skip to main content
10.1145/1073368.1073371acmconferencesArticle/Chapter ViewAbstractPublication PagesscaConference Proceedingsconference-collections
Article

Autonomous pedestrians

Published: 29 July 2005 Publication History

Abstract

We address the difficult open problem of emulating the rich complexity of real pedestrians in urban environments. Our artificial life approach integrates motor, perceptual, behavioral, and cognitive components within a model of pedestrians as individuals. Our comprehensive model feature innovations in these components, as well as in their combination, yielding results of unprecedented fidelity and complexity for fully autonomous multi-human simulation in a large urban environment. We represent the environment using hierarchical data structures, which efficiently support the perceptual queries of the autonomous pedestrians that drive their behavioral responses and sustain their ability to plan their actions on local and global scales.

References

[1]
{ALA*01} Ashida K., Lee S., Allbeck J., Sun H., Badler N., Metaxas D.: Pedestrians: Creating agent behaviors through statistical analysis of observation data. In Proc. IEEE Conf. on Computer Animation (Seoul. Korea, 2001), pp. 84--92.
[2]
{BA00} Blue V., Adler J.: Cellular automata model of emergent collective bi-directional pedestrian dynamics. In Proc. Artificial Life VII (August 2000), pp. 437--445.
[3]
{BJTG98} Batty M., Jiang B., Thurstain-Goodwin M.: Local movement: Agent-based models of pedestrian flow. Center for Advanced Spatial Analysis Working Paper Series 4 (1998).
[4]
{BMS04} Botea A., Müller M., Schaeffer J.: Near optimal hierarchical path-finding. Journal of Game Development 1, 1 (2004), 7--28.
[5]
{BPW93} Badler N., Phillips C., Webber B.: Simulating Humans: Computer Graphics, Animation, and Control. Oxford University Press, 1993.
[6]
{FMS*00} Farenc N., Musse S., Schweiss E., KALLMANN M., AUNE O., BOULIC R., THALMANN D.: A paradigm for controlling virtual humans in urban environment simulations. Applied Artificial Intelligence 14, 1 (2000), 69--91.
[7]
{FTT99} Funge J., Tu X., Terzopoulos D.: Cognitive modeling: Knowledge, reasoning and planning for intelligent characters. In Proc. SIGGRAPH 99 (1999), pp. 29--38.
[8]
{GKM*01} Goldenstein S., Karavelas M., Metaxas D., Guibas L., Aaron E., Goswami A.: Scalable nonlinear dynamical systems for agent steering and crowd simulation. Computers & Graphics 25, 6 (2001), 983--998.
[9]
{GM85} Gipps G., Marksjo B.: A micro-simulation model for pedestrian flows. Mathematics and Computers in Simulation 27 (1985), 95--105.
[10]
{HM95} Helbing D., Molnar P.: Social force model for pedestrian dynamics. Physical Review 51, 5 (1995), 4282--4286.
[11]
{KCR98} Koechling J., Crane A., Raibert M.: Applications of realistic human entities using DI-Guy. In Proc. Spring Simulation Interoperability Workshop (Orlando, FL., 1998).
[12]
{LD04} Lamarche F., Donikian S.: Crowd of virtual humans: A new approach for real time navigation in complex and structured environments. Computer Graphics Forum 23, 3 (2004), 509--518.
[13]
{LMM03} Loscos C., Marchal D., Meyer A.: Intuitive crowd behaviour in dense urban environments using local laws. In Theory and Practice of Computer Graphics (2003), IEEE, pp. 122--129.
[14]
{Lov93} Lovas G. G.: Modeling and simulation of pedestrian traffic flow. In Proc. European Simulation Multiconference (1993).
[15]
{LRBW04} Loyall A. B., Reilly W. S. N., Bates J., Weyhrauch P.: System for authoring highly interactive, personality-rich interactive characters. In SIGGRAPH/EG Symposium on Computer Animation (2004), pp. 59--68.
[16]
{MH03} Metoyer R., Hodgins J.: Reactive pedestrian path following from examples. In Computer Animation and Social Agents (2003), pp. 149--156.
[17]
{MT01} Musse S., Thalmann D.: Hierarchical model for real time simulation of virtual human crowds. IEEE Transactions on Visualization and Computer Graphics 7, 2 (2001), 152--164.
[18]
{NRTMT95} Noser H., Renault O., Thalmann D., Magnenat-Thalmann N.: Navigation for digital actors based on synthetic vision, memory and learning. Computers and Graphics 19, 1 (1995).
[19]
{Rey87} Reynolds C. W.: Flocks, herds, and schools: A distributed behavioral model. Proc. SIGGRAPH 87, Computer Graphics 21, 4 (July 1987), 25--34.
[20]
{Rey99} Reynolds C. W.: Steering behaviors for autonomous characters. In Proc. Game Developers Conf. (1999), pp. 763--782.
[21]
{Sch02} Schadschneider A.: Traffic flow: A statistical physics point of view. Physica A313 (2002), 153--187.
[22]
{SE01} Schreckenberg M., (EDS.) S. S.: Pedestrian and Evacuation Dynamics. Springer-Verlag, 2001.
[23]
{SGC04} Sung M., Gleicher M., Chenney S.: Scalable behaviors for crowd simulation. Computer Graphics Forum 23, 3 (2004), 519--528.
[24]
{ST05} Shao W., Terzopoulos D.: Environmental modeling for autonomous virtual pedestrians. In SAE Symposium on Digital Human Modeling for Design and Engineering. (Iowa City, IA, 2005).
[25]
{TDB*02} Tomlinson B., Downie M., Berlin M., Gray J., Lyons D., Cochran J., Blumberg B.: Leashing the alphawolves: Mixing user direction with autonomous emotion in a pack of semi-autonomous virtual characters. In SIGGRAPH/EG Symposium on Computer Animation (2002), pp. 7--14.
[26]
{TT94} Tu X., Terzopoulos D.: Artificial fishes: Physics, locomotion, perception, behavior. In Proc. SIGGRAPH 94 (July 1994), pp. 43--50.
[27]
{UdHCT04} Ulicny B., De Heras Ciechomski P., Thalmann D.: Crowdbrush: Interactive authoring of real-time crowd scenes. In SIGGRAPH/EG Symposium on Computer Animation (2004), pp. 243--252.

