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.

Video delivery subsystem for multiSoC automotive machine vision platforms Automotive microcontrollers in a form of SystemonChip (SoC) are getting more powerful due to the abundance of algorithms catering to the Advanced DriverAssistance System (ADAS) applications. Apart from traditional LIDAR and RADARbased approaches, many new algorithms heavily depend on cameras and video. Delivering video within a heterogeneous hardware environment such as within a single SoC or across SoCs is a daunting task. In this paper, we give a proposal of a video delivery subsystem to be applied to next generation hardware and software architectures for autonomous vehicles. Proposed subsystem considers all major video exchange routes, including DMA, PCIE and Ethernet, with appropriate software interfaces which can be integrated to future horizontal automotive middleware. Within an early evaluation we demonstrate the utilization of the subsystem in an automotive hardware encompassing three TI TDA2X and two NVIDIA Xavier SoCs.