30,371 research outputs found
Assessing the Human Factor in Truck Driving
Human factors assessment techniques are commonly applied to a variety of workplaces
to examine the nature of operations and how key functions are controlled operationally;
however, these tools appear to overlook key aspects of truck driving, particularly
the driver’s relationship to the driving experience. The fundamental issue is with
the ability to completely decompose truck driving and accurately document the truck
drivers working environment will be problematic. Therefore, to demonstrate how a truck
driver moves between each series of sub-tasks will require a purpose-built assessment
tool that that is both practical and relevant to truck driving
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
The challenge of complexity for cognitive systems
Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research
An agent-based approach to assess drivers’ interaction with pre-trip information systems.
This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’
responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents,
and travel demand results from the decision-making deliberation performed by each individual of the population as regards
route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users
on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the
overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand
formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the
one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing
us to represent many of the mental aspects involved in the deliberation process
Inhibition and young children's performance on the Tower of London task
Young children, when performing problem solving tasks, show a tendency to break task rules and produce incomplete solutions. We propose that this tendency can be explained by understanding problem solving within the context of the development of “executive functions” – general cognitive control functions, which serve to regulate the operation of the cognitive system. This proposal is supported by the construction of two computational models that simulate separately the performance of 3–4 year old and 5–6 year old children on the Tower of London planning task. We seek in particular to capture the emerging role of inhibition in the older group. The basic framework within which the models are developed is derived from Fox and Das’ Domino model [Fox, J., & Das, S. (2000). Safe and sound: Artificial intelligence in hazardous applications. Cambridge, MA: MIT Press] and Norman and Shallice’s [Norman, D.A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behaviour. In R. Davidson, G. Schwartz, & D. Shapiro (Eds.), Consciousness and Self Regulation (Vol. 4). New York: Plenum] theory of willed and automatic action. Two strategies and a simple perceptual bias are implemented within the models and comparisons between model and child performance reveal a good fit for the key dependent measures (number of rule breaks and percentage of incomplete solutions) of the two groups
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