The number of ADAS systems in the automotive industry using imaging technology is steadily increasing year over year, both for external viewing as well as for in-cabin purposes, to help make vehicles safer for drivers, passengers and pedestrians. Most current commercially available systems are based on CIS technology, but next generation systems are increasingly looking to adapt some level of depth sensing technologies. In this paper we will present which image sensor characteristics are important in order to provide robust and reliable ADAS sensing systems and in-cabin monitoring systems, both from traditional CIS solutions, as well as new ToF and LIDAR depth sensing technologies. We will present analysis based on common available sensor parameters to determine the system level performance, and what it means for object detection probability as well as distance uncertainty for depth sensing solutions. The analysis will include some specific common real world traffic scenarios as well as NCAP scenarios.