Andrew Best

Generating Pedestrian Trajectories Consistent with the Fundamental Diagram Based on Physiological and Psychological Factors

Pedestrian crowds often have been modeled as many-particle system including microscopic multi-agent simulators. One of the key challenges is to unearth governing principles that can model pedestrian movement, and use them to reproduce paths and behaviors that are frequently observed in human crowds. To that effect, we present a novel crowd simulation algorithm that generates pedestrian trajectories that exhibit the speed-density relationships expressed by the Fundamental Diagram. Our approach is based on biomechanical principles and psychological factors. The overall formulation results in better utilization of free space by the pedestrians and can be easily combined with well-known multi-agent simulation techniques with little computational overhead. We are able to generate human-like dense crowd behaviors in large indoor and outdoor environments and validate the results with captured real-world crowd trajectories.

PedVR: simulating gaze-based interactions between a real user and virtual crowds

We present a novel interactive approach, PedVR, to generate plausible behaviors for a large number of virtual humans, and to enable natural interaction between the real user and virtual agents. Our formulation is based on a coupled approach that combines a 2D multi-agent navigation algorithm with 3D human motion synthesis. The coupling can result in plausible movement of virtual agents and can generate gazing behaviors, which can considerably increase the believability. We have integrated our formulation with the DK-2 HMD and demonstrate the benefits of our crowd simulation algorithm over prior decoupled approaches. Our user evaluation suggests that the combination of coupled methods and gazing behavior can considerably increase the behavioral plausibility.

Interactive and adaptive data-driven crowd simulation

We present an adaptive data-driven algorithm for interactive crowd simulation. Our approach combines realistic trajectory behaviors extracted from videos with synthetic multi-agent algorithms to generate plausible simulations. We use statistical techniques to compute the movement patterns and motion dynamics from noisy 2D trajectories extracted from crowd videos. These learned pedestrian dynamic characteristics are used to generate collision-free trajectories of virtual pedestrians in slightly different environments or situations. The overall approach is robust and can generate perceptually realistic crowd movements at interactive rates in dynamic environments. We also present results from preliminary user studies that evaluate the trajectory behaviors generated by our algorithm.

Real-time reciprocal collision avoidance with elliptical agents

We present a novel algorithm for real-time collision-free navigation between elliptical agents. Each robot or agent is represented using a tight-fitting 2D ellipse in the plane. We extend the reciprocal velocity obstacle formulation by using conservative linear approximations of ellipses and derive sufficient conditions for collision-free motion based on low-dimensional linear programming. We use precomputed Minkowski Sum approximations for real-time and conservative collision avoidance in large multi-agent environments. Finally, we present efficient techniques to update the orientation to compute collision-free trajectories. Our algorithm can handle thousands of elliptical agents in real-time on a single core and provides significant speedups over prior algorithms for elliptical agents. We compare the runtime performance and behavior with circular agents on different benchmarks.

Motion recognition of self and others on realistic 3D avatars

Current 3D capture and modeling technology can rapidly generate highly photo‐realistic 3D avatars of human subjects. However, while the avatars look like their human counterparts, their movements often do not mimic their own due to existing challenges in accurate motion capture and retargeting. A better understanding of factors that influence the perception of biological motion would be valuable for creating virtual avatars that capture the essence of their human subjects. To investigate these issues, we captured 22 subjects walking in an open space. We then performed a study where participants were asked to identify their own motion in varying visual representations and scenarios. Similarly, participants were asked to identify the motion of familiar individuals. Unlike prior studies that used captured footage with simple “point‐light” displays, we rendered the motion on photo‐realistic 3D virtual avatars of the subject. We found that self‐recognition was significantly higher for virtual avatars than with point‐light representations. Users were more confident of their responses when identifying their motion presented on their virtual avatar. Recognition rates varied considerably between motion types for recognition of others, but not for self‐recognition. Overall, our results are consistent with previous studies that used recorded footage and offer key insights into the perception of motion rendered on virtual avatars.

