Neda Hajiakhoond | Searcharxiv

arXiv2018

A Communication Protocol for Man-Machine Networks

One of the most challenging coordination problems in artificial intelligence is to achieve successful collaboration across large-scale heterogeneous systems that include Robots, Agents, and People (RAP). In the best case, these RAP systems are potentially capable of leveraging the strengths of the individual entities to achieve complex distributed tasks. However, without intelligent communication protocols, man-machine partnerships are likely to fail as the humans become overloaded with irrelevant information. This paper introduces a communication protocol for man machine systems and demonstrates that its message routing performance approaches the central optimized solution in a simulated smart environment scenario.

cs.MA↗

arXiv2018

Evaluation of estimation approaches on the quality and robustness of collision warning system

Vehicle safety is one of the most challenging aspect of future-generation autonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as a proposed solution framework, can be relied as the main structure to address the issues in this area. In this framework, information plays a very important role. Each vehicle has access to its own information immediately. However, another vehicle information is available through a wireless communication. Data loss is very common issue for such communication approach. As a consequence, CCW would suffer from providing late or false detection awareness. Robust estimation of lost data is of this paper interest which its goal is to reconstruct or estimate lost network data from previous available or estimated data as close to actual values as possible under different rate of lost. In this paper, we will investigate and evaluate three different algorithms including constant velocity, constant acceleration and Kalman estimator for this purpose. We make a comparison between their performance which reveals the ability of them in term of accuracy and robustness for estimation and prediction based on previous samples which at the end affects the quality of CCW in awareness generation.

eess.SY↗