Cooperative driving: an ant colony system for autonomous intersection management

Autonomous intersection management (AIM) is an innovative concept for directing vehicles through the intersections. AIM assumes that the vehicles negotiate the right-of-way. This assumption makes the problem of the intersection management significantly different from the usually studied ones such as the optimization of the cycle time, splits, and offsets. The main difficulty is to define a strategy that improves the traffic efficiency. Indeed, due to the fact that each vehicle is considered individually, AIM faces a combinatorial optimization problem that needs quick and efficient solutions for a real time application. This paper proposes a strategy that evacuates vehicles as soon as possible for each sequence of vehicle arrivals. The dynamic programming (DP) that gives the optimal solution is shown to be greedy. A combinatorial explosion is observed if the number of lanes rises. After evaluating the time complexity of the DP, the paper proposes an ant colony system (ACS) to solve the control problem for large number of vehicles and lanes. The complete investigation shows that the proposed ACS algorithm is robust and efficient. Experimental results obtained by the simulation of different traffic scenarios show that the AIM based on ACS outperforms the traditional traffic lights and other recent traffic control strategies.     

On the line planning problems of the hub public-transportation networks

This paper deals with the problem of line planning that is often posed in the public-transportation planning process. Centred on the hub-based networks, two important phases of this process are treated: the determination of lines and the computation of their frequencies. These two phases are performed to minimize connection times, which is one of the main characteristics of service parameters. As a mathematical tool, we use generalized stochastic Petri nets, which allow performance to be evaluated. It is shown that the Petri net modelling is a powerful technique used to study public-transport systems to assist in the decision-making process. Examples are outlined to illustrate the results.     

Chaotic mixing using source�Csink microfluidic flows in a PDMS chip

We present an active fixed-volume mixer based on the creation of multiple source–sink microfluidic flows in a polydimethylsiloxane (PDMS) chip without the need of external or internal pumps. To do so, four different pressure-controlled actuation chambers are arranged on top of the 5 μl volume of the mixing chamber. After the mixing volume is sealed/fixed by microfluidic valves made using ‘microplumbing technology’, a virtual source–sink pair is created by pressurizing one of the membranes and, at the same time, releasing the pressure of a neighboring one. The pressurized air deforms the thin membrane between the mixing and control chambers and creates microfluidic flows from the squeezed region (source) to the released region (sink) where the PDMS membrane is turned into the initial state. Several schemes of operation of virtual source–sink pairs are studied. In the optimized protocol, mixing is realized in just a sub-second time interval, thanks to the implementation of chaotic advection.     

Adaptive observer design for uncertain nonlinear ODE-PDE cascades

We are revisiting the problem of adaptive observer design for systems that are constituted of an Ordinary Differential Equation (ODE), containing a globally Lipschitz function of the state, and a linear Partial Differential Equation (PDE) of a diffusion–reaction heat type. The ODE and PDE are connected in series and both are subject to parametric uncertainties. In addition to nonlinearity and uncertainty, the system complexity also lies in the fact that no sensor can be implemented at the junction point between the ODE and the PDE. In the absence of parameter uncertainty, nonadaptive state observers are available featuring exponential convergence. However, convergence is guaranteed only under the condition that either the Lipschitz coefficient is sufficiently small or the PDE domain length is sufficiently small. To get around this limitation, and also to account for parameter uncertainty, we develop a design that involves two concatenated adaptive observers, covering the two subintervals of the PDE domain. The proposed design employs one extra sensor, providing the measurement of the PDE state at an inner position close to the ODE-PDE junction point. Both observers are shown to be exponentially convergent, under ad-hoc persistent excitation (PE) conditions, with no limitation on the Lipschitz coefficient and domain length.