0
research-article

A Hierarchical Route Guidance Framework for Off-Road Connected Vehicles

[+] Author and Article Information
Judhajit Roy

Ford Motor Company, 20000 Rotunda Dr., Dearborn, MI 48124
jroy@g.clemson.edu

Nianfeng Wan

IAV Automotive Engineering, Inc., 303 Twin Dolphin Drive, Suite 6084, Redwood City, CA 94065
nianfengwan@gmail.com

Angshuman Goswami

Department of Mechanical Engineering, Clemson University, Flour Daniel Building, Clemson, SC 29634
agoswami@g.clemson.edu

Ardalan Vahidi

Department of Mechanical Engineering, Clemson University, Flour Daniel Building, Clemson, SC 29634
avahidi@clemson.edu

Paramsothy Jayakumar

US Army RDECOM TARDEC, 6501 E.11 Mile Road, Warren, MI 48397
paramsothy.jayakumar.civ@mail.mil

Chen Zhang

Ford Motor Company, 20000 Rotunda Dr., Dearborn, MI 48124
czhang56@ford.com

1Corresponding author.

ASME doi:10.1115/1.4038905 History: Received June 19, 2017; Revised December 28, 2017

Abstract

A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large scale high resolution map is mitigated by the proposed hierarchical path planning algorithm. A Dynamic programming (DP) method generates the globally optimal path approximation based on low resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation.

Section 4: U.S. Gov Employees + Reg Authors
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

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 Journal Articles
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