Multiunit automotive perception framework: Synergy between AI and deterministic processing Since neural networks were first introduced into automotive systems, safety has been a major concern. The prevailing safety standard in the automotive industry, ISO26262, does not fully define testing and verification methods for software based on deep learning. In this paper, we propose a multiunit perception framework that increases the determinism of automotive systems incorporating deep learning. Our approach relies on ASIL decomposition and algorithm diversification, which are enabled through the utilization of multiple low ASIL perception units and one high ASIL monitor unit. In addition to the framework concept, we specify how each component can be mapped to appropriate hardware and software platforms. The practical feasibility of the perception framework is demonstrated with a proof of concept prototype.

Development Environment Proposal for Automotive VisionBased Algorithms Next generation autonomous driving functions heavily depend on various computationally intensive algorithms based on machine vision. Design and development of such algorithms is challenging, requiring engagement of a wider community and the availability of appropriate, costeffective tooling. Setting up a hardware and software environment that would both reflect actual automotive requirements and facilitate development and prototyping, is an uneasy task. In this paper, we analyze the available development platforms which could be utilized in automotive machine vision, with specific regard to new algorithm requirements, such as the utilization of AI, hardware acceleration and low latency. We give a proposal of an affordable and scalable development setup consisting of heterogeneous, visionenabled SoCs and automotive interfaces.