Daniel Jolly

Object association with belief functions, an application with vehicles

The problem tackled in this article consists in associating perceived objects detected at a certain time with known objects previously detected, knowing uncertain and imprecise information regarding the association of each perceived objects with each known objects. For instance, this problem can occur during the association step of an obstacle tracking process, especially in the context of vehicle driving aid. A contribution in the modeling of this association problem in the belief function framework is introduced. By interpreting belief functions as weighted opinions according to the Transferable Belief Model semantics, pieces of information regarding the association of known objects and perceived objects can be expressed in a common global space of association to be combined by the conjunctive rule of combination, and a decision making process using the pignistic transformation can be made. This approach is validated on real data.

Evidential calibration process of multi-agent based system

Highlights? We present a problem of calibration and validation of multi-agent based simulations. ? Simulation validation consists to measure if the simulation is close to a reality. ? Before the validation, calibration is a process to define the model parameters. ? We employ the belief function to deal imperfect data, used in validation/calibration. ? This global process is illustrated with an application to forensic entomology. Forensic entomology consists, during a criminal investigation, in studying the insects found on a cadaver to estimate the time of death. This is the only technique that can be used for a large post-mortem interval. But, because of the important system complexity, the result given by the expert are imperfect. In this paper, a decision support system (DSS) has been developed to take into account all the ecosystemic parameters and a significant quantity of biological models. The proposed DSS is based on the belief function theory to validate and calibrate agent based simulations. First results of this architecture are presented within the framework of a real forensic examination.

Improvement of an association algorithm for obstacle tracking

This article describes a modification of an association algorithm for object tracking based on the evidence theory. This association algorithm was first developed by Rombaut and subsequently improved in a general way by Gruyer. This algorithm has been modified here in order to obtain better results when data reliability is poor. This article presents the basic concepts of the evidence theory. Then, the association algorithm developed by Rombaut is explained, and some examples are given to show that this algorithm fails to give the proper decision when data reliability decreases. Finally, the new algorithm is presented and the two algorithms are compared using synthetic data. In order to test the robustness of the two algorithms, they were also tested using real data coming from a CCD camera and these data can be qualified as very noisy with a reliability ranging from good to very bad.

The macroscopic LWR model of the transport equation viewed as a distributed parameter system

The main aim of this paper is the obtaining of transfer function of the macroscopic traffic flow model using an exact analytical method of solution of the non-linear partial differential equations, presenting the transportation problem model as distributed parameter system. The different analytical methods of solution are shown, some of them giving an exact solution. Using this exact method we obtain directly the plant transfer function for the traffic flow model viewed as a distributed parameter system. The entire physical phenomenon is presenting through the partial differential equations whose show the distribution in vehicles on the high ways. All the research is devoted on the transfer function result because of the control system modeling. In the presenting paper we shown the verification of the analytical result of the model simulation thought the time and frequency characteristics analysis.

MACRO-MICRO SIMULATION OF TRAFFIC FLOW

Abstract This paper presents the recent progress in the development of the hybrid concept for the traffic simulation. The main objective is to couple two traffic representation models with different scale levels. The first allows a global traffic representation providing the possibility to simulate a large network. The second model focuses on the individual interactions of vehicles and allows then to understand the phenomena of the microscopic point of view. The paper shows that the proposed approach is generic allowing the cohabitation of various macroscopic models with the microscopic model developed using the agent paradigm. It presents also some simulation results that illustrate the relevance of the elaborated approach.

High order sliding mode control for real-time ramp metering

We propose the application of the High Order Sliding Mode Control (HOSMC) and the concept of differentially flat systems in order to solve the ramp metering problem. The main characteristic of differential flatness is that, the whole system behavior is described by the trajectory of a so called “flat output” and a number of its successive time-derivatives. This leads to a simple design of trajectories without integration of any differential equation. On the other hand, HOSMC is known to be a robust control method appropriate for uncertain systems. The effectiveness of the proposed approach is demonstrated via some simulation results from a case study and enable to foresee several very promising further developments for ramp metering and integrated traffic flow control.

Sliding Mode Control and Flatness-Based Concept for Real-Time Ramp Metering

Abstract The aim of this paper is to present an application of a sliding mode control (SMC) and flatness-based concept to real-time ramp metering. Such application is a novel attempt in the field of traffic control. Differentially flat concept provides simple algorithms to generate optimal trajectories, without integration of any differential equation. On the other hand, SMC is known to be a robust control method appropriate for uncertain systems such that the traffic ones. The proposed approach is based on the well-known space discrete first order macroscopic model. A simple case study shows very promising results for further works including traffic control for more complex motorway network.

Traffic network ramp metering based on High Order Sliding Mode and flatness approaches: A case study

Freeway traffic control is achieved using High Order Sliding Mode Control (HOSMC) and the concept of differentially flat systems. The main objective of this paper, which follows the work in [22] is to evaluate the impact of the traffic ramp metering on the freeway network using real field data of the French motorway A6.

Coordination of Ramp Metering and Variable Speed Limits

Abstract In Kamel et al. (2009), we have proposed a local ramp metering control algorithm to reduce the congestion and consequently the total time spent by the vehicles in the network. In this paper, we extend this control algorithm and propose a control approach for the coordination of ramp metering and variable speed limits. In order to evaluate the controllers efficiency and applicability, a comparison is made with the ramp metering only case and the results related to the Genetic-Fuzzy Control Application to Ramp Metering and Variable Speed Limit Control considered in Ghods et al. (2007). The macroscopic traffic model METANET and its extension for dynamic speed limits are used to present the simulation results. We conclude that the proposed coordination of ramp metering and speed limits approach improve the performance of the freeway traffic network.

Distributed model predictive control of freeway traffic networks: A serial partially cooperative approach

In this paper, a new distributed model predictive control (MPC) scheme for freeway traffic control is proposed. It is aimed at reducing the communication efforts and the computation times in a large network. This new algorithm can coordinate a large number of on-ramps throughout a freeway network in a partially cooperative scheme. The communication is performed between neighboring on-ramps in a special serial fashion and with three different proposed cooperative schemes. The computation time is much less than that of existing distributed MPC approaches in the literature, while achieving a performance close to the one of the centralized MPC method. To evaluate the performance of the proposed partially cooperative schemes, a freeway network case study is selected and the problem of coordination between several on-ramps is solved using different methods from a centralized approach to a fully decentralized one. The obtained results show a significant decrease in the total computation time with respect to the centralized and fully cooperative schemes, while maintaining a close distance to the optimal objective function obtained from the centralized case. Furthermore, the performance of the proposed partially cooperative MPC method is evaluated in the case of incidents in the network.