Showing papers on "Turn-by-turn navigation published in 2019"

Impact of Expertise on Interaction Preferences for Navigation Assistance of Visually Impaired Individuals

Abstract: Navigation assistive technologies have been designed to support individuals with visual impairments during independent mobility by providing sensory augmentation and contextual awareness of their surroundings. Such information is habitually provided through predefned audio-haptic interaction paradigms. However, individual capabilities, preferences and behavior of people with visual impairments are heterogeneous, and may change due to experience, context and necessity. Therefore, the circumstances and modalities for providing navigation assistance need to be personalized to different users, and through time for each user. We conduct a study with 13 blind participants to explore how the desirability of messages provided during assisted navigation varies based on users' navigation preferences and expertise. The participants are guided through two different routes, one without prior knowledge and one previously studied and traversed. The guidance is provided through turn-by-turn instructions, enriched with contextual information about the environment. During navigation and follow-up interviews, we uncover that participants have diversifed needs for navigation instructions based on their abilities and preferences. Our study motivates the design of future navigation systems capable of verbosity level personalization in order to keep the users engaged in the current situational context while minimizing distractions.

Deep Learning Compensation of Rotation Errors During Navigation Assistance for People with Visual Impairments or Blindness

Abstract: Navigation assistive technologies are designed to support people with visual impairments during mobility. In particular, turn-by-turn navigation is commonly used to provide walk and turn instructions, without requiring any prior knowledge about the traversed environment. To ensure safe and reliable guidance, many research efforts focus on improving the localization accuracy of such instruments. However, even when the localization is accurate, imprecision in conveying guidance instructions to the user and in following the instructions can still lead to unrecoverable navigation errors. Even slight errors during rotations, amplified by the following frontal movement, can result in the user taking an incorrect and possibly dangerous path.In this article, we analyze trajectories of indoor travels in four different environments, showing that rotation errors are frequent in state-of-art navigation assistance for people with visual impairments. Such errors, caused by the delay between the instruction to stop rotating and when the user actually stops, result in over-rotation. To compensate for over-rotation, we propose a technique to anticipate the stop instruction so that the user stops rotating closer to the target rotation. The technique predicts over-rotation using a deep learning model that takes into account the user’s current rotation speed, duration, and angle; the model is trained with a dataset of rotations performed by blind individuals. By analyzing existing datasets, we show that our approach outperforms a naive baseline that predicts over-rotation with a fixed value. Experiments with 11 blind participants also show that the proposed compensation method results in lower rotation errors (18.8° on average) compared to the non-compensated approach adopted in state-of-the-art solutions (30.1°).

Refined betatron tune measurements by mixing beam position data

Abstract: The measurement of the betatron tunes in a circular accelerator is of paramount importance due to their impact on beam dynamics. The resolution of the these measurements, when using turn by turn (TbT) data from beam position monitors (BPMs), is greatly limited by the available number of turns in the signal. Due to decoherence from finite chromaticity and/or amplitude detuning, the transverse betatron oscillations appear to be damped in the TbT signal. On the other hand, an adequate number of samples is needed, if precise and accurate tune measurements are desired. In this paper, a method is presented that allows for very precise tune measurements within a very small number of turns. The theoretical foundation of this method is presented with results from numerical and tracking simulations but also from experimental TbT data which are recorded at electron and proton circular accelerators.

DetourNavigator - Using Google Location History to Generate Unfamiliar Personal Routes

Abstract: With the ubiquity of turn-by-turn navigation on toady's smartphones, personal exploration of the unseen has been drastically diminished. Such services make it less likely for users to conquer their less frequented parts of the urban environment. In this paper we present the DetourNavigator, a navigation service that creates routes based on Google Location History along areas that are unfamiliar to the user. Our preliminary user study indicates that these personalized graphs are well suited to generate routes that might lead to more holistic knowledge about the built environment.