Embedded Platforms for Computer Vision-based Advanced Driver Assistance Systems: a Survey

TL;DR

This survey reviews embedded platforms for computer vision in ADAS, analyzing design challenges, implementation options, and future trends to support advanced driver assistance systems effectively.

Contribution

It provides a comprehensive overview of embedded platforms for computer vision in ADAS, highlighting current challenges and future directions in the field.

Findings

Various embedded platforms are used for ADAS computer vision applications.

Trade-offs exist between performance, power, and cost in platform selection.

Future trends include increased use of AI accelerators and standardization efforts.

Abstract

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.