Managed information gathering and fusion for transient transport problems

This paper deals with vehicular traffic management by communication technologies from Traffic Control Center point of view in road networks. The global goal is to manage the urban traffic by road traffic operations, controlling and interventional possibilities in order to minimize the traffic delays and stops and to improve traffic safety on the roads. This paper focuses on transient transport, when the controlling management is crucial. The aim was to detect the beginning time of the transient traffic on the roads, to gather the most appropriate data and to get reliable information for interventional suggestions. More reliable information can be created by information fusion, several fusion techniques are expounded in this paper. A half-automatic solution with Decision Support System has been developed to help with engineers in suggestions of interventions based on real time traffic data. The information fusion has benefits for Decision Support System: the complementary sensors may fill the gaps of one another, the system is able to detect the changing of the percentage of different vehicle types in traffic. An example of detection and interventional suggestion about transient traffic on transport networks of a little town is presented at the end of the paper. The novelty of this paper is the gathering of information - triggered by the state changing from stationer to transient - from ad hoc channels and combining them with information from developed regular channels. --information gathering,information fusion,Kalman filter,transient traffic,Decision Support System

Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems

A key challenge in multi-robot and multi-agent systems is generating solutions that are robust to other self-interested or even adversarial parties who actively try to prevent the agents from achieving their goals. The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies. In contrast this paper considers a more realistic class of problems where a team of asynchronous agents with limited observation and communication capabilities need to compete against multiple strategic adversaries with changing strategies. This problem necessitates agents that can coordinate to detect changes in adversary strategies and plan the best response accordingly. Our approach first optimizes a set of stratagems that represent these best responses. These optimized stratagems are then integrated into a unified policy that can detect and respond when the adversaries change their strategies. The near-optimality of the proposed framework is established theoretically as well as demonstrated empirically in simulation and hardware

Fusing Heterogeneous Data for Detection Under Non-stationary Dependence

In this paper, we consider the problem of detection for dependent, non-stationary signals where the non-stationarity is encoded in the dependence structure. We employ copula theory, which allows for a general parametric characterization of the joint distribution of sensor observations and, hence, allows for a more general description of inter-sensor dependence. We design a copula-based detector using the Neyman-Pearson framework. Our approach involves a sample-wise copula selection scheme, which for a simple hypothesis test, is proved to perform better than previously used single copula selection schemes. We demonstrate the utility of our copula-based approach on simulated data, and also for outdoor sensor data collected by the Army Research Laboratory at the US southwest border