveal a number of limitations of the FSD system (e.g., lane-keeping and phantom braking) that may be anticipated by drivers, enabling them to predict and avoid the problems, thus allowing better mental models of the system and supporting cooperative cognition of the human-AI system in more situations

The Unreasonable Ineptitude of Deep Image Classification Networks

The success of deep image classification networks has been met with enthusiasm and investment from both the academic community and industry. We hypothesize users will expect these systems to behave similarly to humans, and to succeed and fail in ways humans do. To investigate this, we tested six popular image classifiers on imagery from ten tool categories, examining how 17 visual transforms impacted both human and AI classification. Results showed that (1) none of the visual transforms we examined produced substantial impairment for human recognition; (2) human errors were limited to mostly to functional confusions; (3) almost all visual transforms impacted nearly every image classifier negatively and often catastrophically; (4) human expectations about performance of AI classifiers map more closely onto human error than AI performance; and (5) models trained with an enriched training set involving examples of the transformed imagery achieved improved performance but were not inoculated from error

Examining Methods for Combining Speed and Accuracy in a Go/No-Go Vigilance Task

In many human performance tasks, researchers assess performance by measuring both accuracy and response time. A number of theoretical and practical approaches have been proposed to obtain a single performance value that combines these measures, with varying degrees of success. In this report, we examine data from a common paradigm used in applied human factors assessment: a go/no-go vigilance task (Smith et al., 2019). We examined whether 12 different measures of performance were sensitive to the vigilance decrement induced by the design, and also examined how the different measures were correlated. Results suggest that most combined measures were slight improvements over accuracy or response time alone, with the most sensitive and representative result coming from the Linear Ballistic Accumulator model. Practical lessons for applying these measures are discussed