0

Full Content is available to subscribers

Subscribe/Learn More  >

A Framework for Autonomous Vehicles With Goal Inference and Task Allocation Capabilities to Support Peer Collaboration With Human Agents

[+] Author Affiliations
Chang Liu, Shih-Yuan Liu, Elena L. Carano, J. Karl Hedrick

University of California, Berkeley, Berkeley, CA

Paper No. DSCC2014-6262, pp. V002T30A005; 10 pages
doi:10.1115/DSCC2014-6262
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4619-3
  • Copyright © 2014 by ASME

abstract

This paper proposes a framework for autonomous vehicles to collaborate with human agents as peers in task completion scenarios. In this framework, the autonomous vehicles utilize the Bayesian inference method to determine human intention. An optimal task allocation that minimizes the mission completion time while respecting the intention of the human agents is developed using the Mixed Integer Linear Programming (MILP) method. The proposed framework can accommodate different levels of suboptimality in human agents’ behavior by adjusting a tunable parameter in the inference model. The effectiveness of the framework in facilitating human-autonomous vehicle collaboration is demonstrated through simulations.

Copyright © 2014 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In