[ML Lunch] Brian Ziebart on Learning Driving Route Preferences

Speaker: Brian Ziebart
Title: Learning Driving Route Preferences
Venue: NSH 1507
Date: Monday April 14
Time: 12:00 noon


Abstract:
Personal Navigation Devices are useful for obtaining drivingdirections to new destinations, but they are not very intelligent --they observe thousands of miles of preferred driving routes but neverlearn from those observations when planning routes to newdestinations. Motivated by this deficiency, we present a novelapproach for recovering from demonstrated behavior the preferenceweights that drivers place on different types of roads andintersections. The approach resolves ambiguities in inversereinforcement learning (Abbeel and Ng 2004) using the principle ofmaximum entropy (Jaynes 1957), resulting in a probabilistic model forsequential actions. Using the approach, we model thecontext-dependent driving preferences of 25 Yellow Cab Pittsburgh taxidrivers from over 100,000 miles of GPS trace data. Unlike previousapproaches to this modeling problem, which directly modeldistributions over actions at each intersection, our approach learnsthe reasons that make certain routes preferable. Our reason-basedmodel is much more generalizable to new destinations and newcontextual situations, yielding significant performance improvements on a number of driving-related prediction tasks.This is joint work with Andrew Maas, Drew Bagnell, and Anind Dey.