在真实驾驶环境中, 由于感知数据的噪声和其他交通参与者难以预测的行为意图,
自动驾驶汽车如何在高度交互的复杂驾驶环境中考虑不确定性因素的影响, 做出合理的决策 …

Autonomous agents are limited in their ability to observe the world state. Partially observable
Markov decision processes (POMDPs) model planning under world state uncertainty, but …

In this paper, we propose a novel behavior planner that combines game theory with search-
based planning for automated lane merging. Specifically, inspired by human drivers, we …

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially,
in the presence of many aggressive, high-speed traffic participants. This paper presents …

Behavior planning of automated vehicles entails many uncertainties. Partially Observable
Markov Decision Processes (POMDP) are a mathematical framework suited for formulating …

Abstract The framework of Partially Observable Markov Decision Processes (POMDPs)
offers a standard approach to model uncertainty in many robot tasks. Traditionally, POMDPs …

Making effective use of scarce spectrum resources, along with efficient computational
performance, is one of the key challenges for future wireless networks. To tackle this issue …

Partially observable Markov decision processes (POMDP) are a useful model for decision-
making under partial observability and stochastic actions. Partially Observable Monte-Carlo …

The mission of this thesis is to develop algorithms for planning and control of intelligent
mobile robots that operate autonomously in open, interactive environments. Presence of …

Effectively planning robust executions under uncertainty is critical for building autonomous
robots. Partially observable Markov decision processes (POMDPs) provide a standard …