I'm Walking Here!: Pedestrian Intent Recognition Identifying Future Pedestrian Trajectory using Machine Learning On-Board an Autonomous Vehicle
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Authors
Miley, Cayler M.
Poston, Jamie E.
Issue Date
2018
Type
Thesis
Language
en_US
Keywords
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Abstract
For autonomous cars to become street viable, they must go beyond sensing pedestrians to
predicting the actions of pedestrians and their future positions. Pedestrian Intent Recognition
(PIR) is a system for predicting future positions and trajectories of pedestrians on
board an autonomous vehicle. Using the OpenPose library as a basis, a model was created
using a feed forward neural network based on 3D skeletal posture to predict future pedestrian
positions. The predictive model results in an accurate depiction of pedestrian actions
as trained on the Carnegie Mellon Mo-Cap Dataset. PIR functions as a proof of concept
that can be adapted to an autonomous vehicle platform. Future work includes adapting
the model to function using LIDAR and multiple RGB cameras as input for a real-time
prediction system for autonomous vehicles.
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License
In Copyright