Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
Author(s)Luders, Brandon Douglas; Ferguson, Sarah K.; Grande, Robert Conlin; How, Jonathan P
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To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.
DepartmentMassachusetts Institute of Technology. Aerospace Controls Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Springer Tracts in Advanced Robotics
Ferguson, Sarah et al. “Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions.” Algorithmic Foundations of Robotics XI. Ed. H. Levent Akin et al. Vol. 107. Cham: Springer International Publishing, 2015. 161–177.