Programming methodologies for ADAS applications in parallel heterogeneous architectures

Computer Vision (CV) is crucial for understanding and analyzing the driving scene to build more intelligent Advanced Driver Assistance Systems (ADAS). However, implementing CV-based ADAS in a real automotive environment is not straightforward. Indeed, CV algorithms combine the challenges of high computing performance and algorithm accuracy. To respond to these requirements, new heterogeneous circuits are developed. They consist of several processing units with different parallel computing technologies as GPU, dedicated accelerators, etc. To better exploit the performances of such architectures, different languages are required depending on the underlying parallel execution model. In this work, we investigate various parallel programming methodologies based on a complex case study of stereo vision. We introduce the relevant features and limitations of each approach. We evaluate the employed programming tools mainly in terms of computation performances and programming productivity. The feedback of this research is crucial for the development of future CV algorithms in adequacy with parallel architectures with a best compromise between computing performance, algorithm accuracy and programming efforts.