Intention-Aware Pedestrian Avoidance
Name
Intention-aware pedestrian avoidance.pdf
Description
Accepted version
Size
3.32 MB
Format
Adobe PDF
Checksum (MD5)
3dc855546d074165c1bff9abf569f503
Author(s)
Bandyopadhyay, Tirthankar
Jie, Chong Zhuang
Hsu, David
Ang, Marcelo H.
Rus, Daniela L
Frazzoli, Emilio
Date Issued
2013
Journal
Experimental Robotics
Publisher
Springer International Publishing
Citation
Bandyopadhyay, Tirthankar et al. "Intention-Aware Pedestrian Avoidance." Experimental Robotics, edited by Jaydev P. Desai et al., Springer International Publishing, 2013, 963-977. © 2013 Springer International Publishing
Version
Author's final manuscript
Abstract
A critical component of autonomous driving in urban environment is the vehicle’s ability to interact safely and intelligently with the human drivers and on-road pedestrians. This requires identifying the human intentions in real time based on a limited observation history and reacting accordingly. In the context of pedestrian avoidance, traditional approaches like proximity based reactive avoidance, or taking the most likely behavior of the pedestrian into account, often fail to generate a safe and successful avoidance strategy. This is mainly because they fail to take into account the human intention and the inherent uncertainty resulting in identifying such intentions from direct observations.
This work formulates the on-road pedestrian avoidance problem as an instance of the Intention-Aware Motion Planning (IAMP) problem, where the human intention uncertainty is incorporated in a principled manner into the planning framework. Assuming a set of all possible pedestrian intentions in the environment, IAMPs generate a Mixed Observable Markov Decision Process (MOMDP), (a factored variant of Partially Obervable Markov Decision Process (POMDP)) with the human intentions being the unobserved variables. Solving the resulting MOMDP generates a robust pedestrian avoidance policy. In spite of the criticism of POMDPs to be computationally intractable in general, we show that with proper state factorization and latest sampling based approaches the policy can be executed online on a real vehicle on road. We demonstrate this by running the algorithm on a real pedestrian crossing in the NUS campus successfully handling the intentions for multiple pedestrians, even when they are jaywalking. In this paper, we present results in simulation to show the improved performance of the proposed approach over existing methods. Additionally, we present results validating experimentally the assumptions made in formulating the intention aware pedestrian avoidance problem.
This work presents a preliminary step towards safer and effective autonomous navigation in urban environments by incorporating the intentions of pedestrians and other drivers on the road.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-319-00065-7_64
