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Object association is a crucial step in target tracking and data fusion applications. This task can be formalized as the search for a relation between two sets (e.g., a sets of tracks and a set of observations) in such a way that each object in one set is matched with at most one object in the other set. In this paper, this problem is tackled using the formalism of belief functions. Evidence about the possible association of each object pair, usually obtained by comparing the values of some attributes, is modeled by a Dempster-Shafer mass function defined in the frame of all possible relations. These mass functions are combined using Dempster's rule, and the relation with maximal plausibility is found by solving an integer linear programming problem. This problem is shown to be equivalent to a linear assignment problem, which can be solved in polynomial time using, for example, the Hungarian algorithm. This method is demonstrated using simulated and real data. The 3-D extension of this problem (with three object sets) is also formalized and is shown to be NP-Hard.

In this paper, we present a distributed approach to build a dynamic map in the context of VANets (Vehicular Ad hoc Networks). It is based on the principle of cooperative perception where vehicles work as a team in order to extend their field of view. Each vehicle is equipped with sensors allowing it to detect its environment and to build its map, denoted by local map. It receives messages from other vehicles containing mobile objects detected in their surroundings. The algorithm of distributed dynamic map builds a map of the dynamic environment including objects in the sensor's field of view as well as those sent by other vehicles. This algorithm is developed under the belief functions framework. The implementation of such an application is complex and needs many treatments: temporal and spatial alignment, object association, fusion of messages and data dissemination. This approach has been validated by simulation on scenario involving several vehicles in traffic situation.

Sybil attacks have become a serious threat as they can affect the functionality of VANETs (Vehicular Ad Hoc Networks). This paper presents a method for detecting such attacks in VANETs based on distributed data fusion. An algorithm has been developed in order to build distributed confidence over the network under the belief function framework. Our approach has been validated by simulation.

This chapter is devoted to illustrate and characterize the relationship between Swarm Intelligence and cooperation among robots. Individuals with very limited computational capabilities are able to carry out very complex tasks when they can work together. From a methodological point of view, Swarm Intelligence is a set of heuristic solutions inspired by animal swarm behaviors and capable to o↵er empirical solutions to many computationally hard problems pertaining to several disciplines. In this chapter, we will try to outline the main research directions in Swarm Intelligence implementation within a robot network through the cooperation among the robots. The latter topic will be presented along with its advantages, issues and challenges. The convergence of robot cooperation and Swarm Intelligence is leading towards a new discipline, called Swarm Robotics. In this chapter, we will introduce this new field of study, its most relevant works and its main research directions.