Incremental learning of skills in a task-parameterized Gaussian Mixture Model

The final publication is available at link.springer.comProgramming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model.Peer ReviewedPostprint (author's final draft

An Emulation Framework for Evaluating V2X Communications in C-ITS Applications

C-ITS enhances transportation systems with advanced communication tech, enabling vehicle-to-vehicle and vehicle-to-infrastructure data exchange for real-time decision-making. The thesis explores C-ITS concepts, DSRC, and C-V2X tech, and proposes a versatile C-ITS framework for app prototyping and communication evaluation. Real-world tests and simulations validate its potential to improve road safety and efficiency, suggesting integration opportunities for stakeholders and promoting a smarter, sustainable transportation ecosystem

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

VANET Applications: Hot Use Cases

Current challenges of car manufacturers are to make roads safe, to achieve free flowing traffic with few congestions, and to reduce pollution by an effective fuel use. To reach these goals, many improvements are performed in-car, but more and more approaches rely on connected cars with communication capabilities between cars, with an infrastructure, or with IoT devices. Monitoring and coordinating vehicles allow then to compute intelligent ways of transportation. Connected cars have introduced a new way of thinking cars - not only as a mean for a driver to go from A to B, but as smart cars - a user extension like the smartphone today. In this report, we introduce concepts and specific vocabulary in order to classify current innovations or ideas on the emerging topic of smart car. We present a graphical categorization showing this evolution in function of the societal evolution. Different perspectives are adopted: a vehicle-centric view, a vehicle-network view, and a user-centric view; described by simple and complex use-cases and illustrated by a list of emerging and current projects from the academic and industrial worlds. We identified an empty space in innovation between the user and his car: paradoxically even if they are both in interaction, they are separated through different application uses. Future challenge is to interlace social concerns of the user within an intelligent and efficient driving

Synthetic Datasets for Autonomous Driving: A Survey

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study. We also discuss the role that synthetic dataset plays the evaluation, gap test, and positive effect in autonomous driving related algorithm testing, especially on trustworthiness and safety aspects. Finally, we discuss general trends and possible development directions. To the best of our knowledge, this is the first survey focusing on the application of synthetic datasets in autonomous driving. This survey also raises awareness of the problems of real-world deployment of autonomous driving technology and provides researchers with a possible solution.Comment: 19 pages, 5 figure

Assisted Car Platooning and Congestion Control at Road Intersections

Enhancing road safety and traffic efficiency are the important aspects and goals that automakers and researchers trying to achieve in recent years. The autonomous vehicle technology has been identified as a solution to achieve these goals. However, the adoption of fully autonomous vehicles in the current market is still in the very early stages of deployment. The objective of this paper is to develop a Cooperative Adaptive Cruise Control (CACC) model at a road intersection using platooning car-following mobility models, object detection at traffic light units, and Vehicle-to-Everything (V2X) communication through vehicular ad hoc networks (VANETs). The mobility model considers traffic simulation using the SUMO-PLEXE-VEINS platforms integration. Next, a prototype of an assisted car platooning system consisting of roadside unit (RSU) and on-board units (OBU) is developed using artificial intelligence (AI)-based smart traffic light for obstruction detection at an intersection and modified remote-control cars with V2X communication equipped with in-vehicle alert notification, respectively. The results show accurate detection of obstruction by the proposed assisted car platooning system, and an optimised smart traffic light operation that can reduce congestion and fuel consumption, improve traffic flow, and enhance road safety. The findings from this paper can be used as a baseline for the framework of CACC implementation by legislators, policymakers, infrastructure providers, and vehicle manufacturers

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

Assisted Car Platooning and Congestion Control at Road Intersections

Enhancing road safety and traffic efficiency are the important aspects and goals that automakers and researchers trying to achieve in recent years. The autonomous vehicle technology has been identified as a solution to achieve these goals. However, the adoption of fully autonomous vehicles in the current market is still in the very early stages of deployment. The objective of this paper is to develop a Cooperative Adaptive Cruise Control (CACC) model at a road intersection using platooning car-following mobility models, object detection at traffic light units, and Vehicle-to-Everything (V2X) communication through vehicular ad hoc networks (VANETs). The mobility model considers traffic simulation using the SUMO-PLEXE-VEINS platforms integration. Next, a prototype of an assisted car platooning system consisting of roadside unit (RSU) and on-board units (OBU) is developed using artificial intelligence (AI)-based smart traffic light for obstruction detection at an intersection and modified remote-control cars with V2X communication equipped with in-vehicle alert notification, respectively. The results show accurate detection of obstruction by the proposed assisted car platooning system, and an optimised smart traffic light operation that can reduce congestion and fuel consumption, improve traffic flow, and enhance road safety. The findings from this paper can be used as a baseline for the framework of CACC implementation by legislators, policymakers, infrastructure providers, and vehicle manufacturers

Open Platforms for Connected Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen