2 research outputs found
Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
Trajectory prediction is one of the key capabilities for robots to safely
navigate and interact with pedestrians. Critical insights from human intention
and behavioral patterns need to be integrated to effectively forecast long-term
pedestrian behavior. Thus, we propose a framework incorporating a Mutable
Intention Filter and a Warp LSTM (MIF-WLSTM) to simultaneously estimate human
intention and perform trajectory prediction. The Mutable Intention Filter is
inspired by particle filtering and genetic algorithms, where particles
represent intention hypotheses that can be mutated throughout the pedestrian
motion. Instead of predicting sequential displacement over time, our Warp LSTM
learns to generate offsets on a full trajectory predicted by a nominal
intention-aware linear model, which considers the intention hypotheses during
filtering process. Through experiments on a publicly available dataset, we show
that our method outperforms baseline approaches and demonstrate the robust
performance of our method under abnormal intention-changing scenarios. Code is
available at https://github.com/tedhuang96/mifwlstm.Comment: Accepted by RA-L Special Issue on Long-Term Human Motion Predictio
Online monitoring for safe pedestrian-vehicle interactions
As autonomous systems begin to operate amongst humans, methods for safe
interaction must be investigated. We consider an example of a small autonomous
vehicle in a pedestrian zone that must safely maneuver around people in a
free-form fashion. We investigate two key questions: How can we effectively
integrate pedestrian intent estimation into our autonomous stack. Can we
develop an online monitoring framework to give formal guarantees on the safety
of such human-robot interactions. We present a pedestrian intent estimation
framework that can accurately predict future pedestrian trajectories given
multiple possible goal locations. We integrate this into a reachability-based
online monitoring scheme that formally assesses the safety of these
interactions with nearly real-time performance (approximately 0.3 seconds).
These techniques are integrated on a test vehicle with a complete in-house
autonomous stack, demonstrating effective and safe interaction in real-world
experiments.Comment: 15 pages, 5 figures