Transferable Pedestrian Motion Prediction Models at Intersections
Name
1804.00495.pdf
Description
Accepted version
Size
3.76 MB
Format
Unknown
Checksum (MD5)
763f4b0c2fee19b6546c98967f79c7c4
Author(s)
Shen, Macheng
Habibi, Golnaz
How, Jonathan P.
Date Issued
September 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shen, Macheng, Habibi, Golnaz and How, Jonathan P. 2019. "Transferable Pedestrian Motion Prediction Models at Intersections."
Version
Author's final manuscript
Abstract
© 2018 IEEE. One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in developing transfer learning algorithms that can be trained on the pedestrian trajectories collected at one intersection and yet still provide accurate predictions of the trajectories at another, previously unseen intersection. We first discussed the feature selection for transferable pedestrian motion models in general. Following this discussion, we developed one transferable pedestrian motion prediction algorithm based on Inverse Reinforcement Learning (IRL) that infers pedestrian intentions and predicts future trajectories based on observed trajectory. We evaluated our algorithm at three intersections. We used the accuracy of augmented semi-nonnegative sparse coding (ASNSC), trained and tested at the same intersection as a baseline. The result shows that the proposed algorithm improves the baseline accuracy by a statistically significant percentage in both non-transfer task and transfer task.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS.2018.8593783
