Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 3, Issue 4, Pages 3418-3425Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2018.2852793
Keywords
Autonomous vehicle navigation; motion and path planning; planning under uncertainty; crowd motion models
Categories
Funding
- SMART IRG Grant [R-252-000-655-592]
- Singapore MoE Grant [MOE2016-T2-2-068]
- U.S. ARO Grant [W911NF16-1-0085]
- Intel Corporation
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This letter presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is Pedestrian Optimal Reciprocal Collision Avoidance, a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrians' intentions a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a Partially Observable Markov Decision Process algorithm with the pedestrian motion model and runs in real time. Experiments show that it enables a robot scooter to drive safely, efficiently, and smoothly in a crowd with a density of nearly one person per square meter.
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