Sahil Narang

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.

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.

Texture Based Image Retrieval Using Correlation on Haar Wavelet Transform

Content Based Image Retrieval deals with the retrieval of most similar images corresponding to a query image from an image database. It involves feature extraction and similarity computation. This paper proposes a method named Correlation Texture Descriptor (CTD) which computes the correlation between the sub bands formed after applying Haar Discrete Wavelet Transform. Fuzzy Logic is used to compute the similarity of two feature vectors. Experiments determined that the proposed method, CTD, showed a significant improvement in retrieval performance when compared to other methods such as Weighted Standard Deviation (WSD), Gradient operation using Sobel operator and Gray Level Co-occurrence Matrix (GLCM).

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.