  Computer Vision, either alone or combined with other technologies such as
radar or Lidar, is one of the key technologies used in Advanced Driver
Assistance Systems (ADAS). Its role understanding and analysing the driving
scene is of great importance as it can be noted by the number of ADAS
applications that use this technology. However, porting a vision algorithm to
an embedded automotive system is still very challenging, as there must be a
trade-off between several design requisites. Furthermore, there is not a
standard implementation platform, so different alternatives have been proposed
by both the scientific community and the industry. This paper aims to review
the requisites and the different embedded implementation platforms that can be
used for Computer Vision-based ADAS, with a critical analysis and an outlook to
future trends.
