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Anouer Bennajeh

Informations générales

Anouer Bennajeh
Grade

Maître Assistant

Biographie courte
  • ACADEMIC CURSUS
    2014–2018: PhD in management computer science, Higher Institute of Management of Tunis, University of Tunis. Doctoral thesis subject: Multi-agent modeling and simulation of non normative behaviors in a road traffic based on a behavioral approach, under the direction of Pr.Lamjed Ben Said (SMART Lab, Tunis-Tunisia) with the co-supervision of Pr. Samir Aknine (LIRIS Lab, Lyon-France), Mention: Very honorable.
    2010–2013: Master in business intelligence and intelligent applied to management, Collaboration between Institute of Higher Commercial Studies, University of Carthage, and Higher School of Commerce of Tunis, University of Manouba, Specialty: Optimization and Intelligent Systems. Master thesis subject: Mobile Agent-Based Web Services Composition. Under the supervision of Dr. Hela Hachicha, Mention: Very Good.
    2007–2010: Fundamental Degree in Management Computer Science, Higher School of Commerce of Tunis, University of Manouba, Subject: Dynamic Website Builder. Under the supervision of Dr. Olfa Ben Ahmed, Mention: Very Good.

 

  • RESEARCH AXES
    Artificial intelligence; Multi-agent system; Fuzzy logic; Multi-objective Optimization; Behavioral approach; intelligent Transportation; Modeling and simulation of driver behavior.

 

  • PROFESSIONAL EXPERIENCE (TEACHING)
    Management Computer Science, Higher Institute of Management of Tunis.
    Management Computer Science, Esprit School of Business.
    Computer Science, Esprit University of Engineering.
    Computer Science, Free University of Tunis.
    Management Computer Science, Higher School of Economic and Commercial Sciences of Tunis.
    Management Computer Science, Higher School of Digital Economy of Manouba.

Axes de recherche

Artificial Intelligence
Multi Agent System
Multi-Objective Optimization
intelligent transportation
Fuzzy logic

Publications

  • 2022
    Anouer Bennajeh, Lamjed Ben Said

    Autonomous agent adaptive driving control based on fuzzy logic theory and normative behavior

    This work presents an adaptive driving model using fuzzy logic and software agents to imitate human car-following behavior. Validated with the Federal Highway Administration dataset, the model shows high similarity with human trajectories., 2022

    Résumé

    Studying driver behaviors has become a major concern for the transportation community, businesses, and the public. Thus, based on the simulation, we proposed an adaptive driving model in the car-following driving behavior and based on the normative behavior of the driver during decision-making and anticipation, whose intention is to ensure the objectives of imitation of ordinary human behavior and road safety. The presented model is based on a software agent paradigm to model a human driver and the Fuzzy Logic Theory to reflect the driver agent’s reasoning. To validate our model, we used the dataset from the program of the US Federal Highway Administration. In this context, we notice an excellent homogeneity in the deviation of the adopted trajectory of the autonomous driver agent from the adopted trajectories by the human drivers. Moreover, the advantage of our model is that it works with different velocities.

  • Anouer Bennajeh, Lamjed Ben Said

    Driving control based on bilevel optimization and fuzzy logic

    This paper addresses the car-following driving control problem using a bilevel optimization approach that considers both leader and follower behaviors. A fuzzy logic-based model is proposed to capture nonnormative driver behavior, validated with real traf, 2021

    Résumé

    Driving control in the car-following (CF) driving behavior has two aspects. First, in what measure an approximation distance is taken as a safe distance guaranteeing the safety of the follower drivers. Second, how to control the follower's vehicle velocities based on the stimulus of the leading vehicle. In this context, to resolve the driving control problem in the CF driving behavior, a bilevel optimization is presented in this paper, based on the behaviors of the follower and leader drivers. Bearing in mind that mathematics has contributed to the imitation of human behaviors, they are now reaching a level of complexity requiring the entry on the scene of a new player, which is artificial intelligence. Thus, in this paper; we used the fuzzy logic theory for modeling a follower driver with a nonnormative behavior. To validate our model, we used a data set from the program of the US Federal Highway Administration. Therefore, according to the experimental results, there is homogeneity between the actual and the simulated travel trajectories in terms of deviation. Besides, the driver's behavior adopted (normative or nonnormative) is reflected in his reactions to the various components of the road.

  • Anouer Bennajeh, Lamjed Ben Said

    Multi-agent cooperation for an active perception based on driving behavior: Application in a car-following behavior

    This paper introduces a five-layer driving model emphasizing perception through visual processing, comprehension, and projection within car-following behavior. Simulation results, using both urban conditions and the NGSIM dataset., 2020

    Résumé

    Perception is presented as a predominant concern in the functioning of a driving system, where it is necessary to understand how the information, events, and actions of each influence the state of the environment and the objectives of the driver, immediately and in the near future. In this context, we present in this paper a driving model composed of five layers which ensure the autonomy and road safety of a driver agent, in particular, we are interested in this article in the concept of perception which is translated by the first three layers of our driving model, which are: visual perception, comprehension and projection, where the execution of these three layers is based on the driving behavior adopted by the driver agent, which is in our case the car-following driving behavior. Furthermore, we present in this paper two simulation scenarios, the first one is realized based on urban area conditions, and the second one is conducted by using Next Generation SIMulation (NGSIM) dataset of a highway in Los Angeles, California. In this context, the experimental results present the effectiveness of our driving model based on the imitation of human behavior and according to reducing the duration of perception.

  • Anouer Bennajeh, Slim Bechikh, Lamjed Ben Said, Samir Aknine

    Bi-level Decision-making Modeling for an Autonomous Driver Agent: Application in the Car-following Driving Behavior

    https://chatgpt.com/c/68d6582b-d000-8326-8778-077d05d845e8, 2019

    Résumé

    Road crashes are present as an epidemic in road traffic and continue to grow up, where, according to World Health Organization; they cause more than 1.24 million deaths each year and 20 to 50 million non-fatal injuries, so they should represent by 2020 the third leading global cause of illness and injury. In this context, we are interested in this paper to the car-following driving behavior problem, since it alone accounts for almost 70% of road accidents, which they are caused by the incorrect judgment of the driver to keep a safe distance. Thus, we propose in this paper a decision-making model based on bi-level modeling, whose objective is to ensure the integration between road safety and the reducing travel time. To ensure this objective, we used the fuzzy logic approach to model the anticipation concept in order to extract more unobservable data from the road environment. Furthermore, we used the fuzzy logic approach in order to model the driver behaviors, in particular, the normative behaviors. The experimental results indicate that the decision to increase in velocity based on our model is ensured in the context of respecting the road safety.