In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). ISBN 9781681732749, 150 pages.
What does the future hold for the app economy? In this extended cut, we share our top predictions for the coming year. Learn how the app economy is expected to grow and which technologies -- including augmented reality, virtual reality, wearables and more -- will catch on or cool off in the coming year.
Failure prediction is more useful, the earlier it can be done, i.e. the more time can be given to the human driver to take over. By learning to predict g [t,t m], our model will alert the human driver if either the speed prediction and/or the steering angle prediction is going to fail at any of the time points in the time period [t,t m]. The learning goal is then changed to training a deep network model to make a prediction for driving actions for current time t and to make a prediction for the drivability score for the time period from t to a future time point t m. A different length can be used if the application needs. Please see Figure 2 for the illustrative flowchart of the training procedure and solution space of our driving model and the failure prediction model.