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

Lecture Notes in Control and Information Sciences, 2019

PDF thumbnail
(PDF, 2.5 MB )


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.


    AUTHOR     = {Alex Cunningham and Enric Galceran and Dhanvin Mehta and Gonzalo
                 Ferrer and Ryan Eustice and Edwin Olson},
    YEAR       = {2019},
    MONTH      = {January},
    PAGES      = {201-223},
    TITLE      = {{MPDM}: Multi-policy Decision-Making from Autonomous Driving to
                 Social Robot Navigation},
    ISBN       = {978-3-319-91568-5},
    JOURNAL    = {Lecture Notes in Control and Information Sciences},
    DOI        = {10.1007/978-3-319-91569-2_10},