An Investigation into the Performance Evaluation of Connected Vehicle Applications: From Real-World Experiment to Parallel Simulation Paradigm

A novel system was developed that provides drivers lane merge advisories, using vehicle trajectories obtained through Dedicated Short Range Communication (DSRC). It was successfully tested on a freeway using three vehicles, then targeted for further testing, via simulation. The failure of contemporary simulators to effectively model large, complex urban transportation networks then motivated further research into distributed and parallel traffic simulation. An architecture for a closed-loop, parallel simulator was devised, using a new algorithm that accounts for boundary nodes, traffic signals, intersections, road lengths, traffic density, and counts of lanes; it partitions a sample, Tennessee road network more efficiently than tools like METIS, which increase interprocess communications (IPC) overhead by partitioning more transportation corridors. The simulator uses logarithmic accumulation to synchronize parallel simulations, further reducing IPC. Analyses suggest this eliminates up to one-third of IPC overhead incurred by a linear accumulation model

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

Contributions to distributed MPC: coalitional and learning approaches

A growing number of works and applications are consolidating the research area of distributed control with partial and varying communication topologies. In this context, many of the works included in this thesis focus on the so-called coalitional MPC. This approach is characterized by the dynamic formation of groups of cooperative MPC agents (referred to as coalitions) and seeks to provide a performance close to the centralized one with lighter computations and communication demands. The thesis includes a literature review of existing distributed control methods that boost scalability and flexibility by exploiting the degree of interaction between local controllers. Likewise, we present a hierarchical coalitional MPC for traffic freeways and new methods to address the agents' clustering problem, which, given its combinatoria! nature, becomes a key issue for the real-time implementation of this type of controller. Additionally, new theoretical results to provide this clustering strategy with robust and stability guarantees to track changing targets are included. Further works of this thesis focus on the application of learning techniques in distributed and decentralized MPC schemes, thus paving the way for a future extension to the coalitional framework. In this regard, we have focused on the use of neural networks to aid distributed negotiations, and on the development of a multi­ agent learning MPC based on a collaborative data collection

Multimedia communications in vehicular adhoc networks for several applications in the smart cities

[EN] Road safety applications envisaged for vehicular ad hoc networks (VANETs) depend largely on the exchange of messages to deliver information to concerned vehicles. Safety applications as well as inherent VANET characteristics make data dissemination an essential service and a challenging task. We are developing a decentralized efficient solution for broadcast data dissemination through two game-theoretical mechanisms. Besides, VANETs can also include autonomous vehicles (AVs). AVs might represent a revolutionary new paradigm that can be a reality in our cities in the next few years. AVs do not need a driver to work; instead, they should copy a proper human behavior to adapt the driving according to the current circumstances, such as speed limit, pedestrian crossing street or wheather conditions. We will develop an AV software module including artificial intelligence (AI) techniques so that AVs can interact with the dynamic scenario throughout time. Finally, we also will include electrical vehicles (EV) in the VANET, so that special services such as finding and reserving an EV charging station place will be welcome. In addition, we are developing a multimetric geographic routing protocol for VANETs to transmit H.265 video (traffic accident, traffic state, commercial….) over VANETs.This work was partly supported by the Spanish Government through the project TEC2014-54335-C4- 1-R INcident monitoRing In Smart COmmunities, QoS and Privacy (INRISCO). Cristian Iza is recipient of a grant from Secretaria Nacional de Educación Superior, Ciencia y Tecnología SENESCYT. Ahmad Mohamad Mezher is a postdoctoral researcher with the Information Security Group (ISG) at the Universitat Politècnica de Catalunya (UPC).Iza Paredes, C.; Uribe Ramírez, JA.; López Márquez, N.; Lemus, L.; Mezher, A.; Aguilar Igartua, M. (2018). Multimedia communications in vehicular adhoc networks for several applications in the smart cities. Editorial Universitat Politècnica de València. 212-215. https://doi.org/10.4995/JITEL2017.2017.6584OCS21221