期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 3, 期 4, 页码 3418-3425出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2018.2852793
关键词
Autonomous vehicle navigation; motion and path planning; planning under uncertainty; crowd motion models
类别
资金
- SMART IRG Grant [R-252-000-655-592]
- Singapore MoE Grant [MOE2016-T2-2-068]
- U.S. ARO Grant [W911NF16-1-0085]
- Intel Corporation
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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