Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detec- tion and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve …
Various types of Multi-Agent Reinforcement Learning (MARL) methods have been developed, assuming that agents' policies are based on true states. Recent works have improved the robustness of MARL under uncertainties from the reward, transition …
Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still have uncertainties on object detection due to practical challenges, …
Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety and efficiency of the CAV system. In this work, we propose a framework …
With the development of sensing and communication technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based methodologies are integrated into the control process of physical systems and demonstrate …
As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and …
As communication technologies develop, an autonomous vehicle will receive information not only from its own sensing system but also from infrastructures and other vehicles through communication. This paper discusses how to exploit a sequence of …
This paper studies an energy management problem for an isolated microgrid including photovoltaic panels, wind turbines, batteries and ultracapacitors. A normal form game is proposed for the energy management to maximize the energy utilization ratio …