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Assisted/autonomous driving is nowadays a vivid sector for both research and industry, thanks to the advances made using artificial intelligence and the pervasive and fast communication achieved by the Fifth generation (5G) of cellular networks. A fully connected environment where traveling on public roads is done with limited (or without) human intervention may increase road safety. In this work, we present a system to detect possible collisions among vehicles and between pedestrians and vehicles with the final aim of reduce traffic accidents. Our proposal is based on a trajectory prediction algorithm plus a method to estimate the collision probability. Deep learning and Monte Carlo algorithms are used, respectively. The promising results open future research extensions.

The introduction of AI-based smart-sensors on the network might suppose stringent requirements for the network edge, including the necessity to process real-time video feeds. Moreover, the introduction of vehicular communications allows the multiple location placement of necessary computational processes. To this end, we have proposed an AI-based smart- sensor solution that is able to be deployed at the extreme edge of the network (i.e., on the vehicle). The architecture for the connected vehicle is presented and accuracy results are provided for the proposed smart-sensor.

Connected and Automated Mobility (CAM) and Future Railway Mobile Communications System (FRMCS) use cases present strict requirements in terms of service end-to-end latencies, data rates, reliability and service availability, posing challenges in the design of 5G network architectures for cross- border scenarios, where current roaming mechanisms introduce large latencies and service disruption times. The EU-funded Horizon 2020 5GMED project proposes a novel 5G network architecture for the Mediterranean corridor cross-border between Spain and France, where the challenges imposed by CAM and FRMCS in cross-border scenarios are addressed. In this paper, we describe cross-border challenges and present the 5GMED network architecture that will be deployed and evaluated in largescale trials. © 2022 IEEE., ACKNOWLEDGMENT This work was supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 951947 (5GMED), by the grant ONOFRE-3 PID2020-112675RB-C43 funded by MCIN/AEI/10.13039/501100011033, and by the regional FEDER FEMIoT P1(IU16-011655) project. © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.

In this paper a commuting hierarchy of flows including the full Kostant-Toda equation is studied. A Mulase’s approach which uses the so-called Borel-Gauss decomposition leads to explicit rational solutions of the full Kostant-Toda equation. Finally, we investigate how the solution blows up in finite time in the case of matrices of order 2.