UC San Diego

Self-driving vehicle technology has become a popular topic for discussion and debate in the modern day. Although LiDAR is one of the primary sensors being used by most of the groups working towards autonomous driving, it is very costly, prone to mechanical failure, and its localization capability is not scalable due to its dependency on high-definition maps. Also, the perception related benefits of a LiDAR can be achieved by using a sensor fusion of cameras and RADAR. Considering the drawbacks of the LiDARs, the availability of an alternate solution, and the recent progress of computer vision techniques in the last few years, we are proposing an architecture for vision-based autonomous driving. In this thesis, we outline building blocks for the development of this vision-based architecture, describe the functionality of these blocks, and provide a brief overview of existing studies and research to implement these blocks, and thereby achieve a vision-based autonomous system. Furthermore, we discuss the design and implementation of a few of these blocks in the purview of the activities being undertaken at Autonomous Living Laboratory (AVL) at UC San Diego.