Visual Analysis of Uncertainty in Trajectories

Mining trajectory datasets has many important applications. Real trajectory data often involve uncertainty due to inadequate sampling rates and measurement errors. For some trajectories, their precise positions cannot be recovered and the exact routes that vehicles traveled cannot be accurately reconstructed. In this paper, we investigate the uncertainty problem in trajectory data and present a visual analytics system to reveal, analyze, and solve the uncertainties associated with trajectory samples. We first propose two novel visual encoding schemes called the road map analyzer and the uncertainty lens for discovering road map errors and visually analyzing the uncertainty in trajectory data respectively. Then, we conduct three case studies to discover the map errors, to address the ambiguity problem in map-matching, and to reconstruct the trajectories with historical data. These case studies demonstrate the capability and effectiveness of our system. ? 2014 Springer International Publishing.EI

Visual Analysis of Multiple Dynamic Sensitivities along Ascending Trajectories in the Atmosphere

Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics. These parameterizations are a well-known source of uncertainty in weather forecasts that can be quantified via algorithmic differentiation, which computes the sensitivities of prognostic variables to changes in model parameters. It is particularly interesting to use sensitivities to analyze the validity of physical assumptions on which microphysical parameterizations in the numerical model source code are based. In this article, we consider the use case of strongly ascending trajectories, so-called warm conveyor belt trajectories, known to have a significant impact on intense surface precipitation rates in extratropical cyclones. We present visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable, i.e. rain mass density, to multiple model parameters along such trajectories. We propose a visual interface that enables to a) compare the values of multiple sensitivities at a single time step on multiple trajectories, b) assess the spatio-temporal relationships between sensitivities and the shape and location of trajectories, and c) a comparative analysis of the temporal development of sensitivities along multiple trajectories. We demonstrate how our approach enables atmospheric scientists to interactively analyze the uncertainty in the microphysical parameterizations, and along the trajectories, with respect to a selected prognostic variable. We apply our approach to the analysis of convective trajectories within the extratropical cyclone "Vladiana", which occurred between 22-25 September 2016 over the North Atlantic

Perception-aware Path Planning

In this paper, we give a double twist to the problem of planning under uncertainty. State-of-the-art planners seek to minimize the localization uncertainty by only considering the geometric structure of the scene. In this paper, we argue that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task. Thus, we describe how to optimally incorporate the photometric information (i.e., texture) of the scene, in addition to the the geometric one, to compute the uncertainty of vision-based localization during path planning. To avoid the caveats of feature-based localization systems (i.e., dependence on feature type and user-defined thresholds), we use dense, direct methods. This allows us to compute the localization uncertainty directly from the intensity values of every pixel in the image. We also describe how to compute trajectories online, considering also scenarios with no prior knowledge about the map. The proposed framework is general and can easily be adapted to different robotic platforms and scenarios. The effectiveness of our approach is demonstrated with extensive experiments in both simulated and real-world environments using a vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for IEEE Transactions on Robotic

Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty

Progress towards advanced systems for assisted and autonomous driving is leveraging recent advances in recognition and segmentation methods. Yet, we are still facing challenges in bringing reliable driving to inner cities, as those are composed of highly dynamic scenes observed from a moving platform at considerable speeds. Anticipation becomes a key element in order to react timely and prevent accidents. In this paper we argue that it is necessary to predict at least 1 second and we thus propose a new model that jointly predicts ego motion and people trajectories over such large time horizons. We pay particular attention to modeling the uncertainty of our estimates arising from the non-deterministic nature of natural traffic scenes. Our experimental results show that it is indeed possible to predict people trajectories at the desired time horizons and that our uncertainty estimates are informative of the prediction error. We also show that both sequence modeling of trajectories as well as our novel method of long term odometry prediction are essential for best performance.Comment: CVPR 201

Visual analytics of flight trajectories for uncovering decision making strategies

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences

Cognitive loading affects motor awareness and movement kinematics but not locomotor trajectories during goal-directed walking in a virtual reality environment.

The primary purpose of this study was to investigate the effects of cognitive loading on movement kinematics and trajectory formation during goal-directed walking in a virtual reality (VR) environment. The secondary objective was to measure how participants corrected their trajectories for perturbed feedback and how participants' awareness of such perturbations changed under cognitive loading. We asked 14 healthy young adults to walk towards four different target locations in a VR environment while their movements were tracked and played back in real-time on a large projection screen. In 75% of all trials we introduced angular deviations of ±5° to ±30° between the veridical walking trajectory and the visual feedback. Participants performed a second experimental block under cognitive load (serial-7 subtraction, counter-balanced across participants). We measured walking kinematics (joint-angles, velocity profiles) and motor performance (end-point-compensation, trajectory-deviations). Motor awareness was determined by asking participants to rate the veracity of the feedback after every trial. In-line with previous findings in natural settings, participants displayed stereotypical walking trajectories in a VR environment. Our results extend these findings as they demonstrate that taxing cognitive resources did not affect trajectory formation and deviations although it interfered with the participants' movement kinematics, in particular walking velocity. Additionally, we report that motor awareness was selectively impaired by the secondary task in trials with high perceptual uncertainty. Compared with data on eye and arm movements our findings lend support to the hypothesis that the central nervous system (CNS) uses common mechanisms to govern goal-directed movements, including locomotion. We discuss our results with respect to the use of VR methods in gait control and rehabilitation