This paper presents SPHERE, a project aimed at the realization of an integrated framework to abstract the hardware complexity of interconnected, modern system-on-chips (SoC) and simplify the management of their heterogeneous computational resources. The SPHERE framework leverages hypervisor technology to virtualize computational resources and isolate the behavior of different subsystems running on the same platform, while providing safety, security, and real-time communication mechanisms. The main challenges addressed by SPHERE are discussed in the paper along with a set of new technologies developed in the context of the project. They include isolation mechanisms for mixed-criticality applications, predictable I/O virtualization, the management of time-sensitive networks with heterogeneous traffic flows, and the management of field-programmable gate arrays (FPGA) to provide efficient implementations for cryptography modules, as well as hardware acceleration for deep neural networks. The SPHERE architecture is validated through an autonomous driving use-case.

The push towards automated and connected driving functionalities mandates the use of heterogeneous HW platforms in order to provide the required computational resources. For these platforms, the established methods for performance modelling in industry are no longer effective. In this paper, we propose an initial modelling concept for heterogeneous platforms which can then be fed into appropriate tools to derive effective performance predictions. The approach is demonstrated for a prototypical automated driving application on the Nvidia Tegra X2 platform.

As autonomous cars are entering mainstream, new research directions are opening involving several domains, from hardware design to control systems, from energy efficiency to computer vision. An exciting direction of research is represented by the coordination of the different vehicles, moving the focus from the single one to a collective system. In this paper we propose some challenging examples thatshow the motivations for a coordination approach in autonomous driving. Moreover, we present some techniques borrowed from distributed artificial intelligence that can be exploited to tackle the previously mentioned challenges.