Towards Learning Adaptive Workload Maps
نویسنده
چکیده
One approach to mitigate the risks of driver distraction is to build an in-vehicle service manager component that is aware of the attentional requirements of the current and of upcoming traffic situations. This component will rely on technologies for personalized driver workload prediction, based on an enhanced digital map, and/or on sensors for physiological and behavioral workload correlates. In this report, we address first results of our approach towards the following questions: • According to our experiments, what method is best for online/predictive workload estimation? • Which sensors are most suitable? • How do physiological measurements and subjective rating correlate? • Which proportion of workload can be statically predicted (based on map features alone)? • How do workload patterns differ between drivers? • How dynamic is workload (how long does an influence persist)? • Which factors (percentage) influence workload?
منابع مشابه
Who Is More Adaptive? ACME: Adaptive Caching using Multiple Experts
The trend in cache design research is towards finding the single optimum replacement policy that performs better than any other proposed policy by using all the useful criteria at once. However, due to the variety of workloads and system topologies it is daunting, if not impossible, to summarize all this information into one magical value using any static formula. We propose a workload and topo...
متن کاملOngoing Efforts towards Developing a Physiologically Driven Training System
There have been a number of successes of real-time application of physiological measures in operational environments such as with the control of remotely piloted vehicles (RPV). More recently, similar techniques have been investigated within the context of improving learning. A major challenge of the learning environment is that an individual's ability to perform the task, and thus their worklo...
متن کاملTowards A Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning
With the wide spread applications of e-Learning technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Accordingly, instructors are often overwhelmed by the huge number of messages created by students through online discussion forums. It is quite difficult, if not totally impossible, f...
متن کاملLearning Document Image Features With SqueezeNet Convolutional Neural Network
The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...
متن کاملViewing Adaptive Migration Policies for Tiered Storage Systems as a Supervised Learning Problem
Many file migration algorithms rely on simple, unchanging, automated heuristics to make file placement decisions for exclusively hierarchical storage systems. Such approaches cannot adapt to changes in the workload or data center configuration. Systems with manually-tuned policies offer a way to deal with changes but require well-trained administrators, thereby driving up the cost of storage ma...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003