Cited By

View all
  • (2024)A GPU-based hydrodynamic simulator with boid interactionsParallel Computing10.1016/j.parco.2023.103062119:COnline publication date: 1-Feb-2024
  • (2024)Agent-based crowd simulation: an in-depth survey of determining factors for heterogeneous behaviorThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03503-240:7(4993-5004)Online publication date: 1-Jul-2024
  • (2023)Visualization and Bibliometric Analysis of Research Evolution on Digital HumanProceedings of the 2023 6th International Conference on Big Data Technologies10.1145/3627377.3627404(181-188)Online publication date: 22-Sep-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SCA '05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
July 2005
366 pages
ISBN:1595931988
DOI:10.1145/1073368
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 July 2005

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Article

Conference

SCA05
Sponsor:
SCA05: Symposium on Computer Animation
July 29 - 31, 2005
California, Los Angeles

Acceptance Rates

Overall Acceptance Rate 183 of 487 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)49
  • Downloads (Last 6 weeks)5
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2024)A GPU-based hydrodynamic simulator with boid interactionsParallel Computing10.1016/j.parco.2023.103062119:COnline publication date: 1-Feb-2024
  • (2024)Agent-based crowd simulation: an in-depth survey of determining factors for heterogeneous behaviorThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03503-240:7(4993-5004)Online publication date: 1-Jul-2024
  • (2023)Visualization and Bibliometric Analysis of Research Evolution on Digital HumanProceedings of the 2023 6th International Conference on Big Data Technologies10.1145/3627377.3627404(181-188)Online publication date: 22-Sep-2023
  • (2023)Heterogeneous Crowd Simulation Using Parametric Reinforcement LearningIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.313903129:4(2036-2052)Online publication date: 1-Apr-2023
  • (2023)Crowd evacuation simulation in flowing fluidsComputer Animation and Virtual Worlds10.1002/cav.216134:3-4Online publication date: 20-May-2023
  • (2022)Interaction in Social SpaceThe Handbook on Socially Interactive Agents10.1145/3563659.3563662(3-44)Online publication date: 27-Oct-2022
  • (2022)CCP: Configurable Crowd ProfilesACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530712(1-10)Online publication date: 27-Jul-2022
  • (2022)The Handbook on Socially Interactive AgentsundefinedOnline publication date: 27-Oct-2022
  • (2021)Modeling Data-Driven Dominance Traits for Virtual Characters Using Gait AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.295306327:6(2967-2979)Online publication date: 1-Jun-2021
  • (2020)Realistic Simulation of Cultural HeritageNatural Language Processing10.4018/978-1-7998-0951-7.ch064(1314-1347)Online publication date: 2020
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media