Abstract-Intelligent Transportation Systems (ITS) often operate on large road networks, and typically collect traffic data with high temporal resolution. Consequently, ITS need to handle massive volumes of data, and methods to represent that data in more compact representations are sorely needed. Subspace methods such as Principal Component Analysis (PCA) can create accurate low-dimensional models. However, such models are not readily interpretable, as the principal components usually involve a large number of links in the traffic network. In contrast, the CUR matrix decomposition leads to low-dimensional models where the components correspond to individual links in the network; the resulting models can be easily interpreted, and can also be used for compressed sensing of the traffic network. In this paper, the CUR matrix decomposition is applied for two purposes: (1) compression of traffic data; (2) compressed sensing of traffic data. In the former, only data from a "random" subset of links and time instances is stored. In the latter, data for the entire traffic network is inferred from measurements at a "random" subset of links. Numerical results for a large traffic network in Singapore demonstrate the feasibility of the proposed approach.

A real-time system is proposed to quantitatively assess speaking mannerisms and social behavior from audio recordings of two-person dialogs. Speaking mannerisms are quantitatively assessed by low-level speech metrics such as volume, rate, and pitch of speech. The social behavior is quantified by sociometrics including level of interest, agreement, and dominance. Such quantitative measures can be used to provide real-time feedback to the speakers, for instance, to alarm to speaker when the voice is too strong (speaking mannerism), or when the conversation is not proceeding well due to disagreements or numerous interruptions (social behavior). In the proposed approach, machine learning algorithms are designed to compute the sociometrics (level of interest, agreement, and dominance) in real-time from combinations of low-level speech metrics. To this end, a corpus of 150 brief two-person dialogs in English was collected. Several experts assessed the sociometrics for each of those dialogs. Next, the resulting annotated dialogs are used to train the machine learning algorithms in a supervised manner. Through this training procedure, the algorithms learn how the sociometrics depend on the low-level speech metrics, and consequently, are able to compute the sociometrics from speech recordings in an automated fashion, without further help of experts. Numerical tests through leave-one-out cross-validation indicate that the accuracy of the algorithms for inferring the sociometrics is in the range of 80-90%. In future, those reliable predictions can be the key to real-time sociofeedback, where speakers will be provided feedback in real-time about their behavior in an ongoing discussion. Such technology may be helpful in many contexts, for instance in group meetings, counseling, or executive training.

In this work we present a humanoid robot (Nao) that provides real-time sociofeedback to participants taking part in two-person dialogs. The sociofeedback system quantifies speech mannerism and social behavior of participants in an ongoing conversation, determines whether feedback is required, and delivers feedback through Nao. For example, Nao alarms the speaker(s) when the voice is too high or too low, or when the conversation is not proceeding well due to disagreements or numerous interruptions. In this study, participants are asked to engage in two-person conversations while the Nao robot acts as mediator. They then assess the received sociofeedback with respect to various aspects, including its content, appropriateness, and timing. Participants also evaluate their overall perception of Nao as social mediator via the Godspeed questionnaire.

Abstract-Traffic prediction algorithms can help improve the performance of Intelligent Transportation Systems (ITS). To this end, ITS require algorithms with high prediction accuracy. For more robust performance, the traffic systems also require a measure of uncertainty associated with prediction data. Data driven algorithms such as Support Vector Regression (SVR) perform traffic prediction with overall high accuracy. However, they do not provide any information about the associated uncertainty. The prediction error can only be calculated once field data becomes available. Consequently, the applications which use prediction data, remain vulnerable to variations in prediction error. To overcome this issue, we propose Bayesian Support Vector Regression (BSVR). BSVR provides error bars along with the predicted traffic states. We perform sensitivity and specificity analysis to evaluate the efficiency of BSVR in anticipating variations in prediction error. We perform multi-horizon prediction and analyze the performance of BSVR for expressways as well as general road segments.

The detection of driving space is the most fundamental step in intelligent vehicle control. This research paper proposes a generic vision based algorithm for identifying driving surfaces in various indoor and outdoor environments. In this paper, instead of relying on a static model for demarcating the boundaries of the driving surfaces, we propose a novel algorithm that provides an adaptive method to detect a drivable surface in any environment. The uniqueness of the proposed algorithm lies in the robustness of the adaptive model that caters for changes in the environment. These changes may be in the form of light composition, off road disturbances, on road static and dynamic objects, shadows and variations in texture for indoor environment. It basically provides a highly dynamic online mechanism for changing the parameters of the Canny Edge Enhancement algorithm. This enables us to accurately determine the starting point and orientation of the driving surface boundary. Subsequently weighted average is used on the candidate edges to optimize the edge detection results. Experiments were carried out on our university's Intelligent DRIving System (IDRIS) for outdoor environments and on P3AT for indoor purposes. The experimentation results show that the proposed method can detect the driving surface boundaries in real-time for various different environments.