Habib M. Kammoun

An adaptive vehicle guidance system instigated from ant colony behavior

In view of the high dynamicity of traffic flow and the polynomial increase in the number of vehicles on road networks, the route choice problem becomes more complex. A classical shortest path algorithm based only on road length is no longer relevant. We propose in this paper an adaptive vehicle guidance system instigated from the ants behavior, well known for its good adaptativity; this system allows adjusting intelligently and promptly the route choice according to the real-time changes in the road network situations, such as new congestions and jams. This method is implemented as a deliberative module of a vehicle ant agent in a collaborative multiagent system representing the entire road network. Series of simulations, under a multiagent platform, allow us to discuss the improvement of the global road traffic quality in terms of time, fluidity, and adaptativity.

Towards type-2 fuzzy rule base system for road choice

The road traffic becomes more complex to manage because of the high dynamics of traffic flow and the rise of travel time when the number of vehicles augments in the road networks. Hence, the shortest itinerary based on route length (as provided by GPS navigators) cannot be the best solution nowadays. The application of type-2 Fuzzy Logic is regarded as an effective way for transportation engineering to prevent the problem of ambiguity and uncertainty of road perceptions. In this paper, we propose a hierarchical type-2 Fuzzy Logic System to evaluate itinerary by integrating contextual factors influencing the route choice like speed and road work information.

Improvement of the road traffic management by an ant-hierarchical fuzzy system

In view of dynamicity on road networks and the sharp increase of traffic jam states, the road traffic management becomes more complex. It is clear that the shortest path algorithm based only on road length is no longer relevant.We propose in this paper a hybrid method based on two stages based on ant colony behavior and hierarchical fuzzy system. This method allows adjusting intelligently and promptly the road traffic according to the real-time changes in the road network states by the integration of an adaptive vehicle guidance system. The proposed method is implemented as a deliberative module of a vehicle ant agent in a collaborative multiagent system representing the entire road network. Series of simulations, under a multiagent platform, allow us to discuss the improvement of the global road traffic quality in terms of time, fluidity, and adaptability.

Ethernet video analyzer for vehicle

The great raise in the number of Electronic Control Units (ECUs) transmitting video flows (cameras) and networking vehicular technologies has caused a raised amount of exchanged video data on in-car network. Besides, the real-time audio and video based driver assistance systems have increased swiftly. These developments have imposed new requisites in terms of data rates, efficiency, performance, capacity, etc. To date, the interconnection of in-car stream oriented tools has been accomplished by several automotive networks with restricted transmission capacities. Since IP over Ethernet affords fine conditions for video streaming by recommending high transmission capacities, modern BMW cars exploit Ethernet BroadR-Reach for connecting automotive cameras. In this paper, we will present the Ethernet video analyzer tool that record, store, decode and display the video flow for further tasks such as statistics and camera performance evaluation.

Hybrid Fuzzy-MutiAgent planning for robust mobile robot motion

This paper presents an intelligent hybrid system to support the planning for a mobile robot motion in unknown and dynamic environment. Called Fuzzy-MARCoPlan (Fuzzy-MultiAgent Remote Control motion Planning), this system optimizes the path by the introduction of sub-goals and through a multiagent cooperation based on fuzzy reasoning. In fact, we propose to agentify the surrounding zones of the robot; these zone agents compete for attracting the sub-goal. A planning agent, fortified with a fuzzy rule based system, decides on the best sub-goal to reach. Fuzzy-MARCoPlan is simulated and tested on several navigation environments which are generated randomly under the multiagent platform MadKit. These tests confirm the robustness of the proposed system in terms of path optimality in a dynamic environment. Moreover, the obtained results reinforce the advantage of a multiagent planning hybridized with fuzzy reasoning for mobile robot motion planning.