8 citations · 31 across the 11 of their papers we have counts for
12 papers
Safe adaptation in multiagent competition
Macheng Shen, Jonathan P. How
Achieving the capability of adapting to ever-changing environments is a critical step towards building fully autonomous robots that operate safely in complicated scenarios. In mult…
Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph
Chuangchuang Sun, Macheng Shen, Jonathan P. How
The complexity of multiagent reinforcement learning (MARL) in multiagent systems increases exponentially with respect to the agent number. This scalability issue prevents MARL from…
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games
Macheng Shen, Jonathan P. How
This paper presents an algorithmic framework for learning robust policies in asymmetric imperfect-information games, where the joint reward could depend on the uncertain opponent t…
Active Perception in Adversarial Scenarios using Maximum Entropy Deep Reinforcement Learning
Macheng Shen, Jonathan P How
We pose an active perception problem where an autonomous agent actively interacts with a second agent with potentially adversarial behaviors. Given the uncertainty in the intent of…
Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks
Huajing Zhao, Zhaobin Mo, Macheng Shen +2
In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism mak…
Transferable Pedestrian Motion Prediction Models at Intersections
Macheng Shen, Golnaz Habibi, Jonathan P. How
One desirable capability of autonomous cars is to accurately predict the pedestrian motion near intersections for safe and efficient trajectory planning. We are interested in devel…
Semi-Interpenetrating Cooperative Localization in Connected Vehicle Networks
Macheng Shen, Huajing Zhao, Jing Sun +1
We proposed a fusion mechanism for the distributed cooperative map matching (CMM) within the vehicular ad-hoc network. This mechanism makes the information from each node reachable…
The Impact of Road Configuration in V2V-based Cooperative Localization: Mathematical Analysis and Real-world Evaluation
Macheng Shen, Jing Sun, Ding Zhao
Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. It ha…
Improving Localization Accuracy in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters: Theory, Simulations, and Experiments
Macheng Shen, Ding Zhao, Jing Sun +1
A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost Global Navigation Satellite System (GNSS)…
The Impact of Road Configuration on V2V-based Cooperative Localization
Macheng Shen, Ding Zhao, Jing Sun
Cooperative localization with map matching has been shown to reduce Global Navigation Satellite System (GNSS) localization error from several meters to sub-meter level by fusing th…
Optimization of Vehicle Connections in V2V-based Cooperative Localization
Macheng Shen, Jing Sun, Ding Zhao
Cooperative map matching (CMM) uses the Global Navigation Satellite System (GNSS) positioning of a group of vehicles to improve the standalone localization accuracy. It has been sh…
Enhancement of Low-cost GNSS Localization in Connected Vehicle Networks Using Rao-Blackwellized Particle Filters
Macheng Shen, Ding Zhao, Jing Sun
An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane-level accuracy with low-cost Global Navigation Satellit…