Embedded Platforms for Computer Vision-based Advanced Driver Assistance Systems: a Survey
It addresses the problem of selecting and optimizing embedded platforms for ADAS developers, but it is incremental as it synthesizes existing research without new experimental results.
This survey reviews the design requirements and embedded platform options for implementing computer vision in Advanced Driver Assistance Systems (ADAS), highlighting the challenges in balancing trade-offs and the lack of standardization.
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.