URBAN SCALE CROWD DATA ANALYSIS, SIMULATION, AND VISUALIZATION

Transcription

1 URBAN SCALE CROWD DATA ANALYSIS, SIMULATION, AND VISUALIZATION Isaac Rudomin May 2017

2 ABSTRACT We'll dive deep into how we use heterogeneous clusters with GPUs for accelerating urban-scale crowd data analysis, simulation, and visualization. Our main contributions are the development of new behavior models that conform to real data, the ability to scale the system by adding computing resources as needed without making programming modifications and the combination of analysis, simulation, and visualization techniques that help us achieve large-scale crowd simulations with realistic behavior. 2

3 INTRO Why Crowd simulation? One of many massive agent based simulations. Many applications: Videogames, Special events or Emergency simulations, Vehicular traffic, Health What we learn here can be used in other examples of largescale simulation and visualization 3

4 INTRO We have developed methods for realtime crowd simulation and visualization Using several algorithms for collision avoidance and other behaviors Developing methods for generating varied animatable characters (GOD) Using shader based LOD techniques for rendering large crowds For large scale problems we have parallelized simulation using MPI + OmpSs and/or CUDA in heterogeneous clusters visualization by using MPI and composition Work in progress in using XML for specifying GOD for point based hierarchical LOD integrating map applications such as Cesium, Tangram, Mapbox and 3D scenery generated by them integrating real data such as GPS traces to be used to influence simulation using deep learning for modifying behavior 4

5 BLOCK STRUCTURE

6 PW: BEHAVIOR Effective search of neighbours, Collision avoidance Boids (Reynolds), Social Forces (Helbing), Reciprocal Velocity Obstacles, Synthetic Vision

7 PW: BEHAVIOR Physical, Psychological and cultural characteristics of agents, Optimal navigation 7

8 PW: VISION, LEARNING Have used standard vision techniques will use deep reinforcement learning montecarlo tree search to train from trajectories and/or real and simulated video. Using trajectories and spiking neural networks to teach agent to avoid colissions. Work with with Israel Tabarez will continue.

9 PW: GOD GOD: Generate Animate

10 GENERAL DIAGRAM GOD Simulation Simulation Learning Simulation Simulation World & Models ANIMATE LOD DATA STREAM 2D MAP DATA Simulation Simulation Simulation Render Render GO-A-L Render Render Render NEIGHBOR AVOID COLLISION NAVIGATE Composition Composition HETEROGENEOUS Composition CLUSTER Composition Composition Compression IMAGE STREAM 3D Map Camera Display Display Display INPUT-OUTPUT: BROWSER, UNITY, Mapbox HETEROGENEOUS CLUSTER or PC

11 XML FILES: GOD, PBR, H-LOD Used for parameter definition and behavior Geometric attributes Distribution Variety Generation World Distribution Group Definitions Environment Definition and Actors

12 XML FILES: GOD, PBR, H-LOD Templates for XML Definition Texture driven variety generation 12

13 XML FILES: GOD, PBR, H-LOD 13

14 XML FILES: GOD, PBR, H-LOD Geometry reduction Surface splatting Animations are transferable between polygons and point samples for any given level of detail. 14

15 XML FILES: GOD, PBR, H-LOD This structure is used to generate characters varied animated viewable from any camera angle for any given LOD 15

16 XML FILES: GOD, PBR, H-LOD

17 XML FILES: GOD, PBR, H-LOD A tiling system is built on top of a quadtree allowing us to combine geometry from different agents and objects. Each tile is indexed using the quadtree. Characters are indexed as well, knowing at all times in which tile they are currently at. 17

18 XML FILES: GOD, PBR, H-LOD By combining both hierarchical structures, (octree skeleton and quadtree environment) it is possible to create crowds composed by hundreds of thousands of animated characters. Depending on the location of each character LOD is assigned dynamically to reduce computation bottlenecks. 18

19 SYSTEM ARCHITECTURE 19

20 IN DEVELOPMENT webgl output OSM 3D MAP WEB BROWSER CLIENT SERVER (SIMULATION ENGINE) CROWD SIMULATION (2D TILE LEVEL POSITIONS) Data capture Script OUTPUT DATA COLOR TEXTURE (screen). DEPTH TEXTURE. VIRTUAL CAMERA PARAMETERS AND WORLD POSITION OUTPUT DATA WEB SOCKETS CLIENT CAMERA SETTINGS AND DATA CROWD 3D RENDER PRESENTATION OUTPUT RENDER WEB SOCKETS SCREEN COMPOSITION OUTPUT RENDER

21 SIMPLE BEHAVIOR: 1 GPU World: 2D grid of cells empty or occupied by an agent. Collision Avoidance: simple gather method checks 8 directions with radius 5 if another agent in path has same direction its cell is considered a free cell main computation is agent and world updates: with a single GPU once data is in GPU updating & rendering happens on the GPU without data transfers speedup is significant. 21

22 MPI, OMPSS, CUDA Parallel crowd simulation requires dividing the problem in blocks, and for MPI, for OMPSS the idea is the same subdivide world into equal sized (2D) tiles we assign each tile to a node for MPI we assign each subtile, within a node to the CPU or GPU core using OmPSS within the GPUs we use CUDA Double tiling technique tiles and subtiles manage their own agents Four levels of parallelism interchange of agents at borders

23 CLUSTERVISUALIZATION streaming in situ web

24 DATA Data preparation collected from different sources Data Collection Clean and Extraction Data Storage BLOBs Crowd GPS and Video Data NoSQ L Data Analysis and Visualization 24

25 DATA Urban environments OpenStreetMap 25

26 DATA Trajectory Dataset OpenPaths project: Crowds Simulation - 848,000 GPX files Trillion GPX points - and 260GB of GPS data T-Drive trajectory dataset - GPS trajectories of 10,357 taxis within Beijing million of points - and the total distance of 9 million kilometers 26

27 DATA Heatmap Query the data by: day and hour / zone / type of vehicle, / etc... 27

28 DEEP Neural network architecture Input 3 * 20 ReLu FC 150 ReLu FC 150 Output 3 28

29 SCENERY In both systems 2e can import scenery, generate scenery, and we can also compose with the zbuffer generated by other systems, such as the Mapbox Unity plugin or Tangram 29

30 CONCLUSIONS We have a scalable multi agent system architecture Supports the simulation of hundreds of thousands of autonomous agents The crowd rendering engine enables geometrical, visual and animation diversity while maintaining memory requirements low. We have used GLSL/CUDA for data parallelism for systems with one GPU Large scale simulations taking advantage of heterogeneous computing clusters with multiple CPUs and GPUs real-time simulation on clusters using CUDA MPI, OmPSS streaming and in-situ+composition visualization of the results Working on Using real maps and trajectories

31 ACKNOWLEDGEMENTS This work is supported by the Spanish Government through Programa Severo Ochoa (SEV ) by the Spanish Ministry of Science and Technology (project TIN P). by CONACyT, Mexico through the Barcelona Supercomputing Center Centro Nacional de Supercomputación Consejo Nacional de Ciencia y Tecnología Convocatoria 2016 para Estancias Posdoctorales by CONACyT, Mexico PhD Scholarship program

32 MORE INFO Isaac Rudomin(BSC) Hugo Perez(UPC-BSC) Leonel Toledo (BSC) Jorge Eduardo Ramirez(BSC) 32