Design automation and synthesis are becoming increasingly crucial in the creation of vehicle electrical and/or electronic (E/E) architectures. This significance arises from the necessity to manage complexity, integrate hardware and software, adapt to technological advancements, and optimize performance. We explore novel aspects aimed at facilitating the synthesis of E/E architectures by extending a developed framework. Our newly incorporated features encompass reliability-based optimization and the integration of hypervisor technologies. To assess the scalability and applicability of our framework to real-world use cases, we conduct multiple experiments, evaluating computation times for architecture generation.

Virtualization plays an increasingly important role in safety-critical embedded systems like modern vehicles due to its numerous advantages like cost reduction, better scalability, or easier safety verification. These advantages come at the cost of additional performance overhead. While performance evaluation of virtualization solutions for server applications has been the subject of research for a long time, it is still not adequately investigated for embedded use cases. We present the first work to analyze the performance of KVM, an open-source hypervisor, on an Nvidia Drive AGX, a developer kit for self-driving vehicles. Experimental measurements show that the computational overhead restricts the number of partitions. Also, poor memory and file I/O performance suggest that KVM is not usable for practical applications with the Nvidia Drive AGX.

In the recent years, the new generation of vehicles has required high-performance computing units due to a large number of safety-critical applications and functionalities. The process of configuring and integrating critical applications into vehicle central hardware while satisfying safety requirements and optimization objectives, is a time-consuming, complicated and error-prone process. This study describes a novel platform to automate the mapping of safety-critical applications to hardware resources on an automotive high-performance central computer while taking into account predefined safety requirements and optimization goals. This platform contains a monitoring mechanism to verify the fulfillment of safety-critical requirements, measure system performance, assess operating system and middleware, and evaluate the deployed mapping. Three modules that make up our proposed platform: E/E Designer, AHPCC, and Monitoring System.

We propose a novel surface mapping algorithm processing sensor data from, e.g., stereo or structure from motion video, triangulating ultrasonic, lidar, or tactile sensors. The algorithm does not rely on a grid and instead constructs a set of polylines as an extremely efficient representation of surrounding surfaces. We use ridges of a continuous occupancy to extract surfaces from location measurements which are subject to noise. Our algorithm for iterative map updates is efficient, stable, unconditionally convergent, gives strong guarantees about the map accuracy, and is invariant under permutation of the input data. It does not suffer from association errors, and operations such as shape updates, splitting, and merging of surfaces arise naturally and are not handled separately. The algorithm corrects wrongly mapped surfaces with future more accurate measurements, and the polyline approximation of the ridge can be tuned to arbitrary accuracy. Runtime measurements demonstrate the feasibility of the approach for embedded applications.

Autonomous driving has attracted a significant amount of attentions over the last ten years.Providing a flexible platform to schedule the executions of the tasks under hard real-time constraints is also a crucial matter which needs to be taken into account by the integration of intelligent applications.In this work, we propose a resource planner, consisting of a monitoring mechanism, context manager, and decision unit which facilitates the timing requirements in the presence of AI-based applications for the autonomous vehicles.

Without meeting safety requirements in the design of electric/electronic (E/E) architectures, achieving fully-automated vehicles is infeasible. However, considering architecture-related safety requirements (e.g. redundancy for fail-operational) in the design phase is a time-consuming task that requires domain-specific knowledge. This paper tackles this challenge by proposing a novel approach under development that takes topological safety aspects into account and transforms them into an optimization problem to generate safe topologies. We aim at accelerating the architecture design process while reducing unnecessary verification efforts as well as avoiding undesired functional safety violations.