Simulating high-DOF human-like agents using hierarchical feedback planner

We present a multi-agent simulation algorithm to compute the trajectories and full-body motion of human-like agents. Our formulation uses a coupled approach that combines 2D collision-free navigation with high-DOF human motion simulation using a behavioral finite state machine. In order to generate plausible pedestrian motion, we use a closed-loop hierarchical planner that satisfies dynamic stability, biomechanical, and kinematic constraints, and is tightly integrated with multi-agent navigation. Furthermore, we use motion capture data to generate natural looking human motion. The overall system is able to generate plausible motion with upper and lower body movements and avoid collisions with other human-like agents. We highlight its performance in indoor and outdoor scenarios with tens of human-like agents.

Trabecular bone in the calcaneus of runners

Trabecular bone of the human calcaneus is subjected to extreme repetitive forces during endurance running and should adapt in response to this strain. To assess possible bone functional adaptation in the posterior region of the calcaneus, we recruited forefoot-striking runners (n = 6), rearfoot-striking runners (n = 6), and non-runners (n = 6), all males aged 20–41 for this institutionally approved study. Foot strike pattern was confirmed for each runner using a motion capture system. We obtained high resolution peripheral computed tomography scans of the posterior calcaneus for both runners and non-runners. No statistically significant differences were found between runners and nonrunners or forefoot strikers and rearfoot strikers. Mean trabecular thickness and mineral density were greatest in forefoot runners with strong effect sizes (<0.80). Trabecular thickness was positively correlated with weekly running distance (r2 = 0.417, p<0.05) and years running (r2 = 0.339, p<0.05) and negatively correlated with age at onset of running (r2 = 0.515, p<0.01) Trabecular thickness, mineral density and bone volume ratio of nonrunners were highly correlated with body mass (r2 = 0.824, p<0.05) and nonrunners were significantly heavier than runners (p<0.05). Adjusting for body mass revealed significantly thicker trabeculae in the posterior calcaneus of forefoot strikers, likely an artifact of greater running volume and earlier onset of running in this subgroup; thus, individuals with the greatest summative loading stimulus had, after body mass adjustment, the thickest trabeculae. Further study with larger sample sizes is necessary to elucidate the role of footstrike on calcaneal trabecular structure. To our knowledge, intraspecific body mass correlations with measures of trabecular robusticity have not been reported elsewhere. We hypothesize that early adoption of running and years of sustained moderate volume running stimulate bone modeling in trabeculae of the posterior calcaneus.

Similarity, Complementarity, and Agency in HRI: Theoretical Issues in Shifting the Perception of Robots from Tools to Teammates

Robotic teammates are becoming prevalent in increasingly complex and dynamic operational and social settings. For this reason, the perception of robots operating in such environments has transitioned from the perception of robots as tools, extending human capabilities, to the perception of robots as teammates, collaborating with humans and displaying complex social cognitive processes. The goal of this paper is to introduce a discussion on an integrated set of robotic design elements, as well as provide support for the idea that human-robot interaction requires a clearer understanding of social cognitive constructs to optimize human-robot collaboration. We develop a set of research questions addressing these constructs with the goal of improving the engineering of artificial cognitive systems reliant on natural human-robot interaction.

AutonoVi: Autonomous vehicle planning with dynamic maneuvers and traffic constraints

We present AutonoVi, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and integrates traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.

Developing A Theoretical Framework of Task Complexity for Research on Visualization in Support of Decision Making Under Uncertainty

This paper describes initial validation of a theoretical framework to support research on the visualization of uncertainty. Two experiments replicated and extended this framework, illustrating how the manipulation of task complexity produces differences in performance. Additionally, using a combinatory metric of workload and performance, this framework provides a new metric for assessing uncertainty visualization. We describe how this work acts as a theoretical scaffold for examining differing forms of visualizations of uncertainty by providing a means for systematic variations in task context.