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Found 7 papers, 4 papers with code
Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies
As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption.
Intention-Aware Navigation in Crowds with Extended-Space POMDP Planning
This paper presents a hybrid online Partially Observable Markov Decision Process (POMDP) planning system that addresses the problem of autonomous navigation in the presence of multi-modal uncertainty introduced by other agents in the environment.
Navigation between initial and desired community states using shortcuts
Using several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs; and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices.
Bayesian Optimized Monte Carlo Planning
Monte Carlo tree search with progressive widening attempts to improve scaling by sampling from the action space to construct a policy search tree.
Improving Automated Driving through POMDP Planning with Human Internal States
This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving.
Online algorithms for POMDPs with continuous state, action, and observation spaces
Online solvers for partially observable Markov decision processes have been applied to problems with large discrete state spaces, but continuous state, action, and observation spaces remain a challenge.
The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving
This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled.
