Snap-DAS: A vision-based driver assistance system on a SnapdragonTM embedded platform

In the recent years, mobile computing platforms are becoming increasingly cheaper and yet more powerful in terms of computational resources. Automobiles provide a suitable environment to deploy such mobile platforms in order to provide low cost driver assistance systems. In this paper, we propose Snap-DAS which is a vision-based driver assistance system that is implemented on a Snapdragon^TM embedded platform. A forward facing camera combined with the Snapdragon^TM platform constitute Snap-DAS. The compute efficient implementation of the LASeR lane estimation algorithm in [1] is exploited to implement a set of lane related functions on Snap-DAS, which include lane drift warning and lane change event detection. A detailed evaluation is performed on live data and Snap-DAS is also field tested on freeways. Furthermore, we explore the possibility of using Snap-DAS for analyzing drives for online naturalistic driving studies.