MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation

You are here: Homepage > Search

MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation

Cunningham, Alex G. / Galceran, Enric / Mehta, Dhanvin / Ferrer, Gonzalo / Eustice, Ryan M. / Olson, Edwin

Abstract This chapter presents multi-policy decision-making (MPDM): a novel approach to navigating in dynamic multi-agent environments. Rather than planning the trajectory of the robot explicitly, the planning process selects one of a set of closed-loop behaviors whose utility can be predicted through forward simulation that captures the complex interactions between the actions of these agents. These polices capture different high-level behavior and intentions, such as driving along a lane, turning at an intersection, or following pedestrians. We present two different scenarios where MPDM has been applied successfully: an autonomous driving environment models vehicle behavior for both our vehicle and nearby vehicles and a social environment, where multiple agents or pedestrians configure a dynamic environment for autonomous robot navigation. We present extensive validation for MPDM on both scenarios, using simulated and real-world experiments.

Access

Check access

Check availability in my library

Order at Subito €

Export, share and cite

Document information

Title :

MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation

Contributors:

Cunningham, Alex G. (author) / Galceran, Enric (author) / Mehta, Dhanvin (author) / Ferrer, Gonzalo (author) / Eustice, Ryan M. (author) / Olson, Edwin (author)

Published in:

Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions ; 201-223

Lecture Notes in Control and Information Sciences ; 476

Publication date :

2018-06-28

Size :

23 pages

ISBN :

978-3-319-91568-5 , 978-3-319-91569-2

ISSN :

0170-8643 , 1610-7411

DOI :