I work as a Graduate Research Assistant in the GAMMA group under Prof. Dinesh Manocha. My core focus lies in multi-agent simulation and motion planning, with applications to autonomous driving and pedestrian motion synthesis. As such, my research spans several areas including crowd simulation, multi-agent navigation, motion planning, behavioral modeling, & virtual reality.

Research Focus

My primary research focus is on developing efficient motion models for multi-agent simulation. My recent work includes developing dynamics-aware motion models for navigation of autonomous vehicles, immersive models for user-virtual agent interactions in VR, and core problems in crowd simulation.

Social VR: Avatar & Multi-agent Interactions

Current virtual experiences often lack believable and interactive virtual agents, and are also limited in the fidelity of the user's avatar (i.e. the user's embodiement in the virtual world). We propose algorithms to increase the motion, and behavioral realism of the virtual agents, thus creating immersive virtual experiences. Agents are capable of finding collision-free paths in complex environments, and interacting with the avatars using natural language processing and generation, as well as non-verbal behaviours such as gazing, gesturing, facial expressions etc.
Project Website  

Autonomous Vehicle Planning

We present AutonoVi, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies 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.
Project Website  

Crowd Simulation

The problem of simulating the movement and behaviors of human-like crowds is important in many applications, including architecture and urban design, pedestrian dynamics, computer animation, games, virtual reality, etc. Pedestrian crowds often have been modeled as many-particle system including microscopic multi-agent simulators. However, simulating real world behaviors is especially difficult. One must not only compute collision-free paths for potentially 1000's of agents in real time, but also ensure that these paths are consistent with captured human behavior.
Project Website: Density Sensitive Crowd Simulation
Project Website: Crowd Simulation using Elliptical Agents

Publications

Generating Virtual Avatars with Personalized Walking Gaits using Commodity Hardware
Sahil Narang, Andrew Best, Ari Shapiro, Dinesh Manocha
ACM Multimedia Conference (Proceedings of Thematic Workshops), 2017


AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
Andrew Best, Sahil Narang, Daniel Barber, Dinesh Manocha
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017


Motion recognition of self and others on realistic 3D avatars
Sahil Narang, Andrew Best, Andrew Feng, Sin-hwa Kang, Dinesh Manocha, Ari Shapiro
Computer Animation and Virtual Worlds, 2017


PedVR: simulating gaze-based interactions between a real user and virtual crowds
Sahil Narang, Andrew Best, Tanmay Randhavane, Ari Shapiro, Dinesh Manocha
ACM Conference on Virtual Reality Software and Technology (VRST), 2016


Interactive Simulation of Local Interactions in Dense Crowds using Elliptical Agents
Sahil Narang, Andrew Best, Dinesh Manocha
Journal of Statistical Mechanics: Theory and Experiment, Volume 3, 2017


Interactive and Conservative Collision Avoidance for Elliptical Agents
Andrew Best, Sahil Narang, Dinesh Manocha
The International Conference on Robotics and Automation (ICRA), 2016


Simulating High-DOF Human-like Agents using Hierarchical Feedback Planner
Chonhyon Park, Andrew Best, Sahil Narang, Dinesh Manocha
The ACM Symposium on Virtual Reality Software and Technology (VRST), 2015


Generating Pedestrian Trajectories Consistent with the Fundamental Diagram based on Physiological and Psychological Factors
Sahil Narang, Andrew Best, Sean Curtis, Dinesh Manocha
PLoS ONE 2015


DenseSense: Interactive Crowd Simulation using Density-Dependent Filters
Andrew Best, Sahil Narang, Sean Curtis, Dinesh Manocha
Eurographics/ ACM SIGGRAPH Symposium on Computer Animation(SCA) 2014


Texture Based Image Retrieval Using Correlation on Haar Wavelet Transform
D. N. Verma, Sahil Narang, Bhawna Juneja
Third International Conference on Advances in Communication, Network, and Computing(CNC) 2012

Other

A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception
Johannes Mohr, Jong Han Park, Klaus Obermayer
Neural Networks, Volume 60, December 2014, Pages 182-193, ISSN 0893-6080