Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

/pdf/map-matching-for-low-sampling-rate-gps-trajectories-4hstm6zk4l.pdf

Map-matching for low-sampling-rate GPS trajectories

This article reviews the state of the art in social eye gaze for human-robot interaction (HRI) It establishes three categories of gaze research in HRI, defined by differences in goals and methods: a human-centered approach, which focuses on people's responses to gaze; a design-centered approach, which addresses the features of robot gaze behavior and appearance that improve interaction; and a technology-centered approach, which is concentrated on the computational tools for implementing social eye gaze in robots This paper begins with background information about gaze research in HRI and ends with a set of open questions

Social eye gaze in human-robot interaction: a review

In this paper we provide empirical evidence that using humanlike gaze cues during human-robot handovers can improve the timing and perceived quality of the handover event. Handovers serve as the foundation of many human-robot tasks. Fluent, legible handover interactions require appropriate nonverbal cues to signal handover intent, location and timing. Inspired by observations of human-human handovers, we implemented gaze behaviors on a PR2 humanoid robot. The robot handed over water bottles to a total of 102 naive subjects while varying its gaze behaviour: no gaze, gaze designed to elicit shared attention at the handover location, and the shared attention gaze complemented with a turntaking cue. We compared subject perception of and reaction time to the robot-initiated handovers across the three gaze conditions. Results indicate that subjects reach for the offered object significantly earlier when a robot provides a shared attention gaze cue during a handover. We also observed a statistical trend of subjects preferring handovers with turn-taking gaze cues over the other conditions. Our work demonstrates that gaze can play a key role in improving user experience of human-robot handovers, and help make handovers fast and fluent.Categories and Subject Descriptors I.2.9 [Robotics]: Operator interfaces, Commercial robots and applications; H.1.2 [User/Machine Systems]: Human FactorsGeneral TermsExperimentation, Design, Human Factors, Verification.

Meet me where i'm gazing: how shared attention gaze affects human-robot handover timing

Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.

Learning transportation modes from raw gps data

The development of an android with convincingly lifelike appearance and behavior has been a long-standing goal in robotics, and recent years have seen great progress in many of the technologies needed to create such androids. However, it is necessary to actually integrate these technologies into a robot system in order to assess the progress that has been made towards this goal and to identify important areas for future work. To this end, we are developing ERICA, an autonomous android system capable of conversational interaction, featuring advanced sensing and speech synthesis technologies, and arguably the most humanlike android built to date. Although the project is ongoing, initial development of the basic android platform has been completed. In this paper we present an overview of the requirements and design of the platform, describe the development process of an interactive application, report on ERICA's first autonomous public demonstration, and discuss the main technical challenges that remain to be addressed in order to create humanlike, autonomous androids.

ERICA: The ERATO Intelligent Conversational Android

Head motion occurs naturally and in synchrony with speech during human dialogue communication, and may carry paralinguistic information, such as intentions, attitudes and emotions. Therefore, natural-looking head motion by a robot is important for smooth human-robot interaction. Based on rules inferred from analyses of the relationship between head motion and dialogue acts, this paper proposes a model for generating head tilting and nodding, and evaluates the model using three types of humanoid robot (a very human-like android, "Geminoid F", a typical humanoid robot with less facial degrees of freedom, "Robovie R2", and a robot with a 3-axis rotatable neck and movable lips, "Telenoid R2"). Analysis of subjective scores shows that the proposed model including head tilting and nodding can generate head motion with increased naturalness compared to nodding only or directly mapping people's original motions without gaze information. We also find that an upwards motion of a robot's face can be used by robots which do not have a mouth in order to provide the appearance that utterance is taking place. Finally, we conduct an experiment in which participants act as visitors to an information desk attended by robots. As a consequence, we verify that our generation model performs equally to directly mapping people's original motions with gaze information in terms of perceived naturalness.

Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction

Generating natural motion in robots is important for improving human-robot interaction. We developed a tele-operation system where the lip motion of a remote humanoid robot is automatically controlled from the operator's voice. In the present work, we introduce an improved version of our proposed speech-driven lip motion generation method, where lip height and width degrees are estimated based on vowel formant information. The method requires the calibration of only one parameter for speaker normalization. Lip height control is evaluated in two types of humanoid robots (Telenoid-R2 and Geminoid-F). Subjective evaluation indicated that the proposed audio-based method can generate lip motion with naturalness superior to vision-based and motion capture-based approaches. Partial lip width control was shown to improve lip motion naturalness in Geminoid-F, which also has an actuator for stretching the lip corners. Issues regarding online real-time processing are also discussed.

Evaluation of formant-based lip motion generation in tele-operated humanoid robots

Head motion naturally occurs in synchrony with speech and may carry paralinguistic information, such as intention, attitude and emotion, in dialogue communication. With the aim of verifying the relationship between head motion and the dialogue acts carried by speech, analyses were conducted on motion-captured data for several speakers during natural dialogues. The analysis results first confirmed the trends of our previous work, showing that regardless of the speaker, nods frequently occur during speech utterances, not only for expressing dialogue acts such as agreement and affirmation, but also appearing at the last syllable of the phrase, in strong phrase boundaries, especially when the speaker is talking confidently, or expressing interest in the interlocutor's talk. Inter-speaker variability indicated that the frequency of head motion may vary according to the speaker's age or status, while intra-speaker variability indicated that the frequency of head motion also differs depending on the inter-personal relationship with the interlocutor. A simple model for generating nods based on rules inferred from the analysis results was proposed and evaluated in two types of humanoid robots. Subjective scores showed that the proposed model could generate head motions with naturalness comparable to the original motions.

Head motions during dialogue speech and nod timing control in humanoid robots

Head motion occurs naturally and in synchrony with speech during human dialogue communication, and may carry paralinguistic information, such as intentions, attitudes and emotions. Therefore, natural-looking head motion by a robot is important for smooth human–robot interaction. Based on rules inferred from analyses of the relationship between head motion and dialogue acts, this paper proposes a model for generating head tilting and nodding, and evaluates the model using three types of humanoid robot (a very human-like android, "Geminoid F", a typical humanoid robot with less facial degrees of freedom, "Robovie R2", and a robot with a 3-axis rotatable neck and movable lips, "Telenoid R2"). Analysis of subjective scores shows that the proposed model including head tilting and nodding can generate head motion with increased naturalness compared to nodding only or directly mapping people's original motions without gaze information. We also find that an upward motion of a robot's face can be used by robots which do not have a mouth in order to provide the appearance that utterance is taking place. Finally, we conduct an experiment in which participants act as visitors to an information desk attended by robots. As a consequence, we verify that our generation model performs equally to directly mapping people's original motions with gaze information in terms of perceived naturalness.

Generation of nodding, head tilting and gazing for human–robot speech interaction

In order to tele-operate the lip motion of a humanoid robot (such as android) from the utterances of the operator, we developed a speech-driven lip motion generation method. The proposed method is based on the rotation of the vowel space, given by the first and second formants, around the center vowel, and a mapping to the lip opening degrees. The method requires the calibration of only one parameter for speaker normalization, so that no other training of models is required. In a pilot experiment, the proposed audio-based method was perceived as more natural than vision-based approaches, regardless of the language.

Speech-driven lip motion generation for tele-operated humanoid robots.

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