Exploiting Map Topology Knowledge for Context-predictive Multi-interface Car-to-cloud Communication

While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability

Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime

This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time -- the time between dawn and sunrise, or between sunset and dusk. The goal of the method is to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions. In addition to the method, a new dataset of road scenes is compiled; it consists of 35,000 images ranging from daytime to twilight time and to nighttime. Also, a subset of the nighttime images are densely annotated for method evaluation. Our experiments show that our method is effective for model adaptation from daytime scenes to nighttime scenes, without using extra human annotation.Comment: Accepted to International Conference on Intelligent Transportation Systems (ITSC 2018

Big data traffic management in vehicular ad-hoc network

Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety

Blockchain Solutions for Multi-Agent Robotic Systems: Related Work and Open Questions

The possibilities of decentralization and immutability make blockchain probably one of the most breakthrough and promising technological innovations in recent years. This paper presents an overview, analysis, and classification of possible blockchain solutions for practical tasks facing multi-agent robotic systems. The paper discusses blockchain-based applications that demonstrate how distributed ledger can be used to extend the existing number of research platforms and libraries for multi-agent robotic systems.Comment: 5 pages, FRUCT-2019 conference pape

Aerial Planning for Flying Taxi of Dubai

Urban air mobility is the future of transportation in cities, and Dubai is one of the first cities to announce the use of air mobility models in the city. The air taxi is expected to be operational in 2023. It is announced to be electric and autonomous and will be the first in the region. The overall goal of the study was to prepare the infrastructure to operate the taxi. The problem was that the Dubai authorities have not yet started this phase, while the target date for operation has already been announced. The basic design of the study involved a mixed analysis approach of qualitative and quantitative data to get the best results for the zones and locations of the stations in Dubai, focusing on the city center and connecting both urban and rural areas in Dubai. The project lays the foundation for one of the most important pillars for the operation of the air taxi, which is the infrastructure. The taxi needs a base map of the area with the flight zones of Dubai to guide its movement and suggest the best route for the taxi. The type and location of stations is also important in the initial phase of operation. The next phase will be to prepare the possible routes for the air taxi and the emergency landing site

International overview on the legal framework for highly automated vehicles

The evolution of Autonomous and automated technologies during the last decades has been constant and maintained. All of us can remember an old film, in which they shown us a driverless car, and we thought it was just an unreal object born of filmmakers imagination. However, nowadays Highly Automated Vehicles are a reality, even not in our daily lives. Hardly a day we don’t have news about Tesla launching a new model or Google showing the new features of their autonomous car. But don’t have to travel far away from our borders. Here in Europe we also can find different companies trying, with more or less success depending on with, not to be lagged behind in this race. But today their biggest problem is not only the liability of their innovative technology, but also the legal framework for Highly Automated Vehicles. As a quick summary, in only a few countries they have testing licenses, which not allow them to freely drive, and to the contrary most nearly ban their use. The next milestone in autonomous driving is to build and homogeneous, safe and global legal framework. With this in mind, this paper presents an international overview on the legal framework for Highly Automated Vehicles. We also present de different issues that such technologies have to face to and which they have to overcome in the next years to be a real and daily technology

Multiagent Systems in Automotive Applications

The multiagent systems have proved to be a useful tool in the design of solutions to problems of distributed nature. In a distributed system, it is possible that the data, the control actions or even both, be distributed. The concept of agent is a suitable notion for capturing situations where the global knowledge about the status of a system is complex or even impossible to acquire in a single entity. In automotive applications, there exist a great number of scenarios of distributed nature, such as the traffic coordination, routes load balancing problems, traffic negotiation among the infrastructure and cars, to mention a few. Even more, the autonomous driving features of the new generation of cars will require the new methods of car to car communication, car to infrastructure negotiation, and even infrastructure to infrastructure communication. This chapter proposes the application of multiagent system techniques to some problems in the automotive field