The AUTOSAR consortium proposed the AUTOSAR Adaptive standard to tackle the challenges introduced by the design of modern automotive systems. It consists of a service-oriented architecture (SoA) implemented in C++ and built on top of POSIX operating systems. However, unlike the previous AUTOSAR Classic specifications, this novel standard does not address non-functional requirements, including determinism, which is of key importance to guarantee the system's functional safety. This paper proposes a modeling extension to the AUTOSAR Adaptive standard aiming at guaranteeing a deterministic execution by leveraging the System-Level Logical Execution Time (SL-LET) paradigm, already used in the context of AUTOSAR Classic. A prototype implementation is also proposed, which is used to experimentally corroborate the feasibility of the proposed model extension with an evaluation based on a realistic automotive application built on the official AUTOSAR Adaptive Platform Demonstrator (APD).

Modern autonomous systems integrate diverse soft-ware solutions to manage tightly communicating functionalities. These applications commonly communicate using frameworks implementing the publish/subscribe paradigm, such as the Data Distribution Service (DDS). However, these frameworks are real-ized with a multi-threaded software architecture and implement internal policies for message dispatching, posing additional chal-lenges for guaranteeing timing constraints. This work addresses the problem of optimizing a DDS-based interconnected real-time systems, proposing analysis-driven algorithms to set a vast range of parameters, ranging from classical thread priorities to other DDS-specific configurations. We evaluate our approaches on the Autoware Reference System, a realistic testbed from the Autoware autonomous driving framework.

In the last decade, enormous and renewed attention to Artificial Intelligence has emerged thanks to Deep Neural Networks (DNNs), which can achieve high performance in performing specific tasks at the cost of a high computational complexity. GPUs are commonly used to accelerate DNNs, but generally determine a very high power consumption and poor time predictability. For this reason, GPUs are becoming less attractive for resource-constrained, real-time systems, while there is a growing demand for specialized hardware accelerators that can better fit the requirements of embedded systems. Following this trend, this paper focuses on hardware acceleration for the DNNs used by Baidu Apollo, an open-source autonomous driving framework. As an experience report of performing R&D with industrial technologies, we discuss challenges faced in shifting from GPU-based to FPGA-based DNN acceleration when per-formed using the DPU core by Xilinx deployed on an Ultrascale+ SoC FPG A platform. Furthermore, it shows pros and cons of today's hardware accelerating tools. Experimental evaluations were conducted to evaluate the performance of FPGA-accelerated DNNs in terms of accuracy, throughput, and power consumption, in comparison with those achieved on embedded GPUs.