Faulttolerant environmental perception architecture for robust automated driving Autonomous vehicles gain more and more attention. Moving towards highly automated vehicles requires the implementation of faulttolerant systems. In this paper we propose an architecture for a faulttolerant environmental perception, where either one fault in the hardware or one in the software can be detected. The hardware fault detection relies on a Landmark (LM) tracking approach. The software fault detection is based on comparing the outputs of redundant programs. The faulty module is then excluded in the data fusion algorithm by a fault masking. The functionality of the proposed approach is tested in simulation via injecting one hardware and one software fault.

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.