Maxime Guériau

WAVE: Wave Agents in a Virtual Environment - A Proof of Concept Applied to Longitudinal Distance Regulation in Platoon Control

Since a decade multi-agents became a widespread solution to tackle different kinds of issues in various application fields. Among the two main trends in multi-agent approaches (cognitive vs. reactive), the reactive one is particularly interesting for applications that require both fast response time, adaptability and robustness. Reactive Multi-agent Systems rely on simple interaction schemes between entities that can be inspired whether by Biology or Physics. The goal of this paper is to explore the possibility of using interference fringes and waves properties from a multi-agent standpoint so as to tackle a real problem, longitudinal platoon regulation, already explored by different strategies including particle agents approaches or standard PID controller.

Using Physics Inspired Wave Agents in a Virtual Environment: Longitudinal Distance Control in Robots Platoon

The monitoring and the surveillance of industrial and agricultural sites have become first order tasks mainly for security or the safety reasons. The main issues of this activity is tied to the size of the sites and to their accessibility. Thus, it seems nowadays relevant to tackle with this problem with robots, which can detect potential issues with a low operational cost. To that purpose, in addition to individual patrolling behavior, robots need coordination schemes. The goal of this paper is to explore the possibility of using interference fringes and waves properties in a virtual environment to tackle with the longitudinal distance regulation in the platoon control issue. The proposed model, based on a multi-agent paradigm, is considering each vehicle as an agent wave generator in the virtual environment.

A Constructivist Approach for a Self-Adaptive Decision-Making System: Application to Road Traffic Control

The relevance of decision making in autonomous systems is intrinsically related to the system capacity to discriminate its perception-action states. This is particularly challenging in unknown and changing complex environments, where providing a complete a priori representation to the system is not possible. To illustrate the problem, let us consider a decentralized control of road traffic, where a control device of the distributed infrastructure locally controls traffic, by learning to construct a precise representation (perception-action states) of the traffic state. In this context, it is challenging to define from prior knowledge a relevant representation of the traffic state that enables an efficient recommendation-based control. Without considering a prior domain-knowledge representation, we propose an approach able to combine a set of existing traditional unsupervised learning methods that collaborate as a population of agents in order to build an efficient representation. Our approach follows a constructivist learning perspective, where each agent produces a possible discretization of the raw sensed data. Thanks to a multi-agent reinforcement learning process, the population is able to collectively build a representation that combines the good capacities of the individual ones.

Can C-ITS lead to the emergence of Traffic Management 2.0?

Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee relevant improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management and operations. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS make it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classical methods of traffic modelling and traffic control. We focus in this paper on the potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. The methods for introducing a cooperative modelling framework within existing traffic models are discussed. The paper also provides a building block for Cooperative Traffic Management (CTM). Finally, the potential applications of such blocks are presented from a technical as well as an operational standpoint.

An extension of MovSim for multi-agent cooperative vehicles modeling

We present a multi-agent based extension of a microscopic time continuous lane-based simulator designed to develop cooperative vehicle behaviors within a connected environment. We have chosen to extend the Multi-model Open-source Vehicular-traffic SIMulator (MovSim) which offers a complete traffic simulation platform. By integrating concepts coming from artificial intelligence and related intelligent distributed systems such as multi-agent systems, we aim to model complex individual interactions (including sensors measurements, communication between vehicles and with the infrastructure).