Traffic Control via Platoons of Intelligent Vehicles for Saving Fuel Consumption in Freeway Systems

International audienceIn this paper a coupled PDE-ODE model describing the interaction between the bulk traffic flow and a platoon of connected vehicles is adopted to develop a control action aiming at reducing the fuel consumption of the overall traffic flow. The platoon is modeled as a capacity restriction acting on the surrounding traffic. The trajectory of the initial and final points of the platoon are optimized by means of a model predictive control strategy, acting on the speeds of the front-end and back-end of the platoon, thus resulting in controlling both the speed and the length of the platoon. The approach is assessed in simulations

A macroscopic model for platooning in highway traffic

We consider a model describing the presence of a platoon of vehicles moving in the traffic flow. The model consists of a coupled PDE-ODE system describing the interaction between the platoon and the surrounding traffic flow. The scalar conservation law takes into account the main traffic evolution, while the ODEs describe the trajectories of the initial and final points of the platoon, whose length can vary in time. The presence of the platoon acts as a road capacity reduction, resulting in a space-time discontinuous flux function. We describe the solutions of Riemann problems and design a finite volume numerical scheme sharply capturing non-classical discontinuities. Some numerical tests are presented to show the effectiveness of the method

Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems

We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies

A macroscopic model for platooning in highway traffic

International audienceWe consider a model describing the presence of a platoon of vehicles moving in the traffic flow. The model consists of a coupled PDE-ODE system describing the interaction between the platoon and the surrounding traffic flow. The scalar conservation law takes into account the main traffic evolution, while the ODEs describe the trajectories of the initial and final points of the platoon, whose length can vary in time. The presence of the platoon acts as a road capacity reduction, resulting in a space-time discontinuous flux function. We describe the solutions of Riemann problems and design a finite volume numerical scheme sharply capturing non-classical discontinuities. Some numerical tests are presented to show the effectiveness of the method

Deep Learning for Abstraction, Control and Monitoring of Complex Cyber-Physical Systems

Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty.Cyber-Physical Systems (CPS) consist of digital devices that interact with some physical components. Their popularity and complexity are growing exponentially, giving birth to new, previously unexplored, safety-critical application domains. As CPS permeate our daily lives, it becomes imperative to reason about their reliability. Formal methods provide rigorous techniques for verification, control and synthesis of safe and reliable CPS. However, these methods do not scale with the complexity of the system, thus their applicability to real-world problems is limited. A promising strategy is to leverage deep learning techniques to tackle the scalability issue of formal methods, transforming unfeasible problems into approximately solvable ones. The approximate models are trained over observations which are solutions of the formal problem. In this thesis, we focus on the following tasks, which are computationally challenging: the modeling and the simulation of a complex stochastic model, the design of a safe and robust control policy for a system acting in a highly uncertain environment and the runtime verification problem under full or partial observability. Our approaches, based on deep learning, are indeed applicable to real-world complex and safety-critical systems acting under strict real-time constraints and in presence of a significant amount of uncertainty

Robustified distributed model predictive control for coherence and energy efficiency-aware platooning

Platooning has become one of the most appealing formations for intelligent vehicles safety enhancement and traffic regulation. Besides the traditional control algorithms, which are required to enforce at least local and string stability, more complex control schemes can be designed to cope with advanced requirements. In this paper, a suitable Distributed Model Predictive Control (DMPC) scheme, robustified with a second -order Integral Sliding Mode (ISM) correction term, is proposed to enforce and maintain coherence during cruising, while considering energy efficiency during acceleration/deceler-ation phases. While the former aspect has a complex impact on traffic regulation, especially when a large number of vehicles is considered, the latter is of primary importance in an increasingly eco-friendly transportation systems design. The proposed approach is well suited for real-world implementation, and can constitute a valid basis for more advanced control architectures. Simulation results highlight the effectiveness of the proposed architecture in maintaining the formation while guaranteeing a robust achievement of the required performance