Efficient Implementation of Camera Mirror System Algorithm on Heterogeneous Chip Architectures Advanced Driving Assistance Systems (ADAS) are vehiclexe2x80x93based intelligent safety systems which help the drivers to improve safety driving. One important building block of ADAS is a Camera Mirror System (CMS). CMS provides means to enhance rearview mirrors by digital information, such as detected obstacles, collision avoidance alerts and more. Efficient implementation of CMS requires the utilization of dedicated processing in hardware, to ensure minimal latency and coexistence with other algorithms in heterogeneous hardware environments. In this paper we analyze CMS algorithm data pipeline and provide insights on how each phase can be efficiently accelerated using dedicated hardware blocks on presentday highperformance microcontrollers. We give early evaluation of the presented approach on top of two architectures: TI TDA2x and NVIDIA Xavier.

